updated paste functions and web search

app.py

@@ -23,6 +23,9 @@ with gr.Blocks() as demo:
     # This state keeps track of the filename for the current session
     chat_id_state = gr.State("")
     
+    # Hidden state tracking variable to maintain files across the chat session
+    staged_files_state = gr.State([])
+
     with gr.Row():
         # --- Left Panel: Sidebar History ---
         with gr.Column(scale=1, variant="secondary"):
@@ -64,7 +67,8 @@ with gr.Blocks() as demo:
                 scale=8
             )
             
-            # 2. Provide a distinct, clear upload node that connects to your perception agent pipeline
+            # 2. Wire Up the Component Handlers - 2. Provide a distinct, clear upload node that connects to your perception agent pipeline
+            # Triggered immediately when a user finishes choosing files in the file browser window
             upload_btn = gr.UploadButton(
                 "📎 Attach Documents/Images",
                 file_count="multiple",
@@ -72,6 +76,9 @@ with gr.Blocks() as demo:
                 scale=2
             )
 
+            # Visual text tracker showing exactly what files have successfully staged
+            upload_status = gr.Markdown("")
+
         # --- Right Panel: Agentic Control Tower ---
         with gr.Column(scale=1, variant="secondary"):
             gr.Markdown("### 🚀 CoderG Authorization Core")
@@ -98,6 +105,23 @@ with gr.Blocks() as demo:
 
     # --- LOGIC FUNCTIONS ---
 
+    # --- 1: Create a staging function to process files when uploaded ---
+    def handle_file_upload(uploaded_files, current_staged_files):
+        """Processes files when selected via browser and appends to staging session state."""
+        if not current_staged_files:
+            current_staged_files = []
+        
+        # Gradio returns a list of file objects when file_count="multiple"
+        for file_obj in uploaded_files:
+            # Check the property format of incoming file assets from Gradio 6
+            file_path = file_obj.name if hasattr(file_obj, 'name') else file_obj
+            if file_path and file_path not in current_staged_files:
+                current_staged_files.append(file_path)
+        
+        # Return a visual message showing how many files are safely staged
+        status_msg = f"🟢 **{len(current_staged_files)} file(s) staged successfully and attached to next prompt.**"
+        return current_staged_files, status_msg
+
     def bot_response(message, history, chat_id):
         user_content = message["text"]
         
@@ -144,6 +168,63 @@ with gr.Blocks() as demo:
 
     # --- INTERACTION ARCHITECTURE / EVENT HANDLERS ---
 
+    # Hook the UploadButton event listener loop to stage chosen files right away
+    upload_btn.upload(
+        fn=handle_file_upload,
+        inputs=[upload_btn, staged_files_state],
+        outputs=[staged_files_state, upload_status]
+    )
+
+    # Submission wrapper to package parameters together for your multi-format engine
+    # When submitting, we pass BOTH the text and the hidden staged files state array!
+    def process_submission(message_text, current_staged_files, history, chat_id):
+        if not message_text.strip() and not current_staged_files:
+            return history, "", current_staged_files, ""
+            
+        # Packaging matching the identical format of core_logic expectations
+        payload = {
+            "text": message_text,
+            "files": current_staged_files
+        }
+        
+        # Stream responses through bot loops and clear inputs when complete
+        for updated_history in bot_response(payload, history, chat_id):
+            # Continuously yield state update frames, wiping values upon initial loop entry
+            yield updated_history, "", [], ""
+
+    # 1. Bind Enter/Submit behavior for the chat input text box
+    chat_input.submit(
+        fn=process_submission, 
+        inputs=[chat_input, staged_files_state, chatbot, chat_id_state], 
+        outputs=[chatbot, chat_input, staged_files_state, upload_status]
+    ).then(
+        fn=handle_save, 
+        inputs=[chatbot, chat_id_state], 
+        outputs=[chat_id_state, history_list]
+    )
+    
+    # 2. Click Sidebar Item -> Load Content
+    history_list.click(
+        fn=load_past_chat, 
+        inputs=[history_list], 
+        outputs=[chatbot, chat_id_state]
+    )
+
+    # 3. New Chat Button Initialization
+    new_btn.click(
+        fn=lambda: ([], "", [], load_history(), "", "_Awaiting local environment staging completion..._"), 
+        inputs=None, 
+        outputs=[chatbot, chat_id_state, staged_files_state, history_list, upload_status, output_log]
+    )
+
+    # 4. Bind the Control Tower Approve Action button
+    approve_btn.click(
+        fn=push_authorized, 
+        inputs=[target_repo, commit_txt, staged_files], 
+        outputs=[output_log]
+    )
+
+    """
     # 1. Submit Chat -> Stream Response -> Save -> Refresh Sidebar
     chat_input.submit(
         bot_response, 
@@ -175,6 +256,6 @@ with gr.Blocks() as demo:
         [target_repo, commit_txt, staged_files], 
         [output_log]
     )
-
+    """
 # Fully consolidated theme and styling injections down into launch() parameter fields
 demo.launch(theme=gr.themes.Soft(), css="styles.css")

core_logic.py

@@ -104,8 +104,22 @@ def chat_function(message, history):
 
     # 2. Research Trigger
     if any(keyword in user_text.lower() for keyword in ["search", "docs", "latest"]):
-        research_context = web_search(user_text)
-        prompt = f"RESEARCH:\n{research_context}\n\nFILES:\n{context_from_files}\n\nUSER: {user_text}"
+        # Use a fast micro-turn to distill the massive user prompt into optimized keywords
+        distill_response = client.chat.completions.create(
+            model="llama-3.1-8b-instant",
+            messages=[
+                {"role": "system", "content": "You are a search query optimizer. Convert the user's long request into a single, clean search query for a search engine. Output ONLY the search keywords, under 60 characters total. Do not use quotes, markdown, or punctuation."},
+                {"role": "user", "content": user_text}
+            ],
+            temperature=0.0,
+        )
+        optimized_query = distill_response.choices[0].message.content.strip()
+    
+        # Executing clean, highly target web search under the 400-character cap
+        research_context = web_search(optimized_query)
+        prompt = f"RESEARCH:\n{research_context}\n\nFILES:\n{context_from_files}\n\nUSER: {optimized_query}"
+        #research_context = web_search(user_text)
+        #prompt = f"RESEARCH:\n{research_context}\n\nFILES:\n{context_from_files}\n\nUSER: {user_text}"
     else:
         prompt = f"FILES:\n{context_from_files}\n\nUSER: {user_text}"
 
@@ -115,7 +129,7 @@ def chat_function(message, history):
     # ONLY KEEP LAST 3 TURNS: This is the 'Master Stroke' for staying under 6k TPM
     for turn in history[-3:]:
         messages.append({"role": turn["role"], "content": turn["content"]})
-        
+
     messages.append({"role": "user", "content": prompt})
 
     # Spliced directly into ./core_logic.py at the bottom of chat_function()