diff --git "a/app.py" "b/app.py" --- "a/app.py" +++ "b/app.py" @@ -29,22 +29,20 @@ from pydantic import BaseModel, Field import litellm from langchain.tools import Tool - - # Configure logging logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) # Set page configuration st.set_page_config( - page_title="Case Study Analysis Suite", + page_title="Document Analysis Suite", page_icon="📚", layout="wide" ) # Initialize session state variables if 'active_tab' not in st.session_state: - st.session_state.active_tab = "Case Breakdown" + st.session_state.active_tab = "Document Analysis" if 'uploaded_files' not in st.session_state: st.session_state.uploaded_files = None if 'extracted_text' not in st.session_state: @@ -57,22 +55,31 @@ if 'teaching_plan_generated' not in st.session_state: st.session_state.teaching_plan_generated = False if 'board_plan_generated' not in st.session_state: st.session_state.board_plan_generated = False +if 'content_type' not in st.session_state: + st.session_state.content_type = {"type": "Unknown", "confidence": "Low", "indicators": []} # Load environment variables #load_dotenv() - - +# Set the app title based on content type +def get_app_title(content_type): + if content_type == "Case Study": + return "📚 Case Study Analysis Suite" + elif content_type == "Scientific Article": + return "đŸ”Ŧ Scientific Article Analysis Suite" + elif content_type == "News Item": + return "📰 News Item Analysis Suite" + else: + return "📄 Document Analysis Suite" # Page title and description -st.title("📚 Case Study Analysis Suite") -st.subheader("Generate comprehensive Teaching Notes, Teaching Plans, and Board Plans") +st.title(get_app_title(st.session_state.content_type["type"])) +st.subheader("Generate comprehensive Teaching Notes, Teaching Plans, and Discussion Frameworks") st.write("Developed for BIA 568 (Business Intelligence and Analytics) -- Management of A.I. at Stevens Institute of Technology") st.write("---") # Sidebar for API key configuration -# In the sidebar section: with st.sidebar: st.title("⚙ī¸ Configuration") @@ -91,8 +98,6 @@ with st.sidebar: "provider": "gemini" } - - st.divider() # Reset button @@ -148,8 +153,143 @@ def create_download_link(content, filename): href = f'Download {filename}' return href +def create_docx_from_markdown(markdown_content, title, document_type): + """Convert markdown content to a formatted DOCX file""" + # Create new document + doc = Document() + + # Add title + title_paragraph = doc.add_paragraph() + title_paragraph.alignment = WD_ALIGN_PARAGRAPH.CENTER + title_run = title_paragraph.add_run(title) + title_run.font.size = Pt(16) + title_run.bold = True + + # Add document type + type_paragraph = doc.add_paragraph() + type_paragraph.alignment = WD_ALIGN_PARAGRAPH.CENTER + type_run = type_paragraph.add_run(f"Document Type: {document_type}") + type_run.font.size = Pt(12) + type_run.italic = True + + # Add separator + doc.add_paragraph("_" * 50) + + # Process markdown content + lines = markdown_content.split('\n') + current_section = None + + for line in lines: + # Handle headers + if line.startswith('# '): + p = doc.add_heading(line[2:], level=1) + current_section = line[2:] + elif line.startswith('## '): + p = doc.add_heading(line[3:], level=2) + current_section = line[3:] + elif line.startswith('### '): + p = doc.add_heading(line[4:], level=3) + # Handle bullet points + elif line.startswith('* '): + p = doc.add_paragraph() + p.style = 'List Bullet' + p.add_run(line[2:]) + # Handle numbered lists + elif re.match(r'^\d+\. ', line): + p = doc.add_paragraph() + p.style = 'List Number' + p.add_run(re.sub(r'^\d+\. ', '', line)) + # Regular paragraph + elif line.strip(): + p = doc.add_paragraph(line) + + # Save to a temporary file + with tempfile.NamedTemporaryFile(delete=False, suffix='.docx') as tmp_file: + doc.save(tmp_file.name) + return tmp_file.name + +#---------------------------- Content Type Detector ----------------------------# + +class ContentTypeDetector: + def __init__(self, api_key): + self.api_key = api_key + self.llm = LLM( + model='gemini/gemini-2.0-flash', + api_key=self.api_key, + provider="gemini" + ) + + def create_detector_agent(self): + return Agent( + role="Content Type Analyzer", + goal="Determine the type of document from its content", + backstory="""You are an expert in content analysis, capable of + identifying different types of documents based on their structure, + language, and content. You can distinguish between case studies, + scientific articles, news items, and other content types with high accuracy.""", + llm=self.llm, + verbose=True + ) + + def detect_content_type(self, text): + """Determine the type of content from the text""" + detection_task = Task( + description=f""" + Analyze the following text and determine whether it is: + 1. A case study + 2. A scientific article + 3. A news item + 4. Other (specify) + + Identify key indicators that support your classification, such as: + - Structure (abstract, methods, results for scientific articles) + - Language patterns (narrative style for case studies, journalistic style for news) + - Content elements (company information, research data, current events) + + Return the content type and confidence level in the following format: + + Content Type: [type] + Confidence: [high/medium/low] + Indicators: [list key indicators] + + Text to analyze: + {text[:3000]} + """, + expected_output="Content type classification with indicators", + agent=self.create_detector_agent() + ) + + crew = Crew( + agents=[self.create_detector_agent()], + tasks=[detection_task], + process=Process.sequential, + verbose=False + ) + result = crew.kickoff() + + # Parse the result to extract content type + content_type = "Case Study" # Default + confidence = "Medium" + indicators = [] + + for line in result.raw.split('\n'): + if line.startswith('Content Type:'): + content_type = line.replace('Content Type:', '').strip() + elif line.startswith('Confidence:'): + confidence = line.replace('Confidence:', '').strip() + elif line.startswith('Indicators:'): + # Get all indicators (might be on multiple lines) + indicator_text = result.raw.split('Indicators:')[1].strip() + indicators = [ind.strip() for ind in indicator_text.split('-') if ind.strip()] + + return { + "type": content_type, + "confidence": confidence, + "indicators": indicators + } + def process_files(uploaded_files): - """Process uploaded files and extract text""" + """Process uploaded files and extract text with content type detection""" combined_text = "" first_file_text = "" @@ -180,9 +320,15 @@ def process_files(uploaded_files): tmp_file.write(file.getvalue()) temp_file_paths.append(tmp_file.name) - return combined_text, first_file_text, temp_file_paths + # Detect content type if API key is available + content_type = {"type": "Unknown", "confidence": "Low", "indicators": []} + if os.environ.get("GEMINI_API_KEY") and first_file_text: + detector = ContentTypeDetector(os.environ.get("GEMINI_API_KEY")) + content_type = detector.detect_content_type(first_file_text) + + return combined_text, first_file_text, temp_file_paths, content_type -#---------------------------- Document Generator (Case Breakdown) ----------------------------# +#---------------------------- Document Generator (Document Analysis) ----------------------------# class DocumentGenerator: def __init__(self): @@ -341,7 +487,7 @@ class DocumentGenerator: p.paragraph_format.alignment = WD_ALIGN_PARAGRAPH.JUSTIFY self.add_formatted_text(p, line) - def create_word_document(self, title, author, sections_content): + def create_word_document(self, title, author, sections_content, document_type): """Create and return a formatted Word document""" doc = Document() self.setup_document_styles(doc) @@ -349,11 +495,21 @@ class DocumentGenerator: # Title section title_paragraph = doc.add_paragraph() title_paragraph.alignment = WD_ALIGN_PARAGRAPH.CENTER - title_run = title_paragraph.add_run("CASE BREAKDOWN") + + # Set title based on document type + if document_type == "Case Study": + title_run = title_paragraph.add_run("CASE BREAKDOWN") + elif document_type == "Scientific Article": + title_run = title_paragraph.add_run("ARTICLE ANALYSIS") + elif document_type == "News Item": + title_run = title_paragraph.add_run("NEWS ANALYSIS") + else: + title_run = title_paragraph.add_run("DOCUMENT ANALYSIS") + title_run.font.size = Pt(16) title_run.bold = True - # Case study name and author + # Document name and author case_name_paragraph = doc.add_paragraph() case_name_paragraph.alignment = WD_ALIGN_PARAGRAPH.CENTER case_name_run = case_name_paragraph.add_run(title) @@ -390,17 +546,245 @@ def create_element(name): def create_attribute(element, name, value): element.set(qn(name), value) -#---------------------------- Case Breakdown Generator ----------------------------# +#---------------------------- Document Analysis Generator ----------------------------# -class CaseMetadata(BaseModel): - title: str = Field(description="The title of the case study") - author: str = Field(description="The author(s) of the case study") +class DocumentMetadata(BaseModel): + title: str = Field(description="The title of the document") + author: str = Field(description="The author(s) of the document") class SectionContent(BaseModel): content: str = Field(description="Generated content for the section") review: str = Field(description="Review of the content with score and feedback") -class CaseBreakdownCrew: +# Define section templates based on content type +def get_sections_by_content_type(content_type): + if content_type == "Case Study": + return { + "Case Synopsis": """Summarize the case in 3–4 paragraphs of a total 300 words. Explain the company's background, the industry it operates in, + and the key challenge or strategic decision it faces. Discuss any turning points or dilemmas. Why is this case relevant for + business students? + Position in Course: In 1–2 sentences, describe which type of course this case is best suited for + (e.g., Operations Strategy, AI in Business, Global Supply Chain). What key topics does it help students understand""", + + "Learning Objectives": """List 8–10 learning objectives for this case. A total of 300 words. What should students understand after analyzing this case? Focus on + leadership decisions, operational insights, AI adoption, or ethical concerns""", + + "Teaching Strategies": + """ + Describe 8–10 ways an instructor can effectively teach this case. Should they use role-playing? A total of 350 words. + A structured debate? Analyzing real-world examples? How can students actively engage with the material? 