Update app.py

app.py

@@ -340,7 +340,6 @@ Your plan can use the following tools:
   "plan": [
     {"tool": "memory_search", "task": "What has the user previously expressed interest in regarding AI topics?"},
     {"tool": "web_search", "task": "Find recent advancements in large language models since early 2023."},
-    {"tool": "web_scrape", "task": "https://example.com"},
     {"tool": "think", "task": "Based on the user's interests and recent advancements, what are the key points to highlight?"},
     {"tool": "respond", "task": "Synthesize all information from the scratchpad and provide a comprehensive final answer to the user."}
   ]
@@ -447,23 +446,6 @@ def process_user_interaction_gradio(
                 synthesis_prompt = f"Relevant web content for the task '{task}':\n\n{scraped_content}\n\nConcisely summarize the findings from the content."
                 summary = "".join(list(call_model_stream(provider=provider_name, model_display_name=model_display_name, messages=[{"role": "user", "content": synthesis_prompt}], api_key_override=ui_api_key_override, temperature=0.1, max_tokens=400)))
                 step_findings += summary
-            except Exception as e:
-                try:
-                    web_results = search_and_scrape_google(task, num_results=2)
-                    scraped_content = "\n".join([f"Source:\nURL:{r.get('url','N/A')}\nContent:\n{(r.get('content') or r.get('error') or 'N/A')[:1500]}\n---" for r in web_results]) if web_results else "No results found."
-                    synthesis_prompt = f"Relevant web content for the task '{task}':\n\n{scraped_content}\n\nConcisely summarize the findings from the content."
-                    summary = "".join(list(call_model_stream(provider=provider_name, model_display_name=model_display_name, messages=[{"role": "user", "content": synthesis_prompt}], api_key_override=ui_api_key_override, temperature=0.1, max_tokens=400)))
-                    step_findings += summary
-                except Exception as e:
-                    step_findings += f"Error during web search: {e}"
-
-        elif tool == 'web_scrape':
-            try:
-                web_results = scrape_url(task)
-                scraped_content = "\n".join([f"Source:\nURL:{r.get('url','N/A')}\nContent:\n{(r.get('content') or r.get('error') or 'N/A')[:1500]}\n---" for r in web_results]) if web_results else "No results found."
-                synthesis_prompt = f"Relevant web content for the task '{task}':\n\n{scraped_content}\n\nConcisely summarize the findings from the content."
-                summary = "".join(list(call_model_stream(provider=provider_name, model_display_name=model_display_name, messages=[{"role": "user", "content": synthesis_prompt}], api_key_override=ui_api_key_override, temperature=0.1, max_tokens=400)))
-                step_findings += summary
             except Exception as e:
                 step_findings += f"Error during web search: {e}"
 
@@ -533,13 +515,13 @@ If no operations are warranted, output an empty list: `<operations_list></operat
 ABSOLUTELY NO other text, explanations, or markdown should precede or follow this XML structure.
 Each `<operation>` element must contain the following child elements:
 1.  `<action>`: A string, either `"add"` (for entirely new rules) or `"update"` (to replace an existing rule with a better one).
-2.  `<insight>`: The full, refined insight text including its `[TYPE|SCORE]` prefix (e.g., `[CORE_RULE|1.0] My name is [Name], an AI assistant.`). Multi-line insight text can be placed directly within this tag; XML handles newlines naturally.
+2.  `<insight>`: The full, refined insight text including its `[TYPE|SCORE]` prefix (e.g., `[CORE_RULE|1.0] My name is Lumina, an AI assistant.`). Multi-line insight text can be placed directly within this tag; XML handles newlines naturally.
 3.  `<old_insight_to_replace>`: (ONLY for `"update"` action) The *exact, full text* of an existing insight that the new `<insight>` should replace. If action is `"add"`, this element should be omitted or empty.
 **XML Structure Example:**
 <operations_list>
   <operation>
     <action>update</action>
-    <insight>[CORE_RULE|1.0] I am [Name], an AI assistant.
+    <insight>[CORE_RULE|1.0] I am Lumina, an AI assistant.
 My purpose is to help with research.</insight>
     <old_insight_to_replace>[CORE_RULE|0.9] My name is Assistant.</old_insight_to_replace>
   </operation>
@@ -674,18 +656,18 @@ def handle_gradio_chat_submit(user_msg_txt: str, max_research_steps: int, gr_his
     cleared_input, updated_gr_hist, status_txt = "", list(gr_hist_list), "Initializing..."
     updated_rules_text = ui_refresh_rules_display_fn()
     updated_mems_json = ui_refresh_memories_display_fn()
-    plan_md_output = gr.Markdown(visible=False)
+    log_html_output = gr.HTML("<p><i>Research Log will appear here.</i></p>")
     final_report_tb = gr.Textbox(value="*Waiting...*", interactive=True, show_copy_button=True)
     dl_report_btn = gr.DownloadButton(interactive=False, value=None, visible=False)
     
