app.py

import gradio as gr
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, CLIPProcessor, CLIPModel
from PIL import Image
import logging
import spaces
import numpy as np

logging.basicConfig(level=logging.INFO)

class LLaVAPhiModel:
    def __init__(self, model_id="sagar007/Lava_phi"):
        self.device = "cuda"
        self.model_id = model_id
        logging.info("Initializing LLaVA-Phi model...")
        
        self.tokenizer = AutoTokenizer.from_pretrained(model_id)
        if self.tokenizer.pad_token is None:
            self.tokenizer.pad_token = self.tokenizer.eos_token

        self.processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
        self.history = []
        self.model = None
        self.clip = None
        self.projection = None

    @spaces.GPU
    def ensure_models_loaded(self):
        if not torch.cuda.is_available():
            raise RuntimeError("CUDA is not available. This model requires a GPU.")
        if self.model is None:
            from transformers import BitsAndBytesConfig
            quantization_config = BitsAndBytesConfig(
                load_in_8bit=True,
                bnb_8bit_compute_dtype=torch.float16,
                bnb_8bit_use_double_quant=False
            )
            self.model = AutoModelForCausalLM.from_pretrained(
                self.model_id,
                quantization_config=quantization_config,
                device_map="auto",
                torch_dtype=torch.bfloat16,
                trust_remote_code=True
            )
            self.model.config.pad_token_id = self.tokenizer.eos_token_id
            logging.info("Successfully loaded main model on GPU")

        if self.clip is None:
            self.clip = CLIPModel.from_pretrained("openai/clip-vit-base-patch32").to(self.device)
            logging.info("Successfully loaded CLIP model")
            embed_dim = self.model.config.hidden_size
            clip_dim = self.clip.config.projection_dim
            self.projection = torch.nn.Linear(clip_dim, embed_dim).to(self.device)

    # Rest of your class (process_image, generate_response, etc.) remains unchanged
    # ... (omitted for brevity)

def create_demo():
    try:
        model = LLaVAPhiModel()
        
        demo = gr.Blocks(css="footer {visibility: hidden}")
        with demo:
            gr.Markdown(
                """
                # LLaVA-Phi Demo (Optimized for Accuracy)
                Chat with a vision-language model that can understand both text and images.
                """
            )
            
            chatbot = gr.Chatbot(height=400)
            with gr.Row():
                with gr.Column(scale=0.7):
                    msg = gr.Textbox(
                        show_label=False,
                        placeholder="Enter text and/or upload an image",
                        container=False
                    )
                with gr.Column(scale=0.15, min_width=0):
                    clear = gr.Button("Clear")
                with gr.Column(scale=0.15, min_width=0):
                    submit = gr.Button("Submit", variant="primary")
            
            image = gr.Image(type="pil", label="Upload Image (Optional)")
            
            with gr.Accordion("Advanced Settings", open=False):
                gr.Markdown("Adjust these parameters to control hallucination tendency")
                temp_slider = gr.Slider(0.1, 1.0, value=0.3, step=0.1, label="Temperature (lower = more factual)")
                top_p_slider = gr.Slider(0.5, 1.0, value=0.92, step=0.01, label="Top-p (nucleus sampling)")
                top_k_slider = gr.Slider(10, 100, value=50, step=5, label="Top-k")
                rep_penalty_slider = gr.Slider(1.0, 2.0, value=1.2, step=0.1, label="Repetition Penalty")
                update_params = gr.Button("Update Parameters")
            
            def respond(message, chat_history, image):
                if not message and image is None:
                    return chat_history
                
                response = model.generate_response(message, image)
                chat_history.append((message, response))
                return "", chat_history
            
            def clear_chat():
                model.clear_history()
                return None, None
            
            def update_params_fn(temp, top_p, top_k, rep_penalty):
                return model.update_generation_params(temp, top_p, top_k, rep_penalty)
            
            submit.click(
                respond,
                [msg, chatbot, image],
                [msg, chatbot],
            )
            
            clear.click(
                clear_chat,
                None,
                [chatbot, image],
            )
            
            msg.submit(
                respond,
                [msg, chatbot, image],
                [msg, chatbot],
            )
            
            update_params.click(
                update_params_fn,
                [temp_slider, top_p_slider, top_k_slider, rep_penalty_slider],
                None
            )
        
        return demo
    except Exception as e:
        logging.error(f"Error creating demo: {str(e)}")
        raise

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