1st point should be Approach and second point should be Objective + """, + + "Suggested Teaching Plan": + """ + Outline a structured teaching plan for this case. What should be covered first? A total of 350 words. + When should student activities be introduced? How should the case discussion conclude? + """, + + "Key Points and Insights": + """ + List 10 key insights students should take from this case. What makes this case unique? A total of 300 words. + What are the most important strategic, operational, or ethical considerations? + """, + + "Further Insights": + """ + Provide additional insights that go beyond the case. How does this case relate to + larger business trends? What external factors (e.g., regulation, innovation, geopolitical forces) could impact the situation? A total of 400 words. + """, + + "Discussion Questions & Answers": + """ + Create 8–10 discussion questions about the case. What are the key strategic dilemmas? + Where do trade-offs exist? How can students critically analyze the company's decisions? Provide concise answers. A total of 450 words. + """, + + "Assignment Exercises": + """ + List 8–10 in-depth assignments that encourage strategic thinking, data analysis, + or real-world application. Include a mix of strategy proposals, financial analysis, + role-playing exercises, and ethical debates. Define the expected format + (e.g., business report, presentation, comparative analysis) and objective of each exercise. A total of 550- 600 words. + """, + + "Automated Conversation": + """ + Write an AI-generated conversation between three personas relevant to the case. A total of 650- 700 words. + One should be an executive making a strategic decision, another should be an expert, + and the third should be skeptical or resistant. The conversation should explore the challenges, risks, and strategic importance of the case. + """, + + "Case Suggestions": + """ + List 10 ways to make this case more interactive. Should students role-play as executives? + Simulate a crisis response? Conduct a competitive market analysis? + Suggest unique, hands-on ways to engage with the case. A total of 550 words. + """ + } + + elif content_type == "Scientific Article": + return { + "Article Summary": """Summarize the scientific article in 3-4 paragraphs totaling 300 words. Explain the research question, + methodology, key findings, and significance. Discuss the implications for the field. + Position in Course: In 1-2 sentences, describe which type of course this article is + best suited for and what key topics it helps students understand.""", + + "Research Methodology Analysis": """Analyze the research methodology in 300 words. What approaches were used? + What were the strengths and limitations? How could the methodology be improved? + Focus on sample selection, data collection methods, analytical techniques, and validation.""", + + "Key Findings and Implications": """Summarize the 8-10 most important findings and their implications in 350 words. + What new knowledge does this research contribute? How does it confirm or challenge + existing theories? What are the practical applications of these findings?""", + + "Learning Objectives": """List 8-10 learning objectives for this scientific article in 300 words. + What should students understand after analyzing this article? Focus on research methods, + data analysis, scientific reasoning, or ethical implications.""", + + "Teaching Strategies": """Describe 8-10 ways an instructor can effectively teach this article in 350 words. + Should they use research replication? Critical analysis? Applying findings to case studies? + How can students actively engage with the research methodology and results?""", + + "Classroom Activities": """Outline 8-10 specific classroom activities in 400 words that help students + engage with the research, methodology, and findings. Include individual and group + activities that develop critical thinking and analytical skills.""", + + "Discussion Questions & Answers": """Create 8-10 discussion questions about the article in 450 words. + What are the key methodological considerations? How do the findings relate to existing + research? What future research might build on these findings? Provide concise answers.""", + + "Simulated Research Discussion": + """ + Write a simulated research discussion between a principal investigator and 3-4 lab members/colleagues analyzing + this scientific article. A total of 500-600 words. Include methodological critiques, alternative interpretations of data, + suggestions for follow-up experiments, and connections to related research areas. The discussion should demonstrate + scientific thinking, healthy skepticism, and collaborative problem-solving. Include moments of disagreement, + clarification, and breakthrough insights that advance understanding of the research. You can also create persons and their roles based on the article. + """, + + "Further Research Directions": """Suggest 8-10 ways the research could be extended or applied in 400 words. + What questions remain unanswered? What methodology improvements could be made? + How could the findings be tested in different contexts?""", + + "Interdisciplinary Connections": """Explore how this research connects to 8-10 other disciplines or fields in 450 words. + How might these findings impact or be applied in other domains? + What cross-disciplinary research opportunities exist?""", + + "Critical Evaluation": """Provide a critical evaluation of the article in 500 words. + Assess the strength of the evidence, validity of conclusions, and overall contribution + to the field. What are the article's strengths and limitations?""" + } + + elif content_type == "News Item": + return { + "News Summary": """Summarize the news item in 3-4 paragraphs totaling 300 words. + Explain the key events, stakeholders involved, and broader context. + Discuss the significance and implications. In 1-2 sentences, describe why + this news item is relevant for students and what course topics it relates to.""", + + "Contextual Analysis": """Provide historical and current context for this news item in 350 words. + What events, trends, or policies led to this situation? How does this + news fit into broader patterns or developments in this field?""", + + "Key Stakeholders Analysis": """Identify and analyze 8-10 key stakeholders in 400 words. + For each stakeholder, explain their interests, influence, positions, + and how they're affected by the events described in the news item.""", + + "Impact Assessment": """Assess the immediate and potential long-term impacts in 350 words. + What are the economic, social, political, and environmental implications? + How might different industries, communities, or policies be affected?""", + + "Learning Objectives": """List 8-10 learning objectives in 300 words. What should students + understand after analyzing this news item? Focus on analytical skills, + media literacy, critical thinking, or connecting theory to current events.""", + + "Discussion Framework": """Create a structured framework for discussing this news item in 400 words. + How should the discussion be organized? What key questions should guide + the conversation? How can instructors ensure balanced perspectives?""", + + "Discussion Questions & Answers": """Create 8-10 discussion questions in 450 words. + What critical thinking questions will help students analyze this news? + How can students evaluate different perspectives? Provide concise answers.""", + + "Simulated Panel Discussion": + """ + Write a simulated panel discussion between a moderator and 3-4 experts analyzing this news item. + A total of 700-800 words. Include diverse professional and political perspectives on the events, causes, + implications, and media coverage. The panelists should represent different stakeholders or viewpoints, + with the moderator guiding the conversation through key aspects of the news. Include moments of civil + disagreement, fact-checking, contextualizing of information, and insights into broader trends related to the news item. + You can also create persons and their roles based on the article. + """, + + + "Classroom Activities": """Suggest 8-10 classroom activities in 500 words related to this news item. + Include debates, simulations, research projects, media analysis exercises, + and other activities that promote deep engagement with the content.""", + + "Media Analysis Component": """Analyze how this news has been covered by different sources in 400 words. + Compare coverage across 3-4 different media outlets. Identify potential + biases, framing choices, and what might be emphasized or omitted.""", + + "Related Resources": """Identify 8-10 related resources in 350 words for further exploration. + Include academic articles, books, documentaries, podcasts, and other + news sources that provide deeper context or alternative