Update app.py

app.py

@@ -1,154 +1,154 @@
-import gradio as gr
-from setup import *
-import pandas as pd
-from openpyxl import Workbook
-from openpyxl.utils.dataframe import dataframe_to_rows
-from openpyxl.styles import Font
-from agents import research_agent
-from vectorstore import extract_urls, urls_classify_list, clean_and_extract_html_data
-from usecase_agent import usecase_agent_func, vectorstore_writing
-from feasibility_agent import feasibility_agent_func
-
-
-
-# Function to create Excel file
-def create_excel(df):
-    # Create a new Excel workbook and select the active sheet
-    wb = Workbook()
-    ws = wb.active
-    ws.title = "Use Cases"
-
-    # Define and write headers to the Excel sheet
-    headers = ['Use Case', 'Description', 'URLs']
-    ws.append(headers)
-
-    # Write data rows
-    for _, row in df.iterrows():
-        try:
-            use_case = row['use_case']
-            description = row['description']
-            urls = row['urls_list']
-
-            ws.append([use_case, description, None])  # Add use case and description
-            if urls:
-                for url_index, url in enumerate(urls):
-                    cell = ws.cell(row=ws.max_row, column=3)  # URLs go into the third column
-                    cell.value = url
-                    cell.hyperlink = url
-                    cell.font = Font(color="0000FF", underline="single")
-                    
-                    # Add a new row for additional URLs
-                    if url_index < len(urls) - 1:
-                        ws.append([None, None, None])
-        except KeyError as e:
-            print(f"Missing key in DataFrame row: {e}")
-        except Exception as e:
-            print(f"Unexpected error while processing row: {e}")
-
-    excel_file_path = "GenAI_use_cases_feasibility.xlsx"
-    wb.save(excel_file_path)
-    return excel_file_path
-
-
-# Function to handle the report and create the DataFrame
-def pd_creation(report):
-    # Assuming feasibility_agent_func returns a dictionary
-    pd_dict = feasibility_agent_func(report)
-    
-    # Create the DataFrame from the dictionary
-    df = pd.DataFrame(pd_dict)
-    
-    # Convert the dataframe to the format expected by Gradio (list of lists)
-    data = df.values.tolist()  # This creates a list of lists from the dataframe
-    
-    # Create the Excel file and return its path
-    excel_file_path = create_excel(df)  # Create the Excel file and get its path
-    
-    return data, excel_file_path  # Return the formatted data and the Excel file path
-
-
-# Main function that handles the user query and generates the report
-def main(user_input):
-    # Research Agent
-    agentstate_result = research_agent(user_input)
-
-    # Vector Store
-    urls, content = extract_urls(agentstate_result)
-    pdf_urls, html_urls = urls_classify_list(urls)
-    html_docs = clean_and_extract_html_data(html_urls)
-
-    # Writing vector store (not explicitly defined in your example)
-    vectorstore_writing(html_docs)
-
-    # Use-case agent
-    company_name = agentstate_result['company']
-    industry_name = agentstate_result['industry']
-
-    if company_name:
-        topic = f'GenAI Usecases in {company_name} and {industry_name} industry. Explore {company_name} GenAI applications, key offerings, strategic focus areas, competitors, and market share.'
-    else:
-        topic = f'GenAI Usecases in {industry_name}. Explore {industry_name} GenAI applications, trends, challenges, and opportunities.'
-    max_analysts = 3
-
-    report = usecase_agent_func(topic, max_analysts)
-    pd_dict, excel_file_path = pd_creation(report)
-    
-    # Save the report as a markdown file
-    report_file_path = "generated_report.md"
-    with open(report_file_path, "w") as f:
-        f.write(report)
-    
-    return report, report_file_path , excel_file_path
-
-
-# Example queries
-examples = [
-    "AI in healthcare industry",
-    "How is the retail industry leveraging AI and ML?",
-    "AI applications in automotive manufacturing"
-]
-
-
-# Creating the Gradio interface
-with gr.Blocks(theme=gr.themes.Soft(font=gr.themes.GoogleFont('Open Sans'))) as demo:
-    # Header section
-    gr.HTML("<center><h1>UseCaseGenie - Discover GenAI Use cases for your company and Industry! 🤖🧑‍🍳.</h1><center>") 
-    gr.Markdown("""#### This GenAI Assistant 🤖 helps you discover and explore Generative AI use cases for your company and industry. 
-    You can download the generated use case report as a <b>Markdown file</b> to gain insights and explore relevant GenAI applications.
-    ### <b>Steps:</b>
-    1. <b>Enter your query</b> regarding any company or industry.  
-    2. <b>Click on the 'Submit' button</b> and wait for the GenAI assistant to generate the report.  
-    3. <b>Download the generated report<b> 
-    4. Explore the GenAI use cases and URLs for further analysis. 
-    """)
-
-
-    # Input for the user query
-    with gr.Row():
-        user_input = gr.Textbox(label="Enter your Query", placeholder='Type_here...')
