app.py

import gradio as gr
import google.generativeai as genai

# Configure Google Gemini API
genai.configure(api_key="AIzaSyDBGF5y7WqDB0SO7-UO6yjshiEZN3Hpt3g")  # Replace with your actual API key

# Function to get a response from the Google Gemini model
def get_gemini_response(input_text):
    # Check if the input is detailed enough
    if len(input_text.split()) < 10:
        return "Please provide a more detailed user story to help generate relevant needs and wants."

    # Concise prompt to limit output to essential insights
    input_prompt = f"""
    Based on the user story "{input_text}", briefly extract any unarticulated needs and wants.
    
    Only provide essential needs and wants directly relevant to the given story. Do not speculate or over-extrapolate.
    
    Needs and Wants:
    """

    # Generate the content based on text input
    model = genai.GenerativeModel('gemini-1.5-flash')
    response = model.generate_content([input_text, input_prompt])
    return response.text

# Gradio interface function
def extract_needs_and_wants(user_story):
    try:
        return get_gemini_response(user_story)
    except Exception as e:
        return f"Error: {str(e)}"

# Create the Gradio interface
interface = gr.Interface(
    fn=extract_needs_and_wants,
    inputs="text",
    outputs="text",
    title="Unarticulated Needs & Wants Extractor",
    description="Enter a detailed user story to extract the unarticulated needs and wants using the Gemini model.",
    examples=[["The user often speaks about wanting to improve their health but is hesitant to join a gym."]]
)

# Launch the Gradio app
interface.launch()