perspectives.""" + } + else: + return { + "Content Summary": """Summarize the document in 3-4 paragraphs totaling 300 words. + Explain the key themes, information, and purpose of the content. + Discuss why this content is valuable for teaching and learning.""", + + "Key Points and Themes": """Identify 8-10 key points and themes in 350 words. + What are the most important ideas, concepts, or arguments presented? + What makes this content valuable to study?""", + + "Learning Objectives": """List 8-10 learning objectives in 300 words. What should students + understand after studying this content? What skills might they develop? + How does this content connect to broader educational goals?""", + + "Teaching Approaches": """Suggest 8-10 teaching approaches in 400 words. + How can this content be effectively taught? What instructional + methods would be most appropriate for this material?""", + + "Discussion Questions": """Create 8-10 discussion questions in 450 words. + What questions will stimulate critical thinking and deep engagement + with the content? Provide brief answers or guidance for each.""", + + "Learning Activities": """Propose 8-10 learning activities in 500 words. + What individual and group activities would help students + engage with and apply the content? Include a mix of analytical, + creative, and practical exercises.""", + + "Further Exploration": """Suggest resources and directions for further exploration in 350 words. + What related topics might students investigate? What additional + readings or materials would complement this content?""", + + "Simulated Dialogue": + """ + Write a simulated dialogue between an educator and 3-4 participants analyzing this content. + A total of 700-800 words. Include different interpretations, connections to prior knowledge, + practical applications, and critical questions about the material. The dialogue should demonstrate + how different perspectives can enrich understanding and how thoughtful questioning can reveal deeper + insights about the content. Include moments of clarification, realization, and connection-making. + You can also create persons and their roles based on the article. + """, + + "Relevance and Applications": """Discuss the relevance and practical applications in 400 words. + How does this content apply to real-world situations? + Why is it important for students to engage with this material?""", + + "Critical Analysis": """Provide a critical analysis of the content in 450 words. + What perspectives are represented or missing? What assumptions + underlie the content? What strengths and limitations does it have?""", + + "Assessment Strategies": """Suggest 8-10 assessment strategies in 400 words. + How might instructors evaluate student understanding and + application of this content? Include formal and informal + assessment approaches.""" + } + +class DocumentAnalysisCrew: def __init__(self, api_key): self.api_key = api_key self.llm = LLM( @@ -410,30 +794,31 @@ class CaseBreakdownCrew: ) def create_metadata_agent(self): - return Agent( - role="Metadata Analyzer", - goal="Extract title and author information from document content", - backstory="""You specialize in analyzing document content to identify key metadata - such as titles, authors, and other publication information. You have a keen eye for - identifying the most important and relevant document metadata, even when it's not - explicitly labeled.""", - llm=LLM( - model='gemini/gemini-2.0-flash', - api_key=self.api_key, - provider="gemini" - ), - verbose=True - ) - + return Agent( + role="Metadata Analyzer", + goal="Extract title and author information from document content", + backstory="""You specialize in analyzing document content to identify key metadata + such as titles, authors, and other publication information. You have a keen eye for + identifying the most important and relevant document metadata, even when it's not + explicitly labeled.""", + llm=LLM( + model='gemini/gemini-2.0-flash', + api_key=self.api_key, + provider="gemini" + ), + verbose=True + ) + def create_content_generator_agent(self): return Agent( - role="Case Study Content Generator", - goal="Generate comprehensive case analysis content based on section requirements", - backstory="""You are an expert business analyst specializing in case study analysis. - You excel at breaking down complex business cases into structured, insightful content - that highlights key learning points, strategies, and insights. You have extensive experience - in business education and know how to create content that is valuable for teaching and learning.""", + role="Document Content Generator", + goal="Generate comprehensive analysis content based on section requirements", + backstory="""You are an expert analyst specializing in creating educational content from various + document types including case studies, scientific articles, and news items. You excel at breaking + down complex content into structured, insightful analysis that highlights key learning points, research + findings, or current events context. You have extensive experience in education and know how to create + content that is valuable for teaching and learning.""", llm=self.llm, verbose=True ) @@ -444,9 +829,10 @@ class CaseBreakdownCrew: role="Content Quality Reviewer", goal="Evaluate and score content for quality, relevance, and depth", backstory="""You are a seasoned academic reviewer with years of experience evaluating - business case studies and educational content. You have a strong understanding of what makes - effective case study material and can provide constructive feedback to improve content quality. - You carefully analyze content for relevance, clarity, depth, and educational value.""", + educational content across various formats. You have a strong understanding of what makes + effective teaching material for different document types and can provide constructive feedback + to improve content quality. You carefully analyze content for relevance, clarity, depth, and + educational value.""", llm=self.llm, verbose=True ) @@ -454,10 +840,10 @@ class CaseBreakdownCrew: def create_metadata_task(self, text): return Task( description=f""" - Analyze the extracted text and identify the case study title and author(s). + Analyze the extracted text and identify the document title and author(s). Look for information typically found at the beginning of a document, such as: - 1. The case study or article title + 1. The document title (article title, case study name, news headline) 2. The author name(s) Return the identified title and author in the following format: @@ -465,7 +851,7 @@ class CaseBreakdownCrew: Title: [the title] Author: [the author(s)] - If you cannot find this information with certainty, use "Untitled Case Study" for the title + If you cannot find this information with certainty, use "Untitled Document" for the title and "Unknown Author" for the author. Text to analyze: @@ -475,10 +861,13 @@ class CaseBreakdownCrew: agent=self.create_metadata_agent() ) - def create_section_task(self, section_name, prompt, text): + def create_section_task(self, section_name, prompt, text, content_type): + # Adjust prompt based on content type and section + # Content type adjustments are handled at the section template level now + return Task( description=f""" - Generate content for the '{section_name}' section of the case breakdown. + Generate content for the '{section_name}' section of the document analysis. {prompt} @@ -489,6 +878,8 @@ class CaseBreakdownCrew: 4. Use line breaks between paragraphs 5. Keep paragraphs focused and concise + Remember that this is for a {content_type} document, so tailor your analysis appropriately. + Ensure the content is structured, well-organized, and follows proper formatting. Text to analyze: @@ -498,10 +889,10 @@ class CaseBreakdownCrew: agent=self.create_content_generator_agent() ) - def create_review_task(self, section_name, content): + def create_review_task(self, section_name, content, content_type): return Task( description=f""" - Review the content for the '{section_name}' section. + Review the content for the '{section_name}' section for a {content_type} document. Score it from 1-10 based on: 1. Relevance to the section (0-3) @@ -530,13 +921,13 @@ class CaseBreakdownCrew: ) result = crew.kickoff() - title = "Untitled Case Study" + title = "Untitled Document" author = "Unknown Author" for line in result.raw.split('\n'): if line.startswith('Title:'): extracted_title = line.replace('Title:', '').strip() - if extracted_title and extracted_title != "[Untitled Case Study]": + if extracted_title and extracted_title != "[Untitled Document]": title = extracted_title elif line.startswith('Author:'): extracted_author = line.replace('Author:', '').strip() @@ -545,8 +936,8 @@ class CaseBreakdownCrew: return title, author - def generate_section_content(self, text, section_name, section_prompt): - section_task = self.create_section_task(section_name, section_prompt, text) + def generate_section_content(self, text, section_name, section_prompt, content_type): + section_task = self.create_section_task(section_name, section_prompt, text, content_type) crew = Crew( agents=[self.create_content_generator_agent()], tasks=[section_task], @@ -556,8 +947,8 @@ class CaseBreakdownCrew: result = crew.kickoff() return result.raw - def review_content(self, content, section_name): - review_task = self.create_review_task(section_name, content) + def review_content(self, content, section_name, content_type): + review_task = self.create_review_task(section_name, content, content_type) crew = Crew( agents=[self.create_content_reviewer_agent()], tasks=[review_task], @@ -622,7 +1013,7 @@ def extract_text(file_path: str) -> str: