thinking mode

app.py

@@ -1,3 +1,92 @@
+# # import gradio as gr
+# # from openai import OpenAI
+
+# # # 🔹 Configure your agent
+# # agent_endpoint = "https://q77iuwf7ncfemoonbzon2iyd.agents.do-ai.run/api/v1/"
+# # agent_access_key = "CzIwmTIDFNWRRIHvxVNzKWztq8rn5S5w"
+
+# # client = OpenAI(base_url=agent_endpoint, api_key=agent_access_key)
+
+# # # Parameters
+# # DEFAULT_RETRIEVAL_RUNS = 3  # adjustable in UI
+
+# # def policy_chat(message, history, retrieval_runs=DEFAULT_RETRIEVAL_RUNS):
+# #     """
+# #     Chatbot with streaming + multiple retrieval runs.
+# #     """
+# #     # Show "processing" placeholder
+# #     history = history + [[message, "Processing..."]]
+# #     yield history, history  
+
+# #     aggregated_responses = []
+
+# #     for run in range(retrieval_runs):
+# #         try:
+# #             stream = client.chat.completions.create(
+# #                 model="n/a",  # agent handles routing
+# #                 messages=[
+# #                     {"role": "system", "content": "The data must be returned verbatim. Please be quite detailed and include all information. You are new to the analysis of policy documents, hence you need to be objective in retrieving information, and it is not expected that you will analyse and interpret the information."},
+# #                     *[{"role": "user", "content": u} if i % 2 == 0 else {"role": "assistant", "content": b}
+# #                       for i, (u, b) in enumerate(history[:-1])],  # exclude placeholder
+# #                     {"role": "user", "content": message},
+# #                 ],
+# #                 extra_body={"include_retrieval_info": True},
+# #                 stream=True,
+# #             )
+
+# #             response_text = ""
+# #             for chunk in stream:
+# #                 delta = chunk.choices[0].delta
+# #                 if delta and delta.content:  # delta.content is a string or None
+# #                     response_text += delta.content
+# #                     # Stream update to Gradio UI
+# #                     history[-1][1] = response_text
+# #                     yield history, history
+
+# #             aggregated_responses.append(response_text or "⚠️ Empty response")
+
+# #         except Exception as e:
+# #             aggregated_responses.append(f"⚠️ Error during run {run+1}: {str(e)}")
+
+# #     # 🔹 Choose the “best” response (longest for now)
+# #     best_response = max(aggregated_responses, key=len, default="⚠️ No response")
+# #     history[-1][1] = best_response
+# #     yield history, history
+
+
+# # with gr.Blocks(theme=gr.themes.Soft(primary_hue="blue", secondary_hue="gray")) as demo:
+# #     gr.Markdown("# 🤖 Policy-Agent Chatbot\nAsk me about policies. I’ll query the knowledge base multiple times to retrieve the best answer for you!")
+
+# #     popup_widget_html = '''<style>
+# #         .policy-agent-popup-container { position:fixed; bottom:16px; right:16px; z-index:9999; }
+# #         </style>
+# #         <div class="policy-agent-popup-container">
+# #             <script async
+# #                 src="https://q77iuwf7ncfemoonbzon2iyd.agents.do-ai.run/static/chatbot/widget.js"
+# #                 data-agent-id="fcad9141-8590-11f0-b074-4e013e2ddde4"
+# #                 data-chatbot-id="oTQKgtWMkQLbLVw7CIHkbxw25Pu9jekn"
+# #                 data-name="policy-agent Chatbot"
+# #                 data-primary-color="#031B4E"
+# #                 data-secondary-color="#E5E8ED"
+# #                 data-button-background-color="#0061EB"
+# #                 data-starting-message="Hello! I am your policy analysis bot made to help you comb through the policies."
+# #                 data-logo="/static/chatbot/icons/default-agent.svg">
+# #             </script>
+# #         </div>'''
+# #     gr.HTML(popup_widget_html)
+
+# #     chatbot = gr.Chatbot(height=500)
+# #     msg = gr.Textbox(placeholder="Type your question...")
+# #     retrieval_slider = gr.Slider(1, 10, value=DEFAULT_RETRIEVAL_RUNS, step=1, label="Number of retrieval runs")
+# #     clear = gr.Button("Clear Chat")
+
+# #     msg.submit(policy_chat, [msg, chatbot, retrieval_slider], [chatbot, chatbot])
+# #     msg.submit(lambda: "", None, msg)  # clear textbox
+# #     clear.click(lambda: None, None, chatbot, queue=False)
+
+# # if __name__ == "__main__":
+# #     demo.launch(debug=True)
+
 # import gradio as gr
 # from openai import OpenAI
 