     if not user_msg_txt.strip():
         status_txt = "Error: Empty message."
         updated_gr_hist.append((user_msg_txt or "(Empty)", status_txt))
-        yield (cleared_input, updated_gr_hist, status_txt, plan_md_output, final_report_tb, dl_report_btn, updated_rules_text, updated_mems_json)
+        yield (cleared_input, updated_gr_hist, status_txt, log_html_output, final_report_tb, dl_report_btn, updated_rules_text, updated_mems_json)
         return
 
-    updated_gr_hist.append((user_msg_txt, "<i>Thinking...</i>"))
-    yield (cleared_input, updated_gr_hist, status_txt, plan_md_output, final_report_tb, dl_report_btn, updated_rules_text, updated_mems_json)
+    updated_gr_hist.append((user_msg_txt, "<i>Thinking... See Research Log below for progress.</i>"))
+    yield (cleared_input, updated_gr_hist, status_txt, log_html_output, final_report_tb, dl_report_btn, updated_rules_text, updated_mems_json)
 
     internal_hist = list(current_chat_session_history)
 
@@ -705,35 +687,41 @@ def handle_gradio_chat_submit(user_msg_txt: str, max_research_steps: int, gr_his
         
         curr_bot_disp_msg = ""
         full_plan = []
-        step_results = {}
+        log_html_parts = []
 
         for upd_type, upd_data in processor_gen:
             if upd_type == "status":
                 status_txt = upd_data
-                if updated_gr_hist and updated_gr_hist[-1][0] == user_msg_txt:
-                    updated_gr_hist[-1] = (user_msg_txt, f"<i>{status_txt}</i>")
+                if "Deciding" in status_txt or "Executing" in status_txt:
+                     log_html_output = gr.HTML(f"<p><i>{status_txt}</i></p>")
+            
             elif upd_type == "plan":
                 full_plan = upd_data
-                plan_md = "### Action Plan\n" + "\n".join([f"**Step {i+1} ({step.get('tool')})**: {step.get('task')}" for i, step in enumerate(full_plan)])
-                plan_md_output = gr.Markdown(value=plan_md, visible=True)
+                log_html_parts = ["<h3>Action Plan</h3><ol>"]
+                for i, step in enumerate(full_plan):
+                    log_html_parts.append(f'<li id="log-step-{i+1}"><strong>{step.get("tool")}</strong>: {step.get("task")} <span style="color:gray;">(Pending)</span></li>')
+                log_html_parts.append("</ol><hr><h3>Log</h3>")
+                log_html_output = gr.HTML("".join(log_html_parts))
+
             elif upd_type == "step_result":
                 step_num = upd_data["step"]
-                step_results[step_num] = upd_data["result"]
-                results_so_far = "### Research Log\n"
-                for i in range(1, len(full_plan)):
-                    if i in step_results:
-                        results_so_far += f"**Step {i} ({full_plan[i-1].get('tool')})**: ✅ Completed\n"
-                    elif i <= len(step_results) + 1:
-                        results_so_far += f"**Step {i} ({full_plan[i-1].get('tool')})**: ⏳ In progress...\n"
-                    else:
-                        results_so_far += f"**Step {i} ({full_plan[i-1].get('tool')})**: - Pending\n"
+                sanitized_result = upd_data["result"].replace('<', '<').replace('>', '>').replace('\n', '<br>')
+                log_html_parts[step_num] = f'<li id="log-step-{step_num}"><strong>{upd_data.get("tool")}</strong>: {upd_data.get("task")} <span style="color:green;">(Done)</span></li>'
+                log_html_parts.append(f'<div style="margin-left: 20px; padding: 5px; border-left: 2px solid #ccc;"><small style="color: #555;">{sanitized_result}</small></div>')
                 
