Update app.py

app.py

@@ -323,112 +323,4 @@ def create_interface():
 
 if __name__ == "__main__":
     demo = create_interface()
-    demo.queue().launch(share=True)import gradio as gr
-import torch
-import numpy as np
-from PIL import Image
-from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
-
-MODEL_PATH = "THUDM/cogvlm2-video-llama3-chat"
-DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
-TORCH_TYPE = torch.bfloat16 if torch.cuda.is_available() and torch.cuda.get_device_capability()[0] >= 8 else torch.float16
-
-def load_model():
-    """Loads the pre-trained model and tokenizer with quantization configurations."""
-    quantization_config = BitsAndBytesConfig(
-        load_in_4bit=True,
-        bnb_4bit_compute_dtype=TORCH_TYPE,
-        bnb_4bit_use_double_quant=True,
-        bnb_4bit_quant_type="nf4"
-    )
-    
-    tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH, trust_remote_code=True)
-    model = AutoModelForCausalLM.from_pretrained(
-        MODEL_PATH,
-        torch_dtype=TORCH_TYPE,
-        trust_remote_code=True,
-        quantization_config=quantization_config,
-        device_map="auto"
-    ).eval()
-    
-    return model, tokenizer
-
-def predict_image(prompt, image, temperature, model, tokenizer):
-    """Generates predictions based on the image and textual prompt."""
-    image = image.convert("RGB")  # Ensure image is in RGB format
-    
-    # Convert image to model-expected format
-    inputs = model.build_conversation_input_ids(
-        tokenizer=tokenizer,
-        query=prompt,
-        images=[image],
-        history=[],
-        template_version='chat'
-    )
-    
-    inputs = {
-        'input_ids': inputs['input_ids'].unsqueeze(0).to(DEVICE),
-        'token_type_ids': inputs['token_type_ids'].unsqueeze(0).to(DEVICE),
-        'attention_mask': inputs['attention_mask'].unsqueeze(0).to(DEVICE),
-        'images': [[inputs['images'][0].to(DEVICE).to(TORCH_TYPE)]],
-    }
-    
-    gen_kwargs = {
-        "max_new_tokens": 512,
-        "pad_token_id": 128002,
-        "top_k": 1,
-        "do_sample": False,
-        "top_p": 0.1,
-        "temperature": temperature,
-    }
-    
-    with torch.no_grad():
-        outputs = model.generate(**inputs, **gen_kwargs)
-        outputs = outputs[:, inputs['input_ids'].shape[1]:]
-        response = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    
-    return response
-
-model, tokenizer = load_model()
-
-def inference(image):
-    """Generates a description of the input image."""
-    try:
-        if not image:
-            return "Please upload an image first."
-        
-        prompt = "Describe the image and the components observed in the given input image."
-        temperature = 0.3
-        response = predict_image(prompt, image, temperature, model, tokenizer)
-        
-        return response
-    except Exception as e:
-        return f"An error occurred during analysis: {str(e)}"
-
-def create_interface():
-    """Creates the Gradio interface for Image Description System."""
-    with gr.Blocks() as demo:
-        gr.Markdown("""
-        # Image Description System
-        Upload an image, and the system will describe the image and its components.
-        """)
-        
-        with gr.Row():
-            with gr.Column():
-                image_input = gr.Image(label="Upload Image", type="pil")
-                analyze_btn = gr.Button("Describe Image", variant="primary")
-            
-            with gr.Column():
-                output = gr.Textbox(label="Image Description", lines=10)
-        
-        analyze_btn.click(
-            fn=inference,
-            inputs=[image_input],
-            outputs=[output]
-        )
-    
-    return demo
-
-if __name__ == "__main__":
-    demo = create_interface()
-    demo.queue().launch(share=True)
+    demo.queue().launch(share=True)