@@ -9,19 +98,35 @@
 
 # # Parameters
 # DEFAULT_RETRIEVAL_RUNS = 3  # adjustable in UI
+# stop_flag = {"stop": False}  # global flag for stopping runs
+
+# def stop_runs():
+#     """Set stop flag to True to interrupt current processing."""
+#     stop_flag["stop"] = True
+#     return "⛔ Retrieval stopped by user."
+
 
 # def policy_chat(message, history, retrieval_runs=DEFAULT_RETRIEVAL_RUNS):
 #     """
-#     Chatbot with streaming + multiple retrieval runs.
+#     Runs multiple retrievals, stores responses, and returns the best one.
 #     """
-#     # Show "processing" placeholder
+#     # Reset stop flag
+#     stop_flag["stop"] = False
+
+#     # Show "processing" placeholder in UI
 #     history = history + [[message, "Processing..."]]
 #     yield history, history  
 
 #     aggregated_responses = []
 
 #     for run in range(retrieval_runs):
+#         if stop_flag["stop"]:
+#             history[-1][1] = "⛔ Stopped before completion."
+#             yield history, history
+#             return
+
 #         try:
+#             # Stream response but collect full text silently
 #             stream = client.chat.completions.create(
 #                 model="n/a",  # agent handles routing
 #                 messages=[
@@ -37,142 +142,206 @@
 #             response_text = ""
 #             for chunk in stream:
 #                 delta = chunk.choices[0].delta
-#                 if delta and delta.content:  # delta.content is a string or None
+#                 if delta and delta.content:
 #                     response_text += delta.content
-#                     # Stream update to Gradio UI
-#                     history[-1][1] = response_text
-#                     yield history, history
 
 #             aggregated_responses.append(response_text or "⚠️ Empty response")
 
 #         except Exception as e:
 #             aggregated_responses.append(f"⚠️ Error during run {run+1}: {str(e)}")
 
-#     # 🔹 Choose the “best” response (longest for now)
+#     # --- Selection logic ---
+#     # For now, pick the "best" as the longest response
 #     best_response = max(aggregated_responses, key=len, default="⚠️ No response")
+
+#     # Replace "Processing..." with final answer
 #     history[-1][1] = best_response
 #     yield history, history
 
 
 # with gr.Blocks(theme=gr.themes.Soft(primary_hue="blue", secondary_hue="gray")) as demo:
-#     gr.Markdown("# 🤖 Policy-Agent Chatbot\nAsk me about policies. I’ll query the knowledge base multiple times to retrieve the best answer for you!")
-
-#     popup_widget_html = '''<style>
-#         .policy-agent-popup-container { position:fixed; bottom:16px; right:16px; z-index:9999; }
-#         </style>
-#         <div class="policy-agent-popup-container">
-#             <script async
-#                 src="https://q77iuwf7ncfemoonbzon2iyd.agents.do-ai.run/static/chatbot/widget.js"
-#                 data-agent-id="fcad9141-8590-11f0-b074-4e013e2ddde4"
-#                 data-chatbot-id="oTQKgtWMkQLbLVw7CIHkbxw25Pu9jekn"
-#                 data-name="policy-agent Chatbot"
-#                 data-primary-color="#031B4E"
-#                 data-secondary-color="#E5E8ED"
-#                 data-button-background-color="#0061EB"
-#                 data-starting-message="Hello! I am your policy analysis bot made to help you comb through the policies."
-#                 data-logo="/static/chatbot/icons/default-agent.svg">
-#             </script>
-#         </div>'''
-#     gr.HTML(popup_widget_html)
+#     gr.Markdown("# 🤖 Policy-Agent Chatbot\nAsk me about policies. I’ll query the knowledge base multiple times!")
 
 #     chatbot = gr.Chatbot(height=500)
 #     msg = gr.Textbox(placeholder="Type your question...")
 #     retrieval_slider = gr.Slider(1, 10, value=DEFAULT_RETRIEVAL_RUNS, step=1, label="Number of retrieval runs")
-#     clear = gr.Button("Clear Chat")
+#     with gr.Row():
+#         clear = gr.Button("Clear Chat")
+#         stop = gr.Button("⛔ Stop")
 
 #     msg.submit(policy_chat, [msg, chatbot, retrieval_slider], [chatbot, chatbot])
 #     msg.submit(lambda: "", None, msg)  # clear textbox
 #     clear.click(lambda: None, None, chatbot, queue=False)
+#     stop.click(stop_runs, None, chatbot, queue=False)
 
 # if __name__ == "__main__":
 #     demo.launch(debug=True)
 
 import gradio as gr
-from openai import OpenAI
+import openai
+import os
+from datetime import datetime
+import json
 
