Create app.py

app.py

@@ -0,0 +1,79 @@
+import gradio as gr
+from llava.model.builder import load_pretrained_model
+from llava.mm_utils import process_images, tokenizer_image_token
+from llava.constants import IMAGE_TOKEN_INDEX
+import torch
+from PIL import Image
+
+model_path = "microsoft/llava-med-v1.5-mistral-7b"
+tokenizer, model, image_processor, _ = load_pretrained_model(
+    model_path=model_path,
+    model_base=None,
+    model_name="llava-med-v1.5-mistral-7b",
+    load_4bit=False,  # Disable 4-bit quantization for CPU
+    device_map="cpu"   # Force CPU usage
+)
+model.to('cpu')
+
+def analyze_medical_image(image, question):
+    if isinstance(image, str):
+        image = Image.open(image)
+    else:
+        image = Image.fromarray(image)
+    
+    image_tensor = process_images([image], image_processor, model.config)[0]
+    prompt = f"USER: <image>\n{question}\nASSISTANT:"
+    
+    input_ids = tokenizer_image_token(
+        prompt, 
+        tokenizer,
+        IMAGE_TOKEN_INDEX,
+        return_tensors='pt'
+    ).unsqueeze(0)
+    
+    with torch.inference_mode():
+        output_ids = model.generate(
+            input_ids,
+            images=image_tensor.unsqueeze(0),
+            max_new_tokens=512,
+            do_sample=True,
+            temperature=0.7,
+            use_cache=True
+        )
+
+    response = tokenizer.decode(
+        output_ids[0][input_ids.shape[1]:], 
+        skip_special_tokens=True
+    ).strip()
+    
+    return response
+
+with gr.Blocks() as demo:
+    gr.Markdown("# LLaVA-Med Medical Image Analysis")
+    gr.Markdown("Ask questions about medical images using Microsoft's LLaVA-Med 1.5-Mistral-7B")
+    
+    with gr.Row():
+        with gr.Column():
+            image_input = gr.Image(label="Upload Medical Image", type="pil")
+            question_input = gr.Textbox(label="Question", placeholder="Ask about the medical image...")
+            submit_btn = gr.Button("Analyze")
+        
+        with gr.Column():
+            output_text = gr.Textbox(label="Analysis Result", interactive=False)
+    
+    examples = gr.Examples(
+        examples=[
+            ["examples/chest_xray.jpg", "What abnormalities are present in this chest X-ray?"],
+            ["examples/retina_scan.jpg", "Are there any signs of diabetic retinopathy?"]
+        ],
+        inputs=[image_input, question_input],
+        label="Example Queries"
+    )
+    
+    submit_btn.click(
+        fn=analyze_medical_image,
+        inputs=[image_input, question_input],
+        outputs=output_text
+    )
+
+demo.queue(max_size=10).launch()