except Exception as e: return f"Error extracting text: {str(e)}" -def create_teaching_plan_crew(file_paths): +def create_teaching_plan_crew(file_paths, content_type): # Initialize LLM my_llm = LLM( model='gemini/gemini-2.0-flash', @@ -630,24 +1021,72 @@ def create_teaching_plan_crew(file_paths): provider="gemini" ) + # Create tracker instance + tracker = AgentTracker() + + # Adjust agent descriptions and goals based on content type + if content_type == "Case Study": + analyzer_role = 'Case Study Analyzer' + analyzer_goal = 'Extract key concepts, objectives, and data from case study files' + analyzer_backstory = 'You are an expert in analyzing business case studies. Identify core themes, data points, and learning objectives.' + + designer_role = 'Teaching Plan Designer' + designer_goal = 'Create a 2-hour lesson plan based on analyzed case study data' + designer_backstory = 'You are an educator designing a structured lesson plan. Use the extracted case study data to outline activities, discussions, and assessments.' + + task_description = "Design a 2-hour lesson plan with introduction, case analysis, group activity, and assessment." + + elif content_type == "Scientific Article": + analyzer_role = 'Scientific Article Analyzer' + analyzer_goal = 'Extract key research findings, methodology, and implications from scientific article' + analyzer_backstory = 'You are an expert in analyzing scientific research articles. Identify research questions, methodology, findings, and significance.' + + designer_role = 'Scientific Article Teaching Designer' + designer_goal = 'Create a 2-hour research-focused lesson plan based on analyzed article data' + designer_backstory = 'You are an educator designing a structured lesson plan for scientific content. Use the extracted research data to outline discussions, critiques, and applications.' + + task_description = "Design a 2-hour lesson plan with research overview, methodology analysis, findings discussion, and research application activities." + + elif content_type == "News Item": + analyzer_role = 'News Content Analyzer' + analyzer_goal = 'Extract key events, stakeholders, and context from news item' + analyzer_backstory = 'You are an expert in analyzing current events and news content. Identify key events, stakeholders, and broader context.' + + designer_role = 'Current Events Teaching Designer' + designer_goal = 'Create a 2-hour current events lesson plan based on analyzed news data' + designer_backstory = 'You are an educator designing a structured lesson plan for current events. Use the extracted news content to outline discussions, contextual analysis, and media literacy activities.' + + task_description = "Design a 2-hour lesson plan with news overview, context discussion, stakeholder analysis, and media literacy activities." + + else: + analyzer_role = 'Content Analyzer' + analyzer_goal = 'Extract key concepts, themes, and insights from document content' + analyzer_backstory = 'You are an expert in analyzing various types of documents. Identify key themes, concepts, and learning points.' + + designer_role = 'Learning Plan Designer' + designer_goal = 'Create a 2-hour learning plan based on analyzed document content' + designer_backstory = 'You are an educator designing a structured learning plan. Use the extracted content to outline discussions, activities, and assessments.' + + task_description = "Design a 2-hour lesson plan with content introduction, key concept analysis, interactive activities, and assessment." + # Create agents with Gemini configuration pdf_analyzer = Agent( - role='Case Study Analyzer', - goal='Extract key concepts, objectives, and data from case study files', - backstory='You are an expert in analyzing business case studies. Identify core themes, data points, and learning objectives.', + role=analyzer_role, + goal=analyzer_goal, + backstory=analyzer_backstory, llm=my_llm, tools=[extract_text], verbose=True, - step_callback=lambda *args, **kwargs: tracker.update_agent("Case Study Analyzer") + step_callback=lambda *args, **kwargs: tracker.update_agent(analyzer_role) ) plan_generator = Agent( - role='Teaching Plan Designer', - goal='Create a 2-hour lesson plan based on analyzed case study data', - backstory='You are an educator designing a structured lesson plan. Use the extracted case study data to outline activities, discussions, and assessments.', + role=designer_role, + goal=designer_goal, + backstory=designer_backstory, llm=my_llm, verbose=True, - step_callback=lambda *args, **kwargs: tracker.update_agent("Teaching Plan Designer") + step_callback=lambda *args, **kwargs: tracker.update_agent(designer_role) ) reviewer = Agent( @@ -662,9 +1101,10 @@ def create_teaching_plan_crew(file_paths): final_reporter = Agent( role='Teaching Plan Reporter', goal='Ensure to incorporate the feedback from the reviewer agent and finalize the content for the teaching plan', - backstory=""" You are a Harvard Business School Teaching Plan Reporter with 20 years of Experience in teaching and curriculum development. - You are responsible for finalizing the content for the teaching plan and ensuring it is engaging and aligned with learning objectives. - You will use the feedback from the reviewer agent to make necessary revisions and finalize the content for the teaching plan.""", + backstory=f""" You are an expert educator with 20 years of experience in teaching and curriculum development + specializing in {content_type}s. You are responsible for finalizing the content for the teaching plan + and ensuring it is engaging and aligned with learning objectives. You will use the feedback from the + reviewer agent to make necessary revisions and finalize the content for the teaching plan.""", llm=my_llm, verbose=True, step_callback=lambda *args, **kwargs: tracker.update_agent("Teaching Plan Reporter") @@ -675,33 +1115,34 @@ def create_teaching_plan_crew(file_paths): # Define tasks analyze_pdf = Task( - description=f"Extract and analyze the files at {', '.join(file_paths)} for key concepts and learning objectives.", + description=f"Extract and analyze the files at {', '.join(file_paths)} for key concepts and learning objectives, keeping in mind this is a {content_type}.", config={"file_path": combined_file_path}, - expected_output="A summary of key concepts and learning objectives extracted from the files.", + expected_output=f"A summary of key concepts and learning objectives extracted from the {content_type}.", agent=pdf_analyzer ) generate_plan = Task( - description="Design a 2-hour lesson plan with introduction, analysis, group activity, and assessment.", - expected_output="A detailed 2-hour lesson plan with clear sections and activities.", + description=task_description, + expected_output=f"A detailed 2-hour lesson plan for a {content_type} with clear sections and activities.", agent=plan_generator ) review_plan = Task( - description="Review the lesson plan for clarity, alignment with objectives, and student engagement.", + description=f"Review the lesson plan for clarity, alignment with objectives, and student engagement, considering this is a {content_type}.", expected_output="Feedback on the lesson plan's clarity, alignment, and engagement level.", agent=reviewer ) final_plan = Task( - description="Generate the final lesson plan based on the feedback.", + description=f"""Generate the final lesson plan based on the feedback, optimized for teaching a {content_type}.""", expected_output=""" You are also responsible for ensuring the plan is clear and concise. - It should have the overall objective to start with. - It should have a clear introduction - It should have detailed lesson breakdowns with the time to be spent on each section and the title of the section Highlighting all the important concepts to be taught (you can use tables to highlight the concepts in a structured way) - It should have one powerful visual aid which can be a table That can help in better understanding for the students - - It can have simple assessmentsthat enables class participation engagement in brainstorming activities and such that can help in better understanding of the concepts. + - It should include a simulated 10-15 minute class discussion segment showing how a skilled educator might guide students through a key concept in the document + - It can have simple assessments that enables class participation engagement in brainstorming activities and such that can help in better understanding of the concepts. - It should have a clear conclusion that ties back to the overall objective. - Overall Plan should be around 1300 - 1500 words.""", agent=final_reporter @@ -715,15 +1156,15 @@ def create_teaching_plan_crew(file_paths): verbose=True ) - return crew + return crew, tracker -#---------------------------- Board Plan Generator ----------------------------# +#---------------------------- Discussion Framework Generator ----------------------------# -class BoardPlanAnalyzer: - def __init__(self): - api_key = os.environ.get('GEMINI_API_KEY') +class DiscussionFrameworkAnalyzer: + def __init__(self, api_key): + self.api_key = api_key if not api_key: - raise ValueError("Gemini API key not found") + raise ValueError("API key not found") self.llm = LLM( model='gemini/gemini-2.0-flash', @@ -739,17 +1180,17 @@ class BoardPlanAnalyzer: def create_agents(self): """Create specialized agents for different tasks""" - # PDF Processing Agent - self.pdf_processor = Agent( - role='PDF Processor', - goal='Extract and clean text content from PDF case studies', - backstory="""You are an expert at processing PDF documents and extracting + # Document Processing Agent + self.document_processor = Agent( + role='Document Processor', + goal='Extract and clean text content from document