-# 🔹 Configure your agent
-agent_endpoint = "https://q77iuwf7ncfemoonbzon2iyd.agents.do-ai.run/api/v1/"
-agent_access_key = "CzIwmTIDFNWRRIHvxVNzKWztq8rn5S5w"
+# --- Global Variables and Initial Setup ---
+stop_flag = {"stop": False}
 
-client = OpenAI(base_url=agent_endpoint, api_key=agent_access_key)
+# Placeholder for reasoning data
+REASONING_CONTEXT = ""
 
-# Parameters
-DEFAULT_RETRIEVAL_RUNS = 3  # adjustable in UI
-stop_flag = {"stop": False}  # global flag for stopping runs
+# --- Core Functions ---
 
-def stop_runs():
-    """Set stop flag to True to interrupt current processing."""
-    stop_flag["stop"] = True
-    return "⛔ Retrieval stopped by user."
-
-
-def policy_chat(message, history, retrieval_runs=DEFAULT_RETRIEVAL_RUNS):
+def policy_chat(message, history):
     """
-    Runs multiple retrievals, stores responses, and returns the best one.
+    Main chat function that handles user messages, streams responses,
+    and provides reasoning.
     """
-    # Reset stop flag
+    global stop_flag, REASONING_CONTEXT
     stop_flag["stop"] = False
+    
+    # 1. Indicate thinking and reset UI elements
+    yield {
+        thinking_indicator: gr.update(visible=True),
+        reasoning_accordion: gr.update(visible=False, value=""),
+        chatbot: history,
+        stop_btn: gr.update(visible=True),
+        send_btn: gr.update(interactive=False),
+    }
+
+    # --- This is where you would add your retrieval logic ---
+    # For demonstration, we'll create mock reasoning context.
+    # In a real app, you would retrieve this from your RAG pipeline.
+    REASONING_CONTEXT = (
+        "### Retrieved Context\n\n"
+        "1.  **Document:** `policy_document_A.pdf` (Page 17)\n"
+        "    *   **Content:** 'The primary objective for renewable energy is to increase its share in the national grid by 20% by the year 2030.'\n"
+        "2.  **Document:** `strategy_paper_B.pdf` (Page 5)\n"
+        "    *   **Content:** 'Energy efficiency measures will be enforced through new building codes and industrial standards.'\n"
+    )
+    # --- End of retrieval logic section ---
+
+    # 2. Show the reasoning and hide the thinking indicator
+    yield {
+        reasoning_accordion: gr.update(visible=True, value=REASONING_CONTEXT),
+        thinking_indicator: gr.update(visible=False)
+    }
+
+    history.append([message, ""])
+    
+    # 3. Stream the final answer
+    try:
+        # In a real app, you would pass the retrieved context into your prompt
+        response = openai.ChatCompletion.create(
+            model="gpt-3.5-turbo",
+            messages=[{"role": "user", "content": f"Based on the context, answer this question: {message}"}],
+            stream=True,
+            # api_base="your_api_base", # Add your endpoint if needed
+            # api_key="your_api_key"   # Add your key if needed
+        )
+        
+        for chunk in response:
+            if stop_flag["stop"]:
+                break
+            if hasattr(chunk.choices[0].delta, 'content'):
+                content = chunk.choices[0].delta.content
+                if content:
+                    history[-1][1] += content
+                    yield {chatbot: history}
+
+    except Exception as e:
+        history[-1][1] = f"An error occurred: {str(e)}"
+    finally:
+        # 4. Clean up UI after streaming is finished or stopped
+        stop_flag["stop"] = False
+        yield {
+            stop_btn: gr.update(visible=False),
+            send_btn: gr.update(interactive=True),
+            thinking_indicator: gr.update(visible=False)
+        }
+
+def stop_streaming():
+    """Sets the global flag to stop the streaming."""
+    global stop_flag
+    stop_flag["stop"] = True
 
