Validation is added to deal with speculations.

app.py

@@ -2,35 +2,31 @@ import gradio as gr
 import google.generativeai as genai
 
 # Configure Google Gemini API
-genai.configure(api_key="AIzaSyDBGF5y7WqDB0SO7-UO6yjshiEZN3Hpt3g")  # Replace with your API key
+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):
-    model = genai.GenerativeModel('gemini-1.5-flash')
+    # 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."
 
-    # Input prompt for extracting unarticulated needs and wants
+    # Concise prompt to limit output to essential insights
     input_prompt = f"""
-    From the following user story, extract the unarticulated needs and wants.
-    User story: {input_text}
+    Based on the user story "{input_text}", briefly extract any unarticulated needs and wants.
     
-    Needs are the unspoken requirements or desires the person might not have expressed directly. 
-    Wants are the things the person wishes for but didn't explicitly say.
+    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
 
-# Enhanced Gradio interface function with input validation
+# Gradio interface function
 def extract_needs_and_wants(user_story):
-    # Check if the input story is adequate (e.g., at least 20 characters or more than a few words)
-    if len(user_story.strip()) < 20 or len(user_story.split()) < 5:
-        return "Please provide a detailed user story with sufficient content for analysis."
-    
     try:
-        # Process the input if it meets the criteria
         return get_gemini_response(user_story)
     except Exception as e:
         return f"Error: {str(e)}"