gradio_app_new.py

import os
import logging
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
import gradio as gr
import json
import re

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class ModelManager:
    def __init__(self):
        self.model = None
        self.tokenizer = None
        self.device = None
        self.model_loaded = False
        self.load_model()

    def load_model(self):
        """Load the model and tokenizer"""
        try:
            logger.info("Starting model loading...")
            
            # Check if CUDA is available
            if torch.cuda.is_available():
                torch.cuda.set_device(0)
                self.device = "cuda:0"
            else:
                self.device = "cpu"
            logger.info(f"Using device: {self.device}")
            
            if self.device == "cuda:0":
                logger.info(f"GPU: {torch.cuda.get_device_name()}")
                logger.info(f"VRAM Available: {torch.cuda.get_device_properties(0).total_memory / 1024**3:.2f} GB")
            
            # Get HF token from environment
            hf_token = os.getenv("HF_TOKEN")
            
            logger.info("Loading Llama-3.1-8B-Instruct model...")
            base_model_name = "meta-llama/Llama-3.1-8B-Instruct"
            
            self.tokenizer = AutoTokenizer.from_pretrained(
                base_model_name,
                use_fast=True,
                trust_remote_code=True,
                token=hf_token
            )
            
            self.model = AutoModelForCausalLM.from_pretrained(
                base_model_name,
                torch_dtype=torch.float16 if self.device == "cuda:0" else torch.float32,
                device_map="auto" if self.device == "cuda:0" else None,
                trust_remote_code=True,
                token=hf_token
            )
            
            # Set pad token
            if self.tokenizer.pad_token is None:
                self.tokenizer.pad_token = self.tokenizer.eos_token
            
            self.model_loaded = True
            logger.info("✅ Model loaded successfully!")
            
        except Exception as e:
            logger.error(f"❌ Error loading model: {str(e)}")
            self.model_loaded = False

def generate_response(prompt, temperature=0.8, model_manager=None):
    """ELEGANT AI ARCHITECT SOLUTION - Clean, simple, effective"""
    if not model_manager or not model_manager.model_loaded:
        return "Model not loaded"

    try:
        # Detect request type
        is_cot_request = any(phrase in prompt.lower() for phrase in [
            "return exactly this json array",
            "chain of thinking", 
            "verbatim",
            "json array (no other text)"
        ])
        
        # Get actual model context
        max_context = getattr(model_manager.model.config, "max_position_embeddings", 8192)
        logger.info(f"Model context: {max_context} tokens")
        
        # SIMPLE, CLEAR PROMPT FORMATTING
        if is_cot_request:
            system_msg = "You are an expert at generating JSON training data. Return only valid JSON arrays as requested, no additional text."
        else:
            system_msg = "You are a helpful AI assistant generating high-quality training data."
            
        formatted_prompt = f"""<|begin_of_text|><|start_header_id|>system<|end_header_id|>

{system_msg}

<|eot_id|><|start_header_id|>user<|end_header_id|>

{prompt}

<|eot_id|><|start_header_id|>assistant<|end_header_id|>

"""
        
        # SMART TOKEN ALLOCATION
        if is_cot_request:
            # CoT needs substantial output for complete JSON
            max_new_tokens = 3000  # Generous but not excessive  
            min_new_tokens = 500   # Ensure JSON completion
        else:
            max_new_tokens = 1500
            min_new_tokens = 50
            
        # Reserve space for input
        max_input_tokens = max_context - max_new_tokens - 100
        
        logger.info(f"Token plan: Input≤{max_input_tokens}, Output={min_new_tokens}-{max_new_tokens}")

        # Tokenize
        inputs = model_manager.tokenizer(
            formatted_prompt,
            return_tensors="pt",
            truncation=True,
            max_length=max_input_tokens
        )
        
        # Move to device
        if model_manager.device == "cuda:0":
            inputs = {k: v.to(next(model_manager.model.parameters()).device) for k, v in inputs.items()}
        
        # CLEAN GENERATION
        with torch.no_grad():
            outputs = model_manager.model.generate(
                **inputs,
                max_new_tokens=max_new_tokens,
                min_new_tokens=min_new_tokens,
                temperature=temperature,
                top_p=0.9,
                do_sample=True,
                pad_token_id=model_manager.tokenizer.eos_token_id,
                early_stopping=False,
                repetition_penalty=1.1
            )
        
        # Decode
        full_response = model_manager.tokenizer.decode(outputs[0], skip_special_tokens=True)
        
        # Log stats
        input_len = inputs['input_ids'].shape[1]
        output_len = outputs[0].shape[0]
        generated_len = output_len - input_len
        logger.info(f"Generated {generated_len} tokens (min was {min_new_tokens})")
        
        # CLEAN EXTRACTION
        if "<|start_header_id|>assistant<|end_header_id|>" in full_response:
            response = full_response.split("<|start_header_id|>assistant<|end_header_id|>", 1)[-1].strip()
        else:
            # Fallback
            response = full_response[len(formatted_prompt):].strip()
        
        # For CoT, extract clean JSON if possible
        if is_cot_request and '[' in response and ']' in response:
            # Find the most complete JSON array
            json_pattern = r'\[(?:[^[\]]+|\[[^\]]*\])*\]'
            matches = re.findall(json_pattern, response, re.DOTALL)
            
            if matches:
                # Pick the longest match (most complete)
                best_match = max(matches, key=len)
                # Verify it has reasonable content
                if '"user"' in best_match and '"assistant"' in best_match:
                    logger.info(f"Extracted JSON: {len(best_match)} chars")
                    response = best_match
        
        logger.info(f"Final response: {len(response)} chars")
        return response.strip()

    except Exception as e:
        logger.error(f"Generation error: {e}")
        return f"Error: {e}"

# Initialize model
model_manager = ModelManager()

def respond(message, history, temperature):
    """Gradio interface function"""
    try:
        response = generate_response(message, temperature, model_manager)
        history.append([message, response])
        return history, ""
    except Exception as e:
        logger.error(f"Error in respond: {e}")
        history.append([message, f"Error: {e}"])
        return history, ""

# Create Gradio interface
with gr.Blocks(title="Question Generation API") as demo:
    gr.Markdown("# Question Generation API")
    
    chatbot = gr.Chatbot(height=400)
    msg = gr.Textbox(label="Message", placeholder="Enter your prompt...")
    temperature = gr.Slider(minimum=0.1, maximum=1.0, value=0.8, step=0.1, label="Temperature")
    
    with gr.Row():
        submit = gr.Button("Submit", variant="primary")
        clear = gr.Button("Clear")
    
    submit.click(respond, [msg, chatbot, temperature], [chatbot, msg])
    msg.submit(respond, [msg, chatbot, temperature], [chatbot, msg])
    clear.click(lambda: ([], ""), outputs=[chatbot, msg])

if __name__ == "__main__":
    demo.launch(server_name="0.0.0.0", server_port=7860, share=False)