Create App.py

App.py

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+import gradio as gr
+import os
+import subprocess
+import json
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+import torch
+import time
+
+# --- Configuration ---
+MODEL_ID = "mistralai/Mistral-7B-Instruct-v0.2"
+FINETUNED_MODEL_PATH = "./buddha-v2-finetuned"
+SCHEMA_FILE = "master_builder_schema.json"
+
+# --- Model & Tokenizer Initialization (Placeholder for Quick Load) ---
+# We use a placeholder function to avoid loading the massive model immediately on startup
+# which often causes timeouts in Gradio. The real loading happens on the first interaction.
+model = None
+tokenizer = None
+master_builder_schema = {}
+model_loaded = False
+is_finetuned = os.path.exists(FINETUNED_MODEL_PATH)
+
+def load_model():
+    global model, tokenizer, master_builder_schema, model_loaded
+
+    if model_loaded:
+        return
+
+    # Load the Master Builder Schema
+    try:
+        with open(SCHEMA_FILE, "r") as f:
+            master_builder_schema.update(json.load(f))
+    except FileNotFoundError:
+        print(f"Warning: {SCHEMA_FILE} not found.")
+
+    # Determine which model to load
+    load_path = FINETUNED_MODEL_PATH if is_finetuned else MODEL_ID
+    
+    print(f"Loading model from: {load_path}")
+    
+    # Configuration for 4-bit quantization
+    bnb_config = BitsAndBytesConfig(
+        load_in_4bit=True,
+        bnb_4bit_use_double_quant=True,
+        bnb_4bit_quant_type="nf4",
+        bnb_4bit_compute_dtype=torch.bfloat16
+    )
+    
+    try:
+        tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
+        # Load the model with 4-bit config, even if loading from local path
+        model = AutoModelForCausalLM.from_pretrained(
+            load_path,
+            quantization_config=bnb_config,
+            device_map="auto"
+        )
+        model_loaded = True
+        print("Model loading complete.")
+    except Exception as e:
+        print(f"CRITICAL ERROR loading model: {e}")
+        # Return False to signal failure
+        return False
+    
+    return True
+
+# --- Fine-Tuning Status Function ---
+def check_status():
+    global is_finetuned
+    
+    if os.path.exists(FINETUNED_MODEL_PATH):
+        is_finetuned = True
+        return gr.Markdown(
+            f"""
+            ## ✅ Fine-Tuning Complete!
+            The fine-tuned model is available at: `{FINETUNED_MODEL_PATH}`
+            The main chat interface is now powered by the Master Builder V2 Agent!
+            """
+        )
+    
+    # Check for the log file or simply indicate training is required
+    return gr.Markdown(
+        """
+        ## ⏳ Training Required (Torch Error Fix Pending)
+        The fine-tuning process has not finished yet, or the job hasn't been started.
+
+        **CRITICAL STEP:** You must run the installation and training commands in the **JupyterLab Terminal**:
+
+        1.  **Install Libraries (Fixes the 'torch' error):**
+            `pip install -r requirements.txt`
+        2.  **Launch Fine-Tuning:**
+            `/usr/bin/python3 finetune_v2.py`
+        """
+    )
+
+
+# --- Core Response Function ---
+def respond_and_generate_app(message, history, app_request_box):
+    if not model_loaded:
+        if not load_model():
+             # If model failed to load (e.g., due to missing torch), give a manual instruction
+             yield history, f"🔴 **ERROR:** Cannot load model (likely missing **torch**). Please run `pip install -r requirements.txt` in the **JupyterLab Terminal** first."
