app.py

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 feasibility_agent import feasibility_agent_func
from short import graph


# # 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)
    
#     # Check for expected keys in pd_dict before proceeding
#     required_columns = ['use_case', 'description', 'urls_list']
#     print("-----Dict------->", pd_dict)
#     # if not all(col in pd_dict for col in required_columns):
#     #     raise ValueError(f"Missing one or more expected columns: {required_columns}")

#     # 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):
    topic = user_input
    report = graph(topic=topic, max_analysts=5)
    print(report)
    # 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

# Example queries
examples = [
    "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""")
    gr.Markdown("**Note:** The app demo may occasionally show errors due to rate limits of the underlying language model (LLM). If you encounter an error, please try again later. If the problem persists please raise issue. Thank you for your understanding!")

    # 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])

# Run the interface
demo.launch()