Configure app for Hugging Face deployment

app_gradio.py

@@ -1,4 +1,4 @@
-# app_gradio.py (Final Version with Streaming Status)
+# app_gradio.py (Final Version)
 
 import gradio as gr
 import math
@@ -38,7 +38,6 @@ def get_default_state():
 
 # --- Conversation Management ---
 def save_conversation(history, state):
-    """Saves the current chat history to a timestamped JSON file."""
     if not history:
         return "Nothing to save.", gr.update()
     
@@ -54,7 +53,6 @@ def save_conversation(history, state):
     return f"Saved to {filename}", gr.update(choices=get_saved_conversations())
 
 def load_conversation(filepath, state):
-    """Loads a chat history from a JSON file."""
     if not filepath:
         return [], state
     
@@ -66,30 +64,23 @@ def load_conversation(filepath, state):
     return history, state
 
 def get_saved_conversations():
-    """Returns a list of saved conversation file paths."""
     return [os.path.join("conversations", f) for f in os.listdir("conversations") if f.endswith(".json")]
 
 def clear_conversation():
-    """Resets the chat to a new, empty state."""
     return [], get_default_state(), "Conversation cleared.", "No artifacts generated yet."
 
 # --- Artifact Management ---
 def handle_file_upload(file):
-    """Saves an uploaded file to the /uploads directory."""
     if file is None:
         return "No file uploaded."
     
     basename = os.path.basename(file.name)
     destination_path = os.path.join("uploads", basename)
-    
-    # Gradio's File component creates a temp file, so we move it
     os.rename(file.name, destination_path)
-    
     log.info(f"File uploaded to {destination_path}")
     return f"Uploaded: {basename}"
 
 def update_artifact_list():
-    """Scans the outputs directory and returns a list of files."""
     output_files = os.listdir("outputs")
     if not output_files:
         return "No artifacts generated yet."
@@ -97,7 +88,6 @@ def update_artifact_list():
     markdown_list = "### Generated Artifacts:\n"
     for f in output_files:
         markdown_list += f"- `{f}` (in your 'outputs' folder)\n"
-        
     return markdown_list
 
 # --- Core Logic Functions ---
@@ -106,7 +96,6 @@ def start_estimation(message, history, state):
     state["original_user_message"] = message
     history.append({"role": "user", "content": message})
     
-    # Immediately show user message and disable input
     yield history, state, gr.update(value="", interactive=False), gr.update(visible=False), gr.update(), "Analyzing request..."
 
     triage_inputs = {"userInput": message}
@@ -134,10 +123,8 @@ def start_estimation(message, history, state):
 def execute_main_task(history, state, budget):
     log.info(f"Executing main task with budget: ${budget}")
     
-    # Hide approval box and show initial status
     yield history, state, gr.update(visible=False), gr.update(value="", interactive=False), "Task approved. Starting execution..."
 
-    # Add a placeholder for the assistant's response
     history.append({"role": "assistant", "content": "..."})
     
     cost_per_loop = state["estimate"].get('cost_per_loop_usd', 0.05)
@@ -155,27 +142,19 @@ def execute_main_task(history, state, budget):
     }
     
     final_response = "An error occurred during execution."
-    # Use .stream() to get live updates
     for step in main_app.stream(initial_state):
-        # The key will be the name of the node that just ran
         node_name = list(step.keys())[0]
         node_output = step[node_name]
         
-        # Update the status display with the latest message
         status = node_output.get("status_update", "Processing...")
         yield history, state, gr.update(visible=False), gr.update(interactive=False), status
 
-        # Keep track of the final response
         if "draftResponse" in node_output and node_output["draftResponse"]:
             final_response = node_output["draftResponse"]
 
-    # Update the final message in the chat history
     history[-1] = {"role": "assistant", "content": final_response}
-    
-    # Final yield to re-enable input and hide status
     yield history, state, gr.update(visible=False), gr.update(interactive=True), "Task complete. Ready."
 
-
 def cancel_task(history, state):
     history.append({"role": "assistant", "content": "Task cancelled."})
     return history, state, gr.update(visible=False), gr.update(interactive=True), "Task cancelled. Ready."
@@ -190,25 +169,19 @@ with gr.Blocks(theme=gr.themes.Soft(), title="Autonomous AI Lab") as demo:
         with gr.Column(scale=1):
             gr.Markdown("### Controls")
             clear_btn = gr.Button("🗑️ New Conversation")
-            
             gr.Markdown("### Saved Chats")
             saved_chats_dropdown = gr.Dropdown(label="Load a past conversation", choices=get_saved_conversations(), interactive=True)
             save_chat_btn = gr.Button("💾 Save Current Chat")
             save_status_text = gr.Textbox(label="Save Status", interactive=False)
-            
             gr.Markdown("### Artifacts")
             file_uploader = gr.File(label="Upload a file")
             upload_status_text = gr.Textbox(label="Upload Status", interactive=False)
-            
             refresh_artifacts_btn = gr.Button("🔄 Refresh Artifacts List")
             artifact_list_display = gr.Markdown("No artifacts generated yet.")
 
         with gr.Column(scale=4):
             chatbot = gr.Chatbot(label="Conversation", height=600, avatar_images=(None, "https://i.imgur.com/b5OqI32.png"))
-            
-            # NEW: Status display
-            status_display = gr.Markdown("Status: Ready.", elem_classes="status-display")
-
+            status_display = gr.Markdown("Status: Ready.")
             with gr.Group(visible=False) as approval_box:
                 approval_text = gr.Markdown()
                 with gr.Row():
@@ -222,7 +195,6 @@ with gr.Blocks(theme=gr.themes.Soft(), title="Autonomous AI Lab") as demo:
                 submit_btn = gr.Button("Send", variant="primary", scale=1)
 
     # --- Event Handlers ---
-    # Define all outputs for streaming functions
     estimation_outputs = [chatbot, state, msg_textbox, approval_box, budget_input, status_display]
     execution_outputs = [chatbot, state, approval_box, msg_textbox, status_display]
     cancel_outputs = [chatbot, state, approval_box, msg_textbox, status_display]

graph.py

@@ -1,11 +1,11 @@
-# graph.py (Final)
+# graph.py (Final Version)
 
 import json
 import re
 import math
 from typing import TypedDict, List, Dict, Optional
 from langchain_openai import ChatOpenAI
-from langgraph.graph import StateGraph, END, START
+from langgraph.graph import StateGraph, END
 from memory_manager import memory_manager
 from code_executor import execute_python_code
 from logging_config import setup_logging, get_logger
@@ -52,7 +52,7 @@ def parse_json_from_llm(llm_output: str) -> Optional[dict]:
 # --- LLM Initialization ---
 llm = ChatOpenAI(model="gpt-4o", temperature=0.1, max_retries=3, request_timeout=60)
 
-# --- Agent Node Functions (Updated with Status Messages) ---
+# --- Agent Node Functions ---
 def run_triage_agent(state: AgentState):
     log.info("---  triage ---")
     prompt = f"Analyze the user input. Is it a simple conversational greeting or a task? Respond with 'greeting' or 'task'.\n\nUser Input: \"{state['userInput']}\""