files', + backstory="""You are an expert at processing various document types and extracting meaningful content. You ensure the text is properly formatted and ready for analysis.""", tools=[Tool( name="extract_text", - func=self.extract_text_from_pdf, - description="Extracts text content from PDF files" + func=self.extract_text_from_file, + description="Extracts text content from files" )], allow_delegation=False, llm=self.llm, @@ -758,74 +1199,142 @@ class BoardPlanAnalyzer: # Content Analysis Agent self.analyzer = Agent( - role='Case Study Analyzer', - goal='Analyze case studies and identify key points for board plan', - backstory="""You are an expert business analyst skilled at analyzing case - studies and identifying crucial elements. You create clear, structured - analyses that highlight key insights.""", + role='Document Analyzer', + goal='Analyze documents and identify key points for discussion framework', + backstory="""You are an expert analyst skilled at analyzing various types of documents + and identifying crucial elements for educational discussion. You create clear, structured + analyses that highlight key insights for teaching.""", tools=[Tool( name="analyze_content", - func=self.analyze_case_study, - description="Analyzes case study and creates structured board plan" + func=self.analyze_document, + description="Analyzes document and creates structured discussion framework" )], allow_delegation=False, llm=self.llm, verbose=True ) - def extract_text_from_pdf(self, pdf_content: bytes) -> str: - """Extract text from PDF using PyPDF2""" + def extract_text_from_file(self, file_content: bytes) -> str: + """Extract text from file using appropriate method""" try: - # Create PDF reader object - pdf_file = io.BytesIO(pdf_content) - pdf_reader = PyPDF2.PdfReader(pdf_file) - - # Extract text from all pages - text_content = [] - for page in pdf_reader.pages: - text_content.append(page.extract_text()) + # Create file reader object + file_obj = io.BytesIO(file_content) - # Combine all pages with proper spacing - return "\n\n".join(text_content) + # Determine file type + # This is simplified - in reality, you'd check the file signature/magic bytes + if b"%PDF" in file_content[:1024]: # Check for PDF signature + pdf_reader = PyPDF2.PdfReader(file_obj) + text_content = [] + for page in pdf_reader.pages: + text_content.append(page.extract_text()) + return "\n\n".join(text_content) + else: + # Try DOCX + try: + return docx2txt.process(file_obj) + except: + # Try PPTX + try: + file_obj.seek(0) + prs = Presentation(file_obj) + text_content = [] + for slide in prs.slides: + for shape in slide.shapes: + if hasattr(shape, "text"): + text_content.append(shape.text) + return "\n\n".join(text_content) + except: + return "Unable to extract text from unsupported file format" except Exception as e: - raise Exception(f"PDF extraction error: {str(e)}") - - def analyze_case_study(self, text: str) -> dict: - """Analyze case study content using litellm with JSON response""" + raise Exception(f"Extraction error: {str(e)}") + + def analyze_document(self, text: str, document_type: str) -> dict: + """Analyze document content using litellm with JSON response""" + # Adjust prompt based on document type + if document_type == "Case Study": + framework_type = "BOARD PLAN" + sections_prompt = """ + Create a detailed analysis with these exact sections: + 1. Main business challenge/opportunity + 2. Industry context and competitors + 3. Key decision points + 4. Stakeholder perspectives + 5. Implementation considerations + - Resource requirements + - Timeline + - Success metrics + 6. Alternative approaches + 7. Learning takeaways + """ + elif document_type == "Scientific Article": + framework_type = "DISCUSSION POINTS" + sections_prompt = """ + Create a detailed analysis with these exact sections: + 1. Main research question/hypothesis + 2. Methodology assessment + 3. Key findings summary + 4. Limitations and validity + 5. Theoretical implications + 6. Practical applications + 7. Future research directions + 8. Interdisciplinary connections + """ + elif document_type == "News Item": + framework_type = "CURRENT EVENTS FRAMEWORK" + sections_prompt = """ + Create a detailed analysis with these exact sections: + 1. Main event/development + 2. Historical and political context + 3. Key stakeholders and perspectives + 4. Media coverage analysis + 5. Immediate impacts + 6. Long-term implications + 7. Related developments + 8. Critical thinking questions + """ + else: + framework_type = "DISCUSSION FRAMEWORK" + sections_prompt = """ + Create a detailed analysis with these exact sections: + 1. Main topic/theme + 2. Key concepts + 3. Important relationships + 4. Supporting evidence + 5. Counterarguments/limitations + 6. Practical applications + 7. Discussion questions + 8. Further exploration topics + """ + messages = [ { "role": "user", - "content": f"""Analyze this case study and create a structured board plan. + "content": f"""Analyze this {document_type.lower()} and create a structured {framework_type}. - Case study: {text} + Content: {text[:5000]} - Create a detailed analysis with these exact sections: - 1. Main concept/technology being discussed - 2. Industry suitability and context - 3. Benefits and impact - 4. Key roles and perspectives - 5. Implementation details - - Data requirements - - Scaling considerations - - Performance metrics - 6. Next steps and recommendations + {sections_prompt} - Also include risk assessment. + Also include a "Questions for Further Exploration" section. Format the response as a JSON object with this structure: {{ - "boards": [ + "sections": [ {{ - "title": "BOARD 1: [Title]", + "title": "SECTION 1: [Title]", "points": ["point 1", "point 2", "point 3"] }}, - // ... other boards + // ... other sections ], - "risk_assessment": {{ - "title": "WHAT IS THE RISK OF FAILURE?", - "points": ["risk 1", "risk 2"] - }} + "questions": {{ + "title": "QUESTIONS FOR FURTHER EXPLORATION", + "points": ["question 1", "question 2"] + }}, + "discussion": {{ + "title": "SIMULATED DISCUSSION", + "content": "A 300-400 word simulated discussion between an educator and participants" + }} }}""" } ] @@ -838,7 +1347,6 @@ class BoardPlanAnalyzer: provider="gemini", response_format={"type": "json_object"} ) - # Extract and parse the JSON response content = response.choices[0].message.content @@ -851,21 +1359,21 @@ class BoardPlanAnalyzer: st.code(response.choices[0].message.content) raise - def process_case_study(self, pdf_content: bytes) -> dict: - """Process the case study using Crew AI agents""" + def process_document(self, file_content: bytes, document_type: str) -> dict: + """Process the document using Crew AI agents""" try: # First extract text progress_text = st.empty() - progress_text.text("Extracting text from PDF...") - text_content = self.extract_text_from_pdf(pdf_content) + progress_text.text(f"Extracting text from {document_type.lower()}...") + text_content = self.extract_text_from_file(file_content) # Show extracted text for verification with st.expander("View extracted text"): st.text(text_content[:500] + "...") # Then analyze content - progress_text.text("Analyzing content...") - result = self.analyze_case_study(text_content) + progress_text.text(f"Analyzing {document_type.lower()} content...") + result = self.analyze_document(text_content, document_type) return result @@ -877,7 +1385,7 @@ class BoardPlanAnalyzer: # File upload section - shared across all generators if st.session_state.uploaded_files is None: uploaded_files = st.file_uploader( - "Upload case study files (PDF, DOCX, PPT, PPTX)", + "Upload document files (PDF, DOCX, PPT, PPTX)", accept_multiple_files=True, type=['pdf', 'docx', 'ppt', 'pptx'] ) @@ -887,36 +1395,73 @@ if st.session_state.uploaded_files is None: # Process files only once and store results with st.spinner("Processing uploaded files..."): - combined_text, first_file_text, temp_file_paths = process_files(uploaded_files) + combined_text, first_file_text, temp_file_paths, content_type = process_files(uploaded_files) st.session_state.combined_text = combined_text st.session_state.first_file_text = first_file_text st.session_state.temp_file_paths = temp_file_paths + st.session_state.content_type = content_type # Show file upload summary st.success(f"✅ {len(uploaded_files)} file(s) uploaded and processed successfully") + + # Show uploaded files for file in uploaded_files: st.write(f"- {file.name}") -# Tabs for different generators +# Tabs for different generators with updated names if st.session_state.uploaded_files: - # Use native streamlit tabs (no styling overrides) - tab1, tab2, tab3 = st.tabs([ - "Case Breakdown Generator", - "Teaching Plan Generator", - "Board Plan Generator" - ]) + # Show detected content type and allow user to change it + content_type_col, confidence_col = st.columns(2) + with content_type_col: + st.info(f"📄 Detected content type: **{st.session_state.content_type['type']}**") + with confidence_col: + st.info(f"🔍 Detection confidence: **{st.session_state.content_type['confidence']}**") + + # Always display the document type selection + content_options = ["Case Study", "Scientific Article", "News Item", "Other"] + selected_type = st.selectbox( + "Select document type:", + options=content_options, + index=content_options.index(st.session_state.content_type['type']) if st.session_state.content_type['type'] in content_options else 0, + key="document_type_selector" + ) + + # Update the content type in session state + if selected_type != st.session_state.content_type['type']: + st.session_state.content_type['type'] = selected_type + # Reset generated flags when document type changes + st.session_state.breakdown_generated = False + st.session_state.teaching_plan_generated = False + st.session_state.board_plan_generated = False + st.rerun() + + # Update title based on content type + st.title(get_app_title(st.session_state.content_type["type"])) + + # Customize tab names based on content type + if st.session_state.content_type["type"] == "Case Study": + tab_names = ["Case Breakdown Generator", "Teaching Plan Generator", "Board Plan Generator"] + elif st.session_state.content_type["type"] == "Scientific Article": + tab_names = ["Article Analysis Generator", "Teaching Plan Generator", "Discussion Points Generator"] + elif st.session_state.content_type["type"] == "News Item": + tab_names = ["News Analysis Generator", "Discussion Guide Generator", "Current Events Framework"] + else: + tab_names = ["Content Analysis Generator", "Teaching Guide Generator", "Discussion Framework"] + + # Use native streamlit tabs with content-specific names + tab1, tab2, tab3 = st.tabs(tab_names) - #------------------ Case Breakdown Generator Tab ------------------# + #------------------ Document Analysis Generator Tab ------------------# with tab1: - st.header("Case Breakdown Generator") - st.write("Generate a comprehensive breakdown of the case study with sections for teaching purposes.") + st.header(tab_names[0]) + st.write(f"Generate a comprehensive analysis of the {st.session_state.content_type['type'].lower()} with sections for teaching purposes.") if not st.session_state.breakdown_generated: if not api_key: st.warning("⚠ī¸ Please enter an API key in the sidebar before proceeding.") else: # Initialize the breakdown generator - crew_manager = CaseBreakdownCrew(api_key) + crew_manager = DocumentAnalysisCrew(api_key) # Extract metadata with st.spinner("Extracting document metadata..."): @@ -924,75 +1469,14 @@ if st.session_state.uploaded_files: # Display extracted metadata with option to edit st.subheader("Extracted Document Information") - title = st.text_input("Case Study Title:", title) + title = st.text_input("Document Title:", title) author = st.text_input("Author:", author) - # Initialize sections based on template - sections = { - "Case Synopsis": """ Summarize the case in 3–4 paragraphs of a total 300 words. Explain the company's background, the industry it operates in, - and the key challenge or strategic decision it faces. Discuss any turning points or dilemmas. Why is this case relevant for - business students? - Position in Course: In 1–2 sentences, describe which type of course this case is best suited for - (e.g., Operations Strategy, AI in Business, Global Supply Chain). What key topics does it help students understand""", - - - "Learning Objectives": """List 8–10 learning objectives for this case. A total of 300 words. What should students understand after analyzing this case? Focus on - leadership decisions, operational insights, AI adoption, or ethical concerns""", - - "Teaching Strategies": - """ - Describe 8–10 ways an instructor can effectively teach this case. Should they use role-playing? A total of 350 words. - A structured debate? Analyzing real-world examples? How can students actively engage with the material? 1st point should be Approach and second point should be Objective - """, - "Suggested Teaching Plan": - """ - Outline a structured teaching plan for this case. What should be covered first? A total of 350 words. - When should student activities be introduced? How should the case discussion conclude? - """, - - "Key Points and Insights": - """ - List 10 key insights students should take from this case. What makes this case unique? A total of 300 words. - What are the most important strategic, operational, or ethical considerations? - """, - - "Further Insights": - """ - Provide additional insights that go beyond the case. How does this case relate to - larger business trends? What external factors (e.g., regulation, innovation, geopolitical forces) could impact the situation? A total of 400 words. - """, - - "Discussion Questions & Answers": - """ - Create 8–10 discussion questions about the case. What are the key strategic dilemmas? - Where do trade-offs exist? How can students critically analyze the company's decisions? Provide concise answers. A total of 450 words. - """, - - "Assignment Exercises": - """ - List 8–10 in-depth assignments that encourage strategic thinking, data analysis, - or real-world application. Include a mix of strategy proposals, financial analysis, - role-playing exercises, and ethical debates. Define the expected format - (e.g., business report, presentation, comparative analysis) and objective of each exercise. A total of 550- 600 words. - """, - - "Automated Conversation": - """ - Write an AI-generated conversation between three personas relevant to the case. A total of 650- 700 words. - One should be an executive making a strategic decision, another should be an expert, - and the third should be skeptical or resistant. The conversation should explore the challenges, risks, and strategic importance of the case. - """, - - "Case Suggestions": - """ - List 10 ways to make this case more interactive. Should students role-play as executives? - Simulate a crisis response? Conduct a competitive market analysis? - Suggest unique, hands-on ways to engage with the case. A total of 550 words. - """ - } + # Get sections based on content type + sections = get_sections_by_content_type(st.session_state.content_type["type"]) # Generate button - if st.button("Generate Case Breakdown", key="breakdown_button"): + if st.button("Generate Analysis", key="breakdown_button"): # Create tabs for content and review content_tab, review_tab = st.tabs(["Generated Content", "Content Review"]) @@ -1007,11 +1491,20 @@ if st.session_state.uploaded_files: for i, (section_name, prompt) in enumerate(sections.items()): # Generate content using the CrewAI agents st.info(f"Generating {section_name}...") - content = crew_manager.generate_section_content(st.session_state.combined_text, section_name, prompt) + content = crew_manager.generate_section_content( + st.session_state.combined_text, + section_name, + prompt, + st.session_state.content_type["type"] + ) sections_content[section_name] = content # Generate review - review = crew_manager.review_content(content, section_name) + review = crew_manager.review_content( + content, + section_name, + st.session_state.content_type["type"] + ) reviews[section_name] = review # Display content in preview @@ -1039,7 +1532,8 @@ if st.session_state.uploaded_files: doc = doc_generator.create_word_document( title=title, author=author, - sections_content=sections_content + sections_content=sections_content, + document_type=st.session_state.content_type["type"] ) # Save document to temporary file @@ -1049,10 +1543,20 @@ if st.session_state.uploaded_files: # Allow the user to download the document with open(st.session_state.doc_path, 'rb') as file: + # Generate filename based on content type + if st.session_state.content_type["type"] == "Case Study": + filename = f"{title.lower().replace(' ', '_')}_case_breakdown.docx" + elif st.session_state.content_type["type"] == "Scientific Article": + filename = f"{title.lower().replace(' ', '_')}_article_analysis.docx" + elif st.session_state.content_type["type"] == "News Item": + filename = f"{title.lower().replace(' ', '_')}_news_analysis.docx" + else: + filename = f"{title.lower().replace(' ', '_')}_content_analysis.docx" + st.download_button( - label="đŸ“Ĩ Download Case Breakdown (DOCX)", + label=f"đŸ“Ĩ Download {st.session_state.content_type['type']} Analysis (DOCX)", data=file, - file_name=f"{title.lower().replace(' ', '_')}_case_breakdown.docx", + file_name=filename, mime="application/vnd.openxmlformats-officedocument.wordprocessingml.document", key="download_docx_button" ) @@ -1060,7 +1564,7 @@ if st.session_state.uploaded_files: else: # If we already have generated content, display it without regenerating st.subheader("Extracted Document Information") - st.text_input("Case Study Title:", value=st.session_state.title, key="title_display", disabled=True) + st.text_input("Document Title:", value=st.session_state.title, key="title_display", disabled=True) st.text_input("Author:", value=st.session_state.author, key="author_display", disabled=True) # Display the generated content in tabs @@ -1078,18 +1582,29 @@ if st.session_state.uploaded_files: # Allow the user to download the document without regenerating with open(st.session_state.doc_path, 'rb') as file: + # Generate filename based on content type + + if st.session_state.content_type["type"] == "Case Study": + filename = f"{st.session_state.title.lower().replace(' ', '_')}_case_breakdown.docx" + elif st.session_state.content_type["type"] == "Scientific Article": + filename = f"{st.session_state.title.lower().replace(' ', '_')}_article_analysis.docx" + elif st.session_state.content_type["type"] == "News Item": + filename = f"{st.session_state.title.lower().replace(' ', '_')}_news_analysis.docx" + else: + filename = f"{st.session_state.title.lower().replace(' ', '_')}_content_analysis.docx" + st.download_button( - label="đŸ“Ĩ Download Case Breakdown (DOCX)", + label=f"đŸ“Ĩ Download {st.session_state.content_type['type']} Analysis (DOCX)", data=file, - file_name=f"{st.session_state.title.lower().replace(' ', '_')}_case_breakdown.docx", + file_name=filename, mime="application/vnd.openxmlformats-officedocument.wordprocessingml.document", key="download_docx_button" ) #------------------ Teaching Plan Generator Tab ------------------# with tab2: - st.header("Teaching Plan Generator") - st.write("Generate a comprehensive 2-hour teaching plan for the case study.") + st.header(tab_names[1]) + st.write(f"Generate a comprehensive teaching plan for the {st.session_state.content_type['type'].lower()}.") if not