+             return
+
+    # --- Tool Call Simulation (Before Fine-Tuning) ---
+    if "generate app" in message.lower() or "build" in message.lower():
+        # This is a placeholder response
+        placeholder_app = f"""
+        <!-- HTML Code for {message} -->
+        <script src="https://cdn.tailwindcss.com"></script>
+        <div class="p-8 max-w-lg mx-auto bg-gray-100 rounded-xl shadow-lg m-4">
+            <h2 class="text-2xl font-bold text-indigo-600 mb-4">Master Builder Status</h2>
+            <p class="text-gray-700">The Master Builder is currently **running the fine-tuning job** in the background. Once the job is finished, I will be able to generate the full, runnable application for your request: 
+            <i class="font-semibold">'{message}'</i></p>
+            <div class="mt-4 p-3 bg-indigo-100 rounded">
+                <p class="text-sm font-mono text-indigo-800">Final Step: Run `pip install -r requirements.txt` and then `/usr/bin/python3 finetune_v2.py` in the **JupyterLab Terminal**.</p>
+            </div>
+        </div>
+        """
+        app_request_box = gr.HTML(placeholder_app)
+        new_message = "Master Builder received request. Training job status updated."
+        
+    else:
+        # Standard chat response
+        new_message = f"Hello! I am Buddha-v2. I am currently focused on becoming the Master Builder Agent. You asked: '{message}'. To generate a web application, ask me to 'Generate an app for...' or 'Build me a website for...'. Check the **Status Tab** for my fine-tuning progress!"
+        app_request_box = gr.HTML("<h2>Awaiting App Generation Request...</h2>")
+
+    # Update history with the response
+    history = history + [[message, new_message]]
+    
+    # This uses a yield loop to simulate streaming/updating the app panel
+    yield history, app_request_box
+
+# --- Gradio Interface Layout ---
+
+# Custom CSS for a better look
+css = """
+.gradio-container {
+    background: linear-gradient(135deg, #1e3a8a, #374151); /* Dark blue to dark grey */
+    color: white;
+}
+h1, h2, .label {
+    color: #a5b4fc !important; /* Light blue/purple */
+}
+.chat-message {
+    border-radius: 12px;
+}
+.user-message {
+    background-color: #3b82f6 !important; /* Blue for user */
+    color: white !important;
+}
+.bot-message {
+    background-color: #1f2937 !important; /* Darker grey for bot */
+    color: #e5e7eb !important;
+}
+"""
+
+with gr.Blocks(title="Buddha-v2 Master Builder", css=css, theme=gr.themes.Soft()) as demo:
+    gr.HTML("<h1 class='text-center text-4xl py-4 font-bold' style='color: #818cf8;'>Buddha-v2: The Master Manifestor</h1>")
+    
+    # Box to display the generated HTML App
+    app_output = gr.HTML(
+        value="<h2>Awaiting App Generation Request...</h2>", 
+        label="Generated Application Preview", 
+        elem_id="app-output-box"
+    )
+
+    with gr.Tabs():
+        
+        with gr.TabItem("💬 Master Builder Chat"):
+            chatbot = gr.Chatbot(
+                height=400,
+                label="Buddha-v2 Conversation"
+            )
+            
+            msg = gr.Textbox(
+                placeholder="Ask Buddha-v2 a question, or request a web app (e.g., 'Generate an app for tracking expenses').",
+                container=False, 
+                scale=7
+            )
+            
+            clear = gr.Button("Clear")
+
+            # Setup submit actions
+            msg.submit(respond_and_generate_app, [msg, chatbot, app_output], [chatbot, app_output])
+            clear.click(lambda: (None, gr.HTML("<h2>Awaiting App Generation Request...</h2>")), None, [chatbot, app_output])
+            
+            gr.Examples(
+                examples=[
+                    ["What is the Master Builder Agent?"],
+                    ["Generate an app for calculating BMI."],
+                    ["Build me a simple portfolio website."],
+                ],
+                inputs=msg
+            )
+            
+        with gr.TabItem("⚙️ Fine-Tuning Status"):
+            status_output = gr.Markdown(check_status().value)
+            refresh_button = gr.Button("Refresh Status")
+            refresh_button.click(check_status, None, status_output)
+            
+            gr.Markdown(
+                """
+                ### About Master Builder
+                The Buddha-v2 agent is being fine-tuned on the Mistral 7B model to become a **Master Manifestor**. 
+                Its core skill is converting structured data and user requests into beautiful, **single-file, full-stack web applications** (HTML/CSS/JS or React/Angular) using a special instruction set.
+                """
+            )
+            
+        
+demo.launch(share=False)