Update app.py

app.py

@@ -2,13 +2,11 @@ import gradio as gr
 import requests
 
 # Function to call the Flask API and return the LLM response
-def get_match_making_result(
-    name1, year1, month1, day1, hour1, minute1, city1, country1,
-    name2, year2, month2, day2, hour2, minute2, city2, country2, language
-):
+def get_match_making_result(chat_history, name1, year1, month1, day1, hour1, minute1, city1, country1,
+                            name2, year2, month2, day2, hour2, minute2, city2, country2, language):
     # Define the API endpoint
     api_url = "https://ai-research.quarkgen.ai/templedekho/match_maker/v1"
-    
+
     # Prepare the payload to send to the API
     data = {
         "name1": name1, "year1": year1, "month1": month1, "day1": day1, "hour1": hour1, "minute1": minute1, "city1": city1, "country1": country1,
@@ -22,16 +20,24 @@ def get_match_making_result(
         
         # Check if the request was successful
         if response.status_code == 200:
-            return response.json().get("result", "No result returned from the server.")
+            result = response.json().get("result", "No result returned from the server.")
+            # Update chat history with bot's response
+            chat_history.append(("AI Guru", result))
+            return chat_history
         else:
-            return f"Error: {response.status_code}, {response.text}"
+            error_message = f"Error: {response.status_code}, {response.text}"
+            chat_history.append(("AI Guru", error_message))
+            return chat_history
     except Exception as e:
-        return f"An error occurred: {str(e)}"
+        error_message = f"An error occurred: {str(e)}"
+        chat_history.append(("AI Guru", error_message))
+        return chat_history
 
-# Create the Gradio interface
-def create_gradio_interface():
+# Create the Gradio chatbot interface
+def create_gradio_chat_interface():
     with gr.Blocks() as interface:
-        # Add input fields for Person 1
+        chat_history = gr.Chatbot(label="AI Guru Match Maker Chat")
+
         with gr.Row():
             with gr.Column():
                 name1 = gr.Textbox(label="Name (Person 1)", placeholder="Enter the name of Person 1")
@@ -43,7 +49,6 @@ def create_gradio_interface():
                 city1 = gr.Textbox(label="Birth City (Person 1)", placeholder="Enter the city of birth")
                 country1 = gr.Textbox(label="Birth Country (Person 1)", placeholder="Enter the country of birth")
         
-        # Add input fields for Person 2
         with gr.Row():
             with gr.Column():
                 name2 = gr.Textbox(label="Name (Person 2)", placeholder="Enter the name of Person 2")
@@ -55,31 +60,47 @@ def create_gradio_interface():
                 city2 = gr.Textbox(label="Birth City (Person 2)", placeholder="Enter the city of birth")
                 country2 = gr.Textbox(label="Birth Country (Person 2)", placeholder="Enter the country of birth")
         
-        # Add language selection
+        # Language selection
         language = gr.Radio(
             choices=["EN", "HI"],
             label="Select Response Language",
-            value="EN"
+            value="EN",
+            tooltip="Choose 'EN' for English or 'HI' for Hindi"
         )
+
+        # Message to input (acts as a conversation continuation)
+        with gr.Row():
+            user_message = gr.Textbox(label="Ask a question to AI Guru...", placeholder="Enter your question here")
         
-        # Add a submit button and display the results
-        result = gr.Textbox(label="Generated Match Making Result", placeholder="Result will be displayed here...")
+        # Function to update chat on each user input
+        def update_chat(chat_history, user_message):
+            chat_history.append(("User", user_message))
+            return chat_history, ""
+
+        # Submit button for both initiating chat and asking further questions
         submit_btn = gr.Button("Submit")
-        
-        # Call the API when the button is clicked
         submit_btn.click(
-            fn=get_match_making_result,
-            inputs=[name1, year1, month1, day1, hour1, minute1, city1, country1, name2, year2, month2, day2, hour2, minute2, city2, country2, language],
-            outputs=result
+            update_chat,
+            inputs=[chat_history, user_message],
+            outputs=[chat_history, user_message]
+        )
+
+        # Match making button to trigger the calculation
+        match_btn = gr.Button("Get Match Making Result")
+        match_btn.click(
+            get_match_making_result,
+            inputs=[chat_history, name1, year1, month1, day1, hour1, minute1, city1, country1, 
+                    name2, year2, month2, day2, hour2, minute2, city2, country2, language],
+            outputs=[chat_history]
         )
 
     return interface
 
 # Start the Gradio interface
 if __name__ == "__main__":
-    title = "AI Guru Match Maker"
-    description = "Enter your and your partner's details below to get personalized astrological aspects and match-making predictions from the AI Guru."
+    title = "AI Guru Match Maker Chat"
+    description = "Chat with AI Guru and get personalized astrological match-making predictions."
     
-    # Launch the Gradio interface
-    interface = create_gradio_interface()
+    # Launch the Gradio chatbot interface
+    interface = create_gradio_chat_interface()
     interface.launch()