st.session_state.teaching_plan_generated: if not api_key: @@ -1102,22 +1617,21 @@ if st.session_state.uploaded_files: progress_placeholder = st.empty() agent_status_placeholder = st.empty() - # Create a tracker and set its placeholder - tracker = AgentTracker() - tracker.set_placeholder(agent_status_placeholder) - # Initialize progress bar progress_bar = progress_placeholder.progress(0) - # Select LLM provider - llm_provider = "gemini" - # Update progress progress_bar.progress(10) st.info("🔍 Initializing crew and analyzing documents...") - # Create crew - crew = create_teaching_plan_crew(st.session_state.temp_file_paths, llm_provider) + # Create crew with content type awareness + crew, tracker = create_teaching_plan_crew( + st.session_state.temp_file_paths, + st.session_state.content_type["type"] + ) + + # Set the tracker's placeholder + tracker.set_placeholder(agent_status_placeholder) # Update progress progress_bar.progress(30) @@ -1149,11 +1663,21 @@ if st.session_state.uploaded_files: st.subheader("📝 Generated Teaching Plan") st.markdown(result_text) + # Generate appropriate filename based on content type + if st.session_state.content_type["type"] == "Case Study": + filename = "case_study_teaching_plan.md" + elif st.session_state.content_type["type"] == "Scientific Article": + filename = "article_teaching_plan.md" + elif st.session_state.content_type["type"] == "News Item": + filename = "news_discussion_guide.md" + else: + filename = "teaching_plan.md" + # Provide download option st.download_button( label="đŸ“Ĩ Download Teaching Plan (Markdown)", data=result_text, - file_name="teaching_plan.md", + file_name=filename, mime="text/markdown", ) @@ -1162,16 +1686,51 @@ if st.session_state.uploaded_files: # Show instructions with st.expander("ℹī¸ How it works"): - st.write(""" - The Teaching Plan Generator creates a comprehensive 2-hour lesson plan with: + if st.session_state.content_type["type"] == "Case Study": + instructions = """ + The Teaching Plan Generator creates a comprehensive 2-hour lesson plan for case studies with: + + 1. **Clear learning objectives** tied to the case study + 2. **Structured timeline** with time allocations for each activity + 3. **Case discussion framework** for effective analysis + 4. **Group activities** that apply business concepts + 5. **Assessment strategies** to measure understanding + """ + elif st.session_state.content_type["type"] == "Scientific Article": + instructions = """ + The Teaching Plan Generator creates a comprehensive 2-hour lesson plan for scientific articles with: + + 1. **Research objectives** tied to the article's methodology + 2. **Structured timeline** for analyzing research components + 3. **Critical evaluation framework** for assessing research quality + 4. **Application activities** that connect research to practice + 5. **Research extension opportunities** for further exploration + """ + elif st.session_state.content_type["type"] == "News Item": + instructions = """ + The Discussion Guide Generator creates a comprehensive 2-hour lesson plan for news items with: + + 1. **Context objectives** for understanding the news event + 2. **Structured timeline** for exploring different perspectives + 3. **Media literacy framework** for critical news consumption + 4. **Current events activities** that connect to broader concepts + 5. **Discussion strategies** to explore implications and impacts + """ + else: + instructions = """ + The Teaching Guide Generator creates a comprehensive 2-hour lesson plan with: + + 1. **Clear learning objectives** tied to the document content + 2. **Structured timeline** with time allocations for each activity + 3. **Engaging activities** for effective student learning + 4. **Discussion questions** to promote critical thinking + 5. **Assessment strategies** to measure understanding + """ - 1. **Clear learning objectives** tied to the case study - 2. **Structured timeline** with time allocations for each activity - 3. **Engaging activities** for effective student learning - 4. **Discussion questions** to promote critical thinking - 5. **Assessment strategies** to measure understanding + st.write(instructions) - The AI uses a team of specialized agents to analyze your case study, create a draft plan, + st.write(""" + The AI uses a team of specialized agents to analyze your document, create a draft plan, review it for quality, and finalize it into a polished teaching resource. """) else: @@ -1180,149 +1739,301 @@ if st.session_state.uploaded_files: st.subheader("📝 Generated Teaching Plan") st.markdown(st.session_state.teaching_plan_result) - # Provide download option - st.download_button( - label="đŸ“Ĩ Download Teaching Plan (Markdown)", - data=st.session_state.teaching_plan_result, - file_name="teaching_plan.md", - mime="text/markdown", - ) + # Generate appropriate filename based on content type + # Generate appropriate filename based on content type + if st.session_state.content_type["type"] == "Case Study": + md_filename = "case_study_teaching_plan.md" + docx_filename = "case_study_teaching_plan.docx" + docx_title = "Case Study Teaching Plan" + elif st.session_state.content_type["type"] == "Scientific Article": + md_filename = "article_teaching_plan.md" + docx_filename = "article_teaching_plan.docx" + docx_title = "Scientific Article Teaching Plan" + elif st.session_state.content_type["type"] == "News Item": + md_filename = "news_discussion_guide.md" + docx_filename = "news_discussion_guide.docx" + docx_title = "News Item Discussion Guide" + else: + md_filename = "teaching_plan.md" + docx_filename = "teaching_plan.docx" + docx_title = "Teaching Plan" + + # Create columns for download buttons + col1, col2 = st.columns(2) + + # Provide download options + with col1: + st.download_button( + label="đŸ“Ĩ Download as Markdown", + data=st.session_state.teaching_plan_result, + file_name=md_filename, + mime="text/markdown", + ) + + with col2: + # Create DOCX file from the markdown content + docx_path = create_docx_from_markdown( + st.session_state.teaching_plan_result, + docx_title, + st.session_state.content_type["type"] + ) + + # Provide DOCX download button + with open(docx_path, 'rb') as file: + st.download_button( + label="đŸ“Ĩ Download as DOCX", + data=file, + file_name=docx_filename, + mime="application/vnd.openxmlformats-officedocument.wordprocessingml.document", + key="teaching_plan_docx" + ) - #------------------ Board Plan Generator Tab ------------------# + #------------------ Discussion Framework Generator Tab ------------------# with tab3: - st.header("Board Plan Generator") - st.write("Generate a structured board plan analysis for the case study.") + # Update header based on content type + st.header(tab_names[2]) + + # Update description based on content type + if st.session_state.content_type["type"] == "Case Study": + description = "Generate a structured board plan analysis for the case study." + elif st.session_state.content_type["type"] == "Scientific Article": + description = "Generate key discussion points based on the scientific article." + elif st.session_state.content_type["type"] == "News Item": + description = "Generate a current events analysis framework for the news item." + else: + description = "Generate a structured discussion framework for the document." + + st.write(description) if not st.session_state.board_plan_generated: if not api_key: st.warning("⚠ī¸ Please enter an API key in the sidebar before proceeding.") else: - # Create a button to start generation - if st.button("Generate Board Plan", key="board_plan_button"): + # Create a button to start generation with label based on content type + if st.button(f"Generate {tab_names[2]}", key="board_plan_button"): try: - # Select LLM provider - llm_provider = "gemini" - - # Initialize the board plan analyzer - analyzer = BoardPlanAnalyzer(llm_provider=llm_provider) + # Initialize the analyzer + analyzer = DiscussionFrameworkAnalyzer(api_key) - with st.spinner("Analyzing case study to generate board plan..."): + with st.spinner(f"Analyzing {st.session_state.content_type['type'].lower()} to generate {tab_names[2].lower()}..."): # Get content from first uploaded file file = st.session_state.uploaded_files[0] - pdf_content = file.getvalue() + file_content = file.getvalue() - # Process the case study - analysis_result = analyzer.process_case_study(pdf_content) + # Process the document with content type awareness + analysis_result = analyzer.process_document( + file_content, + st.session_state.content_type["type"] + ) # Store in session state st.session_state.board_plan_result = analysis_result st.session_state.board_plan_generated = True # Generate markdown for download - markdown_content = "# Board Plan Analysis\n\n" - for board in analysis_result['boards']: - markdown_content += f"## {board['title']}\n\n" - for point in board['points']: + markdown_content = f"# {tab_names[2]}\n\n" + for section in analysis_result['sections']: + markdown_content += f"## {section['title']}\n\n" + for point in section['points']: markdown_content += f"* {point}\n" markdown_content += "\n" - if 'risk_assessment' in analysis_result: - markdown_content += f"## {analysis_result['risk_assessment']['title']}\n\n" - for point in analysis_result['risk_assessment']['points']: + if 'questions' in analysis_result: + markdown_content += f"## {analysis_result['questions']['title']}\n\n" + for point in analysis_result['questions']['points']: markdown_content += f"* {point}\n" st.session_state.board_plan_markdown = markdown_content - st.success("Board plan generated successfully!") + st.success(f"{tab_names[2]} generated successfully!") # Display the analysis results - st.subheader("Board Plan Analysis") - for board in analysis_result['boards']: - with st.expander(board['title'], expanded=True): - for