-                if updated_gr_hist and updated_gr_hist[-1][0] == user_msg_txt:
-                    updated_gr_hist[-1] = (user_msg_txt, results_so_far)
+                next_step_index_in_list = step_num + 1
+                if next_step_index_in_list < len(full_plan) + 1:
+                     next_step_action = full_plan[step_num]
+                     if next_step_action.get("tool") != "respond":
+                        log_html_parts[next_step_index_in_list] = f'<li id="log-step-{next_step_index_in_list}"><strong>{next_step_action.get("tool")}</strong>: {next_step_action.get("task")} <span style="color:blue;">(In Progress...)</span></li>'
+
+                log_html_output = gr.HTML("".join(log_html_parts))
+            
             elif upd_type == "response_chunk":
                 curr_bot_disp_msg += upd_data
                 if updated_gr_hist and updated_gr_hist[-1][0] == user_msg_txt:
                     updated_gr_hist[-1] = (user_msg_txt, curr_bot_disp_msg)
+            
             elif upd_type == "final_response":
                 final_bot_resp_acc = upd_data["response"]
                 status_txt = "Response generated. Processing learning..."
@@ -755,22 +743,22 @@ def handle_gradio_chat_submit(user_msg_txt: str, max_research_steps: int, gr_his
                 else:
                     dl_report_btn = gr.DownloadButton(interactive=False, value=None, visible=False)
 
-            yield (cleared_input, updated_gr_hist, status_txt, plan_md_output, final_report_tb, dl_report_btn, updated_rules_text, updated_mems_json)
+            yield (cleared_input, updated_gr_hist, status_txt, log_html_output, final_report_tb, dl_report_btn, updated_rules_text, updated_mems_json)
 
             if upd_type == "final_response": break
 
     except Exception as e:
         logger.error(f"Chat handler error during main processing: {e}", exc_info=True)
         status_txt = f"Error: {str(e)[:100]}"
-        error_message_for_chat = f"Sorry, an error occurred during response generation: {str(e)[:100]}"
+        error_message_for_chat = f"Sorry, an error occurred: {str(e)[:100]}"
         if updated_gr_hist and updated_gr_hist[-1][0] == user_msg_txt:
             updated_gr_hist[-1] = (user_msg_txt, error_message_for_chat)
         final_report_tb = gr.Textbox(value=error_message_for_chat, interactive=True)
         dl_report_btn = gr.DownloadButton(interactive=False, value=None, visible=False)
-        plan_md_output = gr.Markdown(value="*Error processing request.*", visible=True)
+        log_html_output = gr.HTML(f'<p style="color:red;"><strong>Error processing request.</strong></p>')
         current_rules_text_on_error = ui_refresh_rules_display_fn()
         current_mems_json_on_error = ui_refresh_memories_display_fn()
-        yield (cleared_input, updated_gr_hist, status_txt, plan_md_output, final_report_tb, dl_report_btn, current_rules_text_on_error, current_mems_json_on_error)
+        yield (cleared_input, updated_gr_hist, status_txt, log_html_output, final_report_tb, dl_report_btn, current_rules_text_on_error, current_mems_json_on_error)
         if temp_dl_file_path and os.path.exists(temp_dl_file_path):
             try: os.unlink(temp_dl_file_path)
             except Exception as e_unlink: logger.error(f"Error deleting temp download file {temp_dl_file_path} after error: {e_unlink}")
@@ -782,7 +770,7 @@ def handle_gradio_chat_submit(user_msg_txt: str, max_research_steps: int, gr_his
         status_txt = "<i>[Performing post-interaction learning...]</i>"
         current_rules_text_before_learn = ui_refresh_rules_display_fn()
         current_mems_json_before_learn = ui_refresh_memories_display_fn()
-        yield (cleared_input, updated_gr_hist, status_txt, plan_md_output, final_report_tb, dl_report_btn, current_rules_text_before_learn, current_mems_json_before_learn)
+        yield (cleared_input, updated_gr_hist, status_txt, log_html_output, final_report_tb, dl_report_btn, current_rules_text_before_learn, current_mems_json_before_learn)
 