-    # Show "processing" placeholder in UI
-    history = history + [[message, "Processing..."]]
-    yield history, history  
-
-    aggregated_responses = []
-
-    for run in range(retrieval_runs):
-        if stop_flag["stop"]:
-            history[-1][1] = "⛔ Stopped before completion."
-            yield history, history
-            return
-
-        try:
-            # Stream response but collect full text silently
-            stream = client.chat.completions.create(
-                model="n/a",  # agent handles routing
-                messages=[
-                    {"role": "system", "content": "The data must be returned verbatim. Please be quite detailed and include all information. You are new to the analysis of policy documents, hence you need to be objective in retrieving information, and it is not expected that you will analyse and interpret the information."},
-                    *[{"role": "user", "content": u} if i % 2 == 0 else {"role": "assistant", "content": b}
-                      for i, (u, b) in enumerate(history[:-1])],  # exclude placeholder
-                    {"role": "user", "content": message},
-                ],
-                extra_body={"include_retrieval_info": True},
-                stream=True,
-            )
-
-            response_text = ""
-            for chunk in stream:
-                delta = chunk.choices[0].delta
-                if delta and delta.content:
-                    response_text += delta.content
-
-            aggregated_responses.append(response_text or "⚠️ Empty response")
-
-        except Exception as e:
-            aggregated_responses.append(f"⚠️ Error during run {run+1}: {str(e)}")
-
-    # --- Selection logic ---
-    # For now, pick the "best" as the longest response
-    best_response = max(aggregated_responses, key=len, default="⚠️ No response")
-
-    # Replace "Processing..." with final answer
-    history[-1][1] = best_response
-    yield history, history
-
+def export_chat(history):
+    """Exports the chat history to a formatted text file."""
+    if not history:
+        return None
+        
+    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    filename = f"chat_history_{timestamp}.txt"
+    
+    formatted_chat = ""
+    for user_msg, bot_msg in history:
+        formatted_chat += f"You: {user_msg}\n"
+        formatted_chat += f"Bot: {bot_msg}\n\n"
+        
+    # Add reasoning context to the export if it exists
+    if REASONING_CONTEXT:
+        formatted_chat += "\n" + "="*20 + "\n"
+        formatted_chat += "Last Reasoning Context:\n"
+        formatted_chat += "="*20 + "\n\n"
+        formatted_chat += REASONING_CONTEXT
+
+    # Create a temporary file that Gradio can serve
+    with open(filename, "w", encoding="utf-8") as f:
+        f.write(formatted_chat)
+        
+    return filename
+
+# --- Gradio UI Layout ---
 
 with gr.Blocks(theme=gr.themes.Soft(primary_hue="blue", secondary_hue="gray")) as demo:
-    gr.Markdown("# 🤖 Policy-Agent Chatbot\nAsk me about policies. I’ll query the knowledge base multiple times!")
+    gr.Markdown("# 🤖 Policy-Agent Chatbot\nAsk me about policies. I'll stream responses and show my reasoning.")
 
-    chatbot = gr.Chatbot(height=500)
-    msg = gr.Textbox(placeholder="Type your question...")
-    retrieval_slider = gr.Slider(1, 10, value=DEFAULT_RETRIEVAL_RUNS, step=1, label="Number of retrieval runs")
     with gr.Row():
-        clear = gr.Button("Clear Chat")
-        stop = gr.Button("⛔ Stop")
+        with gr.Column(scale=3):
+            chatbot = gr.Chatbot(height=600, label="Chat Window", show_copy_button=True)
+            
+            thinking_indicator = gr.Markdown("🤔 Thinking...", visible=False)
+            
+            reasoning_accordion = gr.Markdown(
+                "", 
+                visible=False,
+                label="Visualise Reasoning (Retrieved Context)"
+            )
 
-    msg.submit(policy_chat, [msg, chatbot, retrieval_slider], [chatbot, chatbot])
-    msg.submit(lambda: "", None, msg)  # clear textbox
-    clear.click(lambda: None, None, chatbot, queue=False)
-    stop.click(stop_runs, None, chatbot, queue=False)
+        with gr.Column(scale=1):
+            export_btn = gr.Button("Export Chat")
+            export_file = gr.File(label="Download Chat History", interactive=False)
+            
+    with gr.Row():
+        msg = gr.Textbox(
+            placeholder="Type your question and press Enter or click Send...",
+            scale=4,
+            show_label=False
+        )
+        send_btn = gr.Button("Send", scale=1)
+
+    with gr.Row():
+        stop_btn = gr.Button("⛔ Stop Streaming", visible=False)
+        clear_btn = gr.Button("Clear Chat")
+
+    # --- Event Handlers ---
+    
+    # Combine all UI updates into a single list of outputs for chat submission
+    chat_outputs = [chatbot, thinking_indicator, reasoning_accordion, stop_btn, send_btn]
+    
+    msg.submit(policy_chat, [msg, chatbot], chat_outputs)
+    send_btn.click(policy_chat, [msg, chatbot], chat_outputs)
+    
+    # Clear message box after send
+    msg.submit(lambda: "", None, msg, queue=False)
+    send_btn.click(lambda: "", None, msg, queue=False)
+
+    # Button actions
+    stop_btn.click(stop_streaming, None, None, queue=False)
+    clear_btn.click(lambda: ([], "", None), None, [chatbot, reasoning_accordion, export_file], queue=False)
+    export_btn.click(export_chat, chatbot, export_file)
 
 if __name__ == "__main__":
-    demo.launch(debug=True)
+    # NOTE: You may need to configure your OpenAI key via environment variables
+    # For example: os.environ["OPENAI_API_KEY"] = "your_key_here"
+    # openai.api_base = "your_api_base_here_if_needed"
+    demo.launch(debug=True)