point in board['points']: + st.subheader(tab_names[2]) + for section in analysis_result['sections']: + with st.expander(section['title'], expanded=True): + for point in section['points']: st.markdown(f"â€ĸ {point}") - if 'risk_assessment' in analysis_result: - st.subheader("Risk Assessment") - for point in analysis_result['risk_assessment']['points']: + if 'questions' in analysis_result: + st.subheader("Questions for Further Exploration") + for point in analysis_result['questions']['points']: st.markdown(f"â€ĸ {point}") + + if 'discussion' in analysis_result: + st.subheader("Simulated Discussion") + st.markdown(analysis_result['discussion']['content']) + + # Generate appropriate filename based on content type + if st.session_state.content_type["type"] == "Case Study": + md_filename = "board_plan.md" + docx_filename = "board_plan.docx" + docx_title = "Board Plan Analysis" + elif st.session_state.content_type["type"] == "Scientific Article": + md_filename = "discussion_points.md" + docx_filename = "discussion_points.docx" + docx_title = "Discussion Points Analysis" + elif st.session_state.content_type["type"] == "News Item": + md_filename = "current_events_framework.md" + docx_filename = "current_events_framework.docx" + docx_title = "Current Events Framework" + else: + md_filename = "discussion_framework.md" + docx_filename = "discussion_framework.docx" + docx_title = "Discussion Framework" - # Provide download option - st.download_button( - label="đŸ“Ĩ Download Board Plan (Markdown)", - data=st.session_state.board_plan_markdown, - file_name="board_plan.md", - mime="text/markdown", - ) + # Create columns for download buttons + col1, col2 = st.columns(2) + + # Provide download options + with col1: + st.download_button( + label="đŸ“Ĩ Download as Markdown", + data=st.session_state.board_plan_markdown, + file_name=md_filename, + mime="text/markdown", + ) + + with col2: + # Create DOCX file from the markdown content + docx_path = create_docx_from_markdown( + st.session_state.board_plan_markdown, + docx_title, + st.session_state.content_type["type"] + ) + + # Provide DOCX download button + with open(docx_path, 'rb') as file: + st.download_button( + label="đŸ“Ĩ Download as DOCX", + data=file, + file_name=docx_filename, + mime="application/vnd.openxmlformats-officedocument.wordprocessingml.document", + key="discussion_framework_docx" + ) except Exception as e: st.error(f"An error occurred: {str(e)}") st.error("Please ensure the file is not encrypted and contains extractable text.") - # Show instructions + # Show instructions based on content type with st.expander("ℹī¸ How it works"): - st.write(""" - The Board Plan Generator creates a structured analysis with these components: + if st.session_state.content_type["type"] == "Case Study": + instructions = """ + The Board Plan Generator creates a structured analysis with these components: + + 1. **Main business challenge/opportunity** being addressed in the case study + 2. **Industry context and competitors** relevant to the case + 3. **Key decision points** that require strategic choices + 4. **Stakeholder perspectives** from different roles and viewpoints + 5. **Implementation considerations** including resources, timeline, and metrics + 6. **Alternative approaches** that could be considered + 7. **Learning takeaways** for applying key lessons + """ + elif st.session_state.content_type["type"] == "Scientific Article": + instructions = """ + The Discussion Points Generator creates a structured analysis with these components: + + 1. **Main research question/hypothesis** examined in the article + 2. **Methodology assessment** evaluating research approach + 3. **Key findings summary** highlighting important discoveries + 4. **Limitations and validity** considerations + 5. **Theoretical implications** for the field + 6. **Practical applications** of the research + 7. **Future research directions** suggested by the findings + 8. **Interdisciplinary connections** to other fields + """ + elif st.session_state.content_type["type"] == "News Item": + instructions = """ + The Current Events Framework Generator creates a structured analysis with these components: + + 1. **Main event/development** described in the news item + 2. **Historical and political context** surrounding the event + 3. **Key stakeholders and perspectives** involved + 4. **Media coverage analysis** examining reporting approaches + 5. **Immediate impacts** on various sectors + 6. **Long-term implications** for policy and society + 7. **Related developments** connected to this news + 8. **Critical thinking questions** for deeper analysis + """ + else: + instructions = """ + The Discussion Framework Generator creates a structured analysis with these components: + + 1. **Main topic/theme** of the document + 2. **Key concepts** central to understanding the content + 3. **Important relationships** between ideas and elements + 4. **Supporting evidence** presented in the document + 5. **Counterarguments/limitations** worth considering + 6. **Practical applications** of the content + 7. **Discussion questions** for deeper exploration + 8. **Further exploration topics** related to the content + """ - 1. **Main concept/technology** being discussed in the case study - 2. **Industry suitability and context** for application - 3. **Benefits and impact** of implementing the solution - 4. **Key roles and perspectives** from stakeholders - 5. **Implementation details** including data requirements, scaling, and metrics - 6. **Next steps and recommendations** for moving forward - 7. **Risk assessment** to identify potential challenges + st.write(instructions) + st.write(""" The generator provides a structured framework perfect for teaching, presentations, - or boardroom discussions. + or educational discussions. """) else: # Display the previously generated content - st.success("Board plan generated successfully!") + st.success(f"{tab_names[2]} generated successfully!") # Display the analysis results - st.subheader("Board Plan Analysis") - for board in st.session_state.board_plan_result['boards']: - with st.expander(board['title'], expanded=True): - for point in board['points']: + st.subheader(tab_names[2]) + for section in st.session_state.board_plan_result['sections']: + with st.expander(section['title'], expanded=True): + for point in section['points']: st.markdown(f"â€ĸ {point}") - if 'risk_assessment' in st.session_state.board_plan_result: - st.subheader("Risk Assessment") - for point in st.session_state.board_plan_result['risk_assessment']['points']: + if 'questions' in st.session_state.board_plan_result: + st.subheader("Questions for Further Exploration") + for point in st.session_state.board_plan_result['questions']['points']: st.markdown(f"â€ĸ {point}") + # Generate appropriate filename based on content type + if st.session_state.content_type["type"] == "Case Study": + filename = "board_plan.md" + elif st.session_state.content_type["type"] == "Scientific Article": + filename = "discussion_points.md" + elif st.session_state.content_type["type"] == "News Item": + filename = "current_events_framework.md" + else: + filename = "discussion_framework.md" + # Provide download option st.download_button( - label="đŸ“Ĩ Download Board Plan (Markdown)", + label=f"đŸ“Ĩ Download {tab_names[2]} (Markdown)", data=st.session_state.board_plan_markdown, - file_name="board_plan.md", + file_name=filename, mime="text/markdown", ) else: # Display welcome message and instructions when no files are uploaded st.markdown(""" - ## Welcome to the Case Study Analysis Suite + ## Welcome to the Document Analysis Suite - This application provides three powerful tools for analyzing case studies: + This application provides three powerful tools for analyzing different types of documents: - 1. **Case Breakdown Generator**: Creates a comprehensive breakdown of the case with sections for teaching purposes, formatted as a DOCX document. + 1. **Document Analysis Generator**: Creates a comprehensive analysis with sections tailored to the document type (case study, scientific article, news item, or other content). - 2. **Teaching Plan Generator**: Develops a 2-hour lesson plan with activities, discussions, and assessments based on the case study. + 2. **Teaching Plan Generator**: Develops a 2-hour lesson plan with activities, discussions, and assessments based on the document. - 3. **Board Plan Generator**: Produces a structured board plan analysis with key insights and implementation details. + 3. **Discussion Framework Generator**: Produces a structured analysis with key insights based on the type of document uploaded. ### Getting Started - 1. Choose your API provider in the sidebar (Google Gemini or OpenAI) - 2. Enter your API key - 3. Upload one or more case study files in PDF, DOCX, PPT, or PPTX format - 4. Select the generator tab you want to use - 5. Click the generate button and wait for the results + 1. Enter your API key in the sidebar + 2. Upload one or more document files in PDF, DOCX, PPT, or PPTX format + 3. The system will automatically detect the document type (case study, scientific article, news item, or other) + 4. You can confirm or change the detected document type + 5. Select the generator tab you want to use + 6. Click the generate button and wait for the results - Each generator produces downloadable content in an appropriate format for your use. + Each generator produces downloadable content tailored to your document type. """) # Footer st.divider() -st.caption("Created with CrewAI, Streamlit, and Gemini â€ĸ Built by Arun Kashyap â€ĸ Š 2025") \ No newline at end of file +st.caption("Created with CrewAI, Streamlit, and Gemini â€ĸ Built by Arun Kashyap â€ĸ Š 2025") \ No newline at end of file