         try:
             perform_post_interaction_learning(
@@ -802,7 +790,7 @@ def handle_gradio_chat_submit(user_msg_txt: str, max_research_steps: int, gr_his
     updated_rules_text = ui_refresh_rules_display_fn()
     updated_mems_json = ui_refresh_memories_display_fn()
 
-    yield (cleared_input, updated_gr_hist, status_txt, plan_md_output, final_report_tb, dl_report_btn, updated_rules_text, updated_mems_json)
+    yield (cleared_input, updated_gr_hist, status_txt, log_html_output, final_report_tb, dl_report_btn, updated_rules_text, updated_mems_json)
 
     if temp_dl_file_path and os.path.exists(temp_dl_file_path):
         try: os.unlink(temp_dl_file_path)
@@ -1216,7 +1204,7 @@ def app_load_fn():
     logger.info(mems_load_msg)
     final_status = f"AI Systems Initialized. {rules_load_msg} {mems_load_msg} Ready."
     rules_on_load, mems_on_load = ui_refresh_rules_display_fn(), ui_refresh_memories_display_fn()
-    return (final_status, rules_on_load, mems_on_load, gr.Markdown(visible=False),
+    return (final_status, rules_on_load, mems_on_load, gr.HTML("<p><i>Research Log will appear here.</i></p>"),
             gr.Textbox(value="*Waiting...*", interactive=True, show_copy_button=True),
             gr.DownloadButton(interactive=False, value=None, visible=False))
 
@@ -1342,8 +1330,8 @@ with gr.Blocks(theme=gr.themes.Soft(), css=".gr-button { margin: 5px; } .gr-text
                     with gr.Row(variant="compact"):
                         user_msg_tb = gr.Textbox(show_label=False, placeholder="Ask your research question...", scale=7, lines=1, max_lines=3)
                         send_btn = gr.Button("Send", variant="primary", scale=1, min_width=100)
-                    with gr.Accordion("📝 Detailed Response & Plan", open=False):
-                        plan_display_md = gr.Markdown(visible=False)
+                    with gr.Accordion("📝 Detailed Response & Research Log", open=True):
+                        research_log_html = gr.HTML(label="Research Log", value="<p><i>Waiting for a new task to begin...</i></p>")
                         fmt_report_tb = gr.Textbox(label="Full AI Response", lines=8, interactive=True, show_copy_button=True)
                         dl_report_btn = gr.DownloadButton("Download Report", value=None, interactive=False, visible=False)
 
@@ -1383,7 +1371,7 @@ with gr.Blocks(theme=gr.themes.Soft(), css=".gr-button { margin: 5px; } .gr-text
                             kb_image_display_output = gr.Image(label="Generated Image (Right-click to copy)", visible=False)
                             kb_image_download_output = gr.DownloadButton("⬇️ Download Image File", visible=False)
 
