Update app.py
Browse files
app.py
CHANGED
@@ -69,22 +69,13 @@ st.write("---")
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# Sidebar for API key configuration
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with st.sidebar:
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st.title("⚙️ Configuration")
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api_key_source = st.radio("Select API Key Provider:",
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["Google (Gemini)", "OpenAI"],
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help="Choose which AI provider to use")
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else:
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api_key = st.text_input("Enter your OpenAI API Key", type="password",
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help="Required for the AI model to function")
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if api_key:
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os.environ["OPENAI_API_KEY"] = api_key
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st.divider()
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# Reset button
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@@ -93,7 +84,6 @@ with st.sidebar:
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del st.session_state[key]
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st.rerun()
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#---------------------------- Utility Functions ----------------------------#
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def extract_text_from_pdf(file):
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@@ -398,6 +388,7 @@ class CaseBreakdownCrew:
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self.api_key = api_key
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def create_metadata_agent(self):
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return Agent(
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role="Metadata Analyzer",
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goal="Extract title and author information from document content",
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@@ -409,6 +400,7 @@ class CaseBreakdownCrew:
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)
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def create_content_generator_agent(self):
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return Agent(
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role="Case Study Content Generator",
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goal="Generate comprehensive case analysis content based on section requirements",
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@@ -420,6 +412,7 @@ class CaseBreakdownCrew:
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)
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def create_content_reviewer_agent(self):
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return Agent(
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role="Content Quality Reviewer",
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goal="Evaluate and score content for quality, relevance, and depth",
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@@ -607,16 +600,10 @@ def create_teaching_plan_crew(file_paths, llm_provider="gemini"):
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tracker.set_placeholder(st.empty())
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# Initialize LLM based on provider
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if llm_provider == "gemini":
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my_llm = LLM(
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model='gemini/gemini-2.0-flash',
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api_key=os.environ.get("GEMINI_API_KEY")
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)
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else:
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my_llm = LLM(
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# Define agents with callbacks for UI updates
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pdf_analyzer = Agent(
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@@ -708,21 +695,15 @@ def create_teaching_plan_crew(file_paths, llm_provider="gemini"):
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#---------------------------- Board Plan Generator ----------------------------#
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class BoardPlanAnalyzer:
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def __init__(self
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self.model = "gemini/gemini-2.0-flash"
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else:
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api_key = os.environ.get('OPENAI_API_KEY')
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self.model = "gpt-4-turbo"
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if not api_key:
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raise ValueError(
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else:
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os.environ['OPENAI_API_KEY'] = api_key
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litellm.set_verbose = True
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@@ -745,6 +726,7 @@ class BoardPlanAnalyzer:
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description="Extracts text content from PDF files"
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allow_delegation=False,
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verbose=True
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)
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@@ -761,6 +743,7 @@ class BoardPlanAnalyzer:
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description="Analyzes case study and creates structured board plan"
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allow_delegation=False,
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verbose=True
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)
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@@ -904,7 +887,7 @@ if st.session_state.uploaded_files:
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st.warning("⚠️ Please enter an API key in the sidebar before proceeding.")
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else:
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# Initialize the breakdown generator
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crew_manager = CaseBreakdownCrew(api_key)
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# Extract metadata
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with st.spinner("Extracting document metadata..."):
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@@ -1098,7 +1081,7 @@ if st.session_state.uploaded_files:
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progress_bar = progress_placeholder.progress(0)
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# Select LLM provider
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llm_provider = "gemini"
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# Update progress
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progress_bar.progress(10)
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# Sidebar for API key configuration
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with st.sidebar:
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st.title("⚙️ Configuration")
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api_key = st.text_input("Enter your Gemini API Key", type="password",
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help="Required for the AI model to function")
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if api_key:
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os.environ["GEMINI_API_KEY"] = api_key
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os.environ["GOOGLE_API_KEY"] = api_key
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st.divider()
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# Reset button
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del st.session_state[key]
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st.rerun()
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#---------------------------- Utility Functions ----------------------------#
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def extract_text_from_pdf(file):
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self.api_key = api_key
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def create_metadata_agent(self):
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llm = LLM(model='gemini/gemini-2.0-flash', api_key=self.api_key)
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return Agent(
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role="Metadata Analyzer",
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goal="Extract title and author information from document content",
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)
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def create_content_generator_agent(self):
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llm = LLM(model='gemini/gemini-2.0-flash', api_key=self.api_key)
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return Agent(
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role="Case Study Content Generator",
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goal="Generate comprehensive case analysis content based on section requirements",
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)
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def create_content_reviewer_agent(self):
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llm = LLM(model='gemini/gemini-2.0-flash', api_key=self.api_key)
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return Agent(
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role="Content Quality Reviewer",
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goal="Evaluate and score content for quality, relevance, and depth",
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tracker.set_placeholder(st.empty())
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# Initialize LLM based on provider
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my_llm = LLM(
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model='gemini/gemini-2.0-flash',
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api_key=os.environ.get("GEMINI_API_KEY")
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)
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# Define agents with callbacks for UI updates
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pdf_analyzer = Agent(
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#---------------------------- Board Plan Generator ----------------------------#
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class BoardPlanAnalyzer:
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def __init__(self):
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api_key = os.environ.get('GEMINI_API_KEY')
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self.model = "gemini/gemini-2.0-flash"
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if not api_key:
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raise ValueError("Gemini API key not found")
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os.environ['GEMINI_API_KEY'] = api_key
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self.api_key = api_key
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litellm.set_verbose = True
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description="Extracts text content from PDF files"
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allow_delegation=False,
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llm=self.model,
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verbose=True
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)
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description="Analyzes case study and creates structured board plan"
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allow_delegation=False,
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llm=self.model,
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verbose=True
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)
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st.warning("⚠️ Please enter an API key in the sidebar before proceeding.")
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else:
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# Initialize the breakdown generator
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crew_manager = CaseBreakdownCrew(api_key, api_key_source)
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# Extract metadata
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with st.spinner("Extracting document metadata..."):
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progress_bar = progress_placeholder.progress(0)
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# Select LLM provider
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llm_provider = "gemini"
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# Update progress
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progress_bar.progress(10)
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