-
-    # Examples to help users with inputs
-    with gr.Row():  
-        gr.Examples(examples=examples, inputs=user_input)
-
-    # Buttons for submitting and downloading
-    with gr.Row():
-        submit_button = gr.Button("Submit")
-        clear_btn = gr.ClearButton([user_input], value='Clear')
-
-    # File download buttons
-    with gr.Row():
-        # Create a downloadable markdown file
-        download_report_button = gr.File(label="Usecases Report")    
-
-        # Create a downloadable Excel file
-        download_excel_button = gr.File(label="Feasibility Excel File")   
-
-    # Display report in Markdown format
-    with gr.Row():
-        report_output = gr.Markdown()
-
-    submit_button.click(main, inputs=[user_input], outputs=[report_output, download_report_button,download_excel_button])
-
-# Run the interface
-demo.launch()
+import gradio as gr
+from setup import *
+import pandas as pd
+from openpyxl import Workbook
+from openpyxl.utils.dataframe import dataframe_to_rows
+from openpyxl.styles import Font
+from agents import research_agent
+from vectorstore import extract_urls, urls_classify_list, clean_and_extract_html_data
+from usecase_agent import usecase_agent_func, vectorstore_writing
+from feasibility_agent import feasibility_agent_func
+
+
+
+# Function to create Excel file
+def create_excel(df):
+    # Create a new Excel workbook and select the active sheet
+    wb = Workbook()
+    ws = wb.active
+    ws.title = "Use Cases"
+
+    # Define and write headers to the Excel sheet
+    headers = ['Use Case', 'Description', 'URLs']
+    ws.append(headers)
+
+    # Write data rows
+    for _, row in df.iterrows():
+        try:
+            use_case = row['use_case']
+            description = row['description']
+            urls = row['urls_list']
+
+            ws.append([use_case, description, None])  # Add use case and description
+            if urls:
+                for url_index, url in enumerate(urls):
+                    cell = ws.cell(row=ws.max_row, column=3)  # URLs go into the third column
+                    cell.value = url
+                    cell.hyperlink = url
+                    cell.font = Font(color="0000FF", underline="single")
+                    
+                    # Add a new row for additional URLs
+                    if url_index < len(urls) - 1:
+                        ws.append([None, None, None])
+        except KeyError as e:
+            print(f"Missing key in DataFrame row: {e}")
+        except Exception as e:
+            print(f"Unexpected error while processing row: {e}")
+
+    excel_file_path = "GenAI_use_cases_feasibility.xlsx"
+    wb.save(excel_file_path)
+    return excel_file_path
+
+
+# Function to handle the report and create the DataFrame
+def pd_creation(report):
+    # Assuming feasibility_agent_func returns a dictionary
+    pd_dict = feasibility_agent_func(report)
+    
+    # Create the DataFrame from the dictionary
+    df = pd.DataFrame(pd_dict)
+    
+    # Convert the dataframe to the format expected by Gradio (list of lists)
+    data = df.values.tolist()  # This creates a list of lists from the dataframe
+    
+    # Create the Excel file and return its path
+    excel_file_path = create_excel(df)  # Create the Excel file and get its path
+    
+    return data, excel_file_path  # Return the formatted data and the Excel file path
+
+
+# Main function that handles the user query and generates the report
+def main(user_input):
+    # Research Agent
+    agentstate_result = research_agent(user_input)
+
+    # Vector Store
+    urls, content = extract_urls(agentstate_result)
+    pdf_urls, html_urls = urls_classify_list(urls)
+    html_docs = clean_and_extract_html_data(html_urls)
+
+    # Writing vector store (not explicitly defined in your example)
+    vectorstore_writing(html_docs)
+
+    # Use-case agent
+    company_name = agentstate_result['company']
+    industry_name = agentstate_result['industry']
+
+    if company_name:
+        topic = f'GenAI Usecases in {company_name} and {industry_name} industry. Explore {company_name} GenAI applications, key offerings, strategic focus areas, competitors, and market share.'
+    else:
+        topic = f'GenAI Usecases in {industry_name}. Explore {industry_name} GenAI applications, trends, challenges, and opportunities.'
+    max_analysts = 3
+
+    report = usecase_agent_func(topic, max_analysts)
+    pd_dict, excel_file_path = pd_creation(report)
+    
+    # Save the report as a markdown file
+    report_file_path = "generated_report.md"
+    with open(report_file_path, "w") as f:
+        f.write(report)
+    
+    return report, report_file_path , excel_file_path
+
+
+# Example queries
+examples = [
+    "AI in healthcare industry",
+    "How is the retail industry leveraging AI and ML?",
+    "AI applications in automotive manufacturing"
+]
+
+
+# Creating the Gradio interface
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    # Header section
+    gr.HTML("<center><h1>UseCaseGenie - Discover GenAI Use cases for your company and Industry! 🤖🧑‍🍳.</h1><center>") 
+    gr.Markdown("""#### This GenAI Assistant 🤖 helps you discover and explore Generative AI use cases for your company and industry. 
+    You can download the generated use case report as a <b>Markdown file</b> to gain insights and explore relevant GenAI applications.
+    ### <b>Steps:</b>
+    1. <b>Enter your query</b> regarding any company or industry.  
+    2. <b>Click on the 'Submit' button</b> and wait for the GenAI assistant to generate the report.  
+    3. <b>Download the generated report<b> 
+    4. Explore the GenAI use cases and URLs for further analysis. 
+    """)
+
+
+    # Input for the user query
+    with gr.Row():
+        user_input = gr.Textbox(label="Enter your Query", placeholder='Type_here...')
+
+    # Examples to help users with inputs
+    with gr.Row():  
+        gr.Examples(examples=examples, inputs=user_input)
+
+    # Buttons for submitting and downloading
+    with gr.Row():
+        submit_button = gr.Button("Submit")
+        clear_btn = gr.ClearButton([user_input], value='Clear')
+
+    # File download buttons
+    with gr.Row():
+        # Create a downloadable markdown file
+        download_report_button = gr.File(label="Usecases Report")    
+
+        # Create a downloadable Excel file
+        download_excel_button = gr.File(label="Feasibility Excel File")   
+
+    # Display report in Markdown format
+    with gr.Row():
+        report_output = gr.Markdown()
+
+    submit_button.click(main, inputs=[user_input], outputs=[report_output, download_report_button,download_excel_button])
+
+# Run the interface
+demo.launch()