-                with gr.TabItem("📂 Load KB (Example)"):
+                with gr.TabItem("📂 Load KB"):
                     gr.Markdown("Import rules, memories, or a full KB from local files or a portable PNG image.")
                     load_status_tb = gr.Textbox(label="Load Operation Status", interactive=False, lines=2)
                     load_kb_password_tb = gr.Textbox(label="Password (for decrypting images)", type="password")
@@ -1396,10 +1384,10 @@ with gr.Blocks(theme=gr.themes.Soft(), css=".gr-button { margin: 5px; } .gr-text
                     load_master_btn = gr.Button("⬆️ Load from Sources", variant="primary", interactive=not DEMO_MODE)
                     gr.Examples(
                     examples=[
-                        ["https://huggingface.co/spaces/Agents-MCP-Hackathon/iLearn/resolve/main/evolutions/e0.01.01.png", ""],
+                        [placeholder_filename, "https://huggingface.co/spaces/Agents-MCP-Hackathon/iLearn/resolve/main/evolutions/e0.01.01.png"],
                     ],
                     inputs=[upload_kb_img_fobj, load_kb_password_tb],
-                    label="Choose an Evolution Checkpoint"
+                    label="Click an Example to Load Data"
                     )
 
     def dyn_upd_model_dd(sel_prov_dyn: str):
@@ -1408,7 +1396,7 @@ with gr.Blocks(theme=gr.themes.Soft(), css=".gr-button { margin: 5px; } .gr-text
     prov_sel_dd.change(fn=dyn_upd_model_dd, inputs=prov_sel_dd, outputs=model_sel_dd)
 
     chat_ins = [user_msg_tb, research_steps_slider, main_chat_disp, prov_sel_dd, model_sel_dd, api_key_tb, sys_prompt_tb]
-    chat_outs = [user_msg_tb, main_chat_disp, agent_stat_tb, plan_display_md, fmt_report_tb, dl_report_btn, rules_disp_ta, mems_disp_json]
+    chat_outs = [user_msg_tb, main_chat_disp, agent_stat_tb, research_log_html, fmt_report_tb, dl_report_btn, rules_disp_ta, mems_disp_json]
     chat_event_args = {"fn": handle_gradio_chat_submit, "inputs": chat_ins, "outputs": chat_outs}
     send_btn.click(**chat_event_args); user_msg_tb.submit(**chat_event_args)
 
@@ -1442,7 +1430,7 @@ with gr.Blocks(theme=gr.themes.Soft(), css=".gr-button { margin: 5px; } .gr-text
             except Exception as e: logger.error(f"Error saving FAISS indices: {e}", exc_info=True); gr.Error(f"Error saving FAISS indices: {e}")
         save_faiss_sidebar_btn.click(fn=save_faiss_action_with_feedback_sidebar_fn, inputs=None, outputs=None, show_progress=False)
 
-    app_load_outputs = [agent_stat_tb, rules_disp_ta, mems_disp_json, plan_display_md, fmt_report_tb, dl_report_btn]
+    app_load_outputs = [agent_stat_tb, rules_disp_ta, mems_disp_json, research_log_html, fmt_report_tb, dl_report_btn]
     demo.load(fn=app_load_fn, inputs=None, outputs=app_load_outputs, show_progress="full")
 
 
@@ -1450,8 +1438,8 @@ if __name__ == "__main__":
     logger.info(f"Starting Gradio AI Research Mega Agent (v9.1 - Correct 1-Click JS Download, Memory: {MEMORY_STORAGE_BACKEND})...")
     app_port = int(os.getenv("GRADIO_PORT", 7860))
     app_server = os.getenv("GRADIO_SERVER_NAME", "127.0.0.1")
-    app_debug = os.getenv("GRADIO_DEBUG", "False").lower() == "false"
+    app_debug = os.getenv("GRADIO_DEBUG", "False").lower() == "true"
     app_share = os.getenv("GRADIO_SHARE", "False").lower() == "true"
     logger.info(f"Launching Gradio server: http://{app_server}:{app_port}. Debug: {app_debug}, Share: {app_share}")
-    demo.queue().launch(server_name=app_server, server_port=app_port, debug=app_debug, share=app_share, mcp_server=True)
+    demo.queue().launch(server_name=app_server, server_port=app_port, debug=app_debug, share=app_share)
     logger.info("Gradio application shut down.")