Update app.py

app.py

@@ -2,6 +2,7 @@ import os
 import gradio as gr
 import openai
 from langdetect import detect
+import json
 
 # Set up OpenAI API with your custom endpoint
 openai.api_key = os.getenv("API_KEY")
@@ -66,6 +67,34 @@ def get_groq_response(message, user_language):
     except Exception as e:
         return f"Oops, looks like something went wrong! Error: {str(e)}"
 
+# Function to format the response data in a readable and copyable form
+def format_response(data):
+    # Check if the dataset is empty or not in a format that can be iterated
+    if not data:
+        return "No data available for this query."
+
+    # Ensure that the data is iterable (list, dict, etc.)
+    if isinstance(data, dict):
+        formatted_response = "Here are the insights for your query:\n\n"
+        
+        for key, value in data.items():
+            formatted_response += f"**{key.capitalize()}**:\n"
+            if isinstance(value, list):  # Check if the value is a list
+                for idx, item in enumerate(value, start=1):
+                    formatted_response += f"{idx}. {item}\n"
+            else:
+                formatted_response += f"{value}\n"
+            formatted_response += "\n"
+
+    elif isinstance(data, list):  # If the data is a list
+        formatted_response = "Here are the insights for your query:\n\n"
+        for idx, item in enumerate(data, start=1):
+            formatted_response += f"{idx}. {item}\n"
+    else:
+        formatted_response = str(data)
+
+    return formatted_response.strip()
+
 # Function to handle the interaction and queries
 def market_analysis_agent(user_input, history=[]):
     try:
@@ -81,25 +110,28 @@ def market_analysis_agent(user_input, history=[]):
         
         # Handle private market queries with datasets
         if "company" in user_input.lower():
-            response = format_response(company_profile)
+            response = company_profile
         elif "financials" in user_input.lower():
-            response = format_response(financials)
+            response = financials
         elif "investors" in user_input.lower():
-            response = format_response(investors)
+            response = investors
         elif "products" in user_input.lower():
-            response = format_response(products_services)
+            response = products_services
         elif "news" in user_input.lower() or "updates" in user_input.lower():
-            response = format_response(news_updates)
+            response = news_updates
         elif "legal" in user_input.lower() or "compliance" in user_input.lower():
-            response = format_response(legal_compliance)
+            response = legal_compliance
         elif "social media" in user_input.lower() or "instagram" in user_input.lower() or "linkedin" in user_input.lower() or "twitter" in user_input.lower():
-            response = format_response(social_media)
+            response = social_media
         elif "workforce" in user_input.lower():
-            response = format_response(workforce)
+            response = workforce
         else:
             # Get dynamic AI response if query doesn't match predefined terms
             response = get_groq_response(user_input, user_language)
 
+        # Format the response for easy readability and copy-pasting
+        formatted_response = format_response(response)
+
         # Add some professional and engaging replies for the user
         cool_replies = [
             "That’s a great insight! Keep the questions coming. 🔍",
@@ -108,32 +140,15 @@ def market_analysis_agent(user_input, history=[]):
             "You've got the right focus. Let's sharpen those strategies. 🧠",
             "You're on the right track. Let’s optimize that idea! 🔧"
         ]
-        response = f"{response} {cool_replies[hash(user_input) % len(cool_replies)]}"
-        
+        formatted_response += f"\n{cool_replies[hash(user_input) % len(cool_replies)]}"
+
         # Add to chat history
-        history.append((user_input, response))
+        history.append((user_input, formatted_response))
         return history, history
 
     except Exception as e:
         return [(user_input, f"Oops, something went wrong: {str(e)}")], history
 
-# Function to format dataset responses for readability and easy copying
-def format_response(data):
-    # Check if the dataset is empty
-    if not data:
-        return "No data available for this query."
-
-    formatted_response = "Here are the insights for your query:\n\n"
-    
-    # Loop through each dataset and format it in a readable manner
-    for key, value in data.items():
-        formatted_response += f"**{key.capitalize()}**:\n"
-        for idx, item in enumerate(value, start=1):
-            formatted_response += f"{idx}. {item}\n"
-        formatted_response += "\n"
-    
-    return formatted_response.strip()
-
 # Gradio Interface setup
 chat_interface = gr.Interface(
     fn=market_analysis_agent,  # Function for handling user interaction