updated core_logic, app

app.py

@@ -6,6 +6,10 @@ The Interface Skeleton - The code sets up the navigation panel and the multimoda
 """
 
 
+import gradio as gr
+from core_logic import chat_function
+from storage import save_chat, load_history, get_chat_content
+
 import gradio as gr
 from core_logic import chat_function
 from storage import save_chat, load_history, get_chat_content
@@ -14,52 +18,42 @@ with gr.Blocks() as demo:
     chat_id_state = gr.State("")
     
     with gr.Row():
-        # --- Sidebar ---
         with gr.Column(scale=1, variant="secondary"):
             gr.Markdown("### 🛠️ Silicon Architect")
             new_btn = gr.Button("➕ New Chat", variant="primary")
-            
             history_list = gr.Dataset(
                 components=[gr.Textbox(visible=False)], 
                 label="Recent Conversations", 
                 samples=load_history()
             )
             
-        # --- Main Chat ---
         with gr.Column(scale=4):
-            # FIX: Removed 'multimodal' and 'autoscroll' which caused the TypeError
-            chatbot = gr.Chatbot(
-                show_label=False, 
-                height=700
-            )
-            
-            # The input remains multimodal; the chatbot will display 
-            # whatever the input provides automatically.
+            chatbot = gr.Chatbot(show_label=False, height=700)
             chat_input = gr.MultimodalTextbox(
                 interactive=True,
                 placeholder="Discuss architecture or upload code...",
                 show_label=False
             )
 
-    # --- Event Logic ---
     def bot_response(message, history):
-        # chat_function yields strings; we must return the updated history dict
-        history.append({"role": "user", "content": message["text"]})
-        # Placeholder for assistant message
+        # 1. Add User Message
+        user_content = message["text"]
+        history.append({"role": "user", "content": user_content})
+        
+        # 2. Add empty Assistant Message to be filled
         history.append({"role": "assistant", "content": ""})
         
-        for partial_resp in chat_function(message, history[:-2]):
+        # 3. Stream the response
+        # history[:-1] sends everything EXCEPT the empty assistant slot we just made
+        for partial_resp in chat_function(message, history[:-1]):
             history[-1]["content"] = partial_resp
             yield history
 
+    # Event Handlers
     chat_input.submit(bot_response, [chat_input, chatbot], [chatbot]).then(
         lambda h: save_chat(None, h), [chatbot], None
     )
     
     new_btn.click(lambda: ([], ""), None, [chatbot, chat_id_state])
 
-# Move theme and CSS here for Gradio 6.0
-demo.launch(
-    theme=gr.themes.Soft(),
-    css="styles.css"
-)
+demo.launch(theme=gr.themes.Soft(), css="styles.css")

core_logic.py

@@ -31,33 +31,36 @@ When a user provides files, analyze the code structure and logic before proposin
 """
 
 def chat_function(message, history):
-    # message is now a dict: {"text": "...", "files": [...]}
     user_text = message.get("text", "")
     files = message.get("files", [])
     
-    # Process Files
     context_from_files = ""
     for f in files:
-        # f can be a filepath string or a dict depending on upload status
         path = f["path"] if isinstance(f, dict) else f
         context_from_files += parse_file(path)
     
-    # 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}"
     else:
         prompt = f"FILES:\n{context_from_files}\n\nUSER: {user_text}"
 
-    # Build Gradio 6.0 compliant messages
     messages = [{"role": "system", "content": SYSTEM_PROMPT}]
-    # 'history' is already a list of dicts in Gradio 6
-    messages.extend(history)
+    
+    # Ensure history is in the correct format for the API
+    for turn in history:
+        messages.append({"role": turn["role"], "content": turn["content"]})
+        
     messages.append({"role": "user", "content": prompt})
 
     response_text = ""
-    for chunk in client.chat_completion(messages, max_tokens=2048, stream=True, temperature=0.2):
-        token = chunk.choices[0].delta.content
-        if token:
-            response_text += token
-            yield response_text
+    try:
+        for chunk in client.chat_completion(messages, max_tokens=2048, stream=True, temperature=0.2):
+            # FIX: Check if choices exists and is not empty
+            if hasattr(chunk, 'choices') and len(chunk.choices) > 0:
+                token = chunk.choices[0].delta.content
+                if token:
+                    response_text += token
+                    yield response_text
+    except Exception as e:
+        yield f"Architecture Error: {str(e)}"