testing

app.py

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+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer, AutoProcessor, TextStreamer
+import torch
+
+# Configure torch to use CPU
+device = "cpu"
+torch.set_default_device(device)
+
+# Load model and tokenizer
+def load_model():
+    model_name = "forestav/unsloth_vision_radiography_finetune"
+    
+    # Load with CPU optimization settings
+    model = AutoModelForCausalLM.from_pretrained(
+        model_name,
+        device_map="cpu",
+        torch_dtype=torch.float16,
+        low_cpu_mem_usage=True
+    )
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    processor = AutoProcessor.from_pretrained(model_name)
+    return model, tokenizer, processor
+
+# Initialize model and tokenizer globally
+print("Loading model...")
+model, tokenizer, processor = load_model()
+print("Model loaded!")
+
+def analyze_image(image, instruction):
+    if instruction.strip() == "":
+        instruction = "You are an expert radiographer. Describe accurately what you see in this image."
+    
+    # Prepare the messages
+    messages = [
+        {"role": "user", "content": [
+            {"type": "image"},
+            {"type": "text", "text": instruction}
+        ]}
+    ]
+    
+    # Process the image and text
+    inputs = processor(
+        images=image,
+        text=tokenizer.apply_chat_template(messages, add_generation_prompt=True),
+        return_tensors="pt"
+    )
+    
+    # Generate the response
+    text_streamer = TextStreamer(tokenizer, skip_prompt=True)
+    
+    # Generate with lower resource settings
+    with torch.no_grad():
+        outputs = model.generate(
+            **inputs,
+            max_new_tokens=128,
+            temperature=1.2,
+            min_p=0.1,
+            use_cache=True,
+            streamer=text_streamer
+        )
+    
+    # Decode the response
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return response
+
+# Create the Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("""
+    # Medical Image Analysis Assistant
+    Upload a medical image and receive a professional description from an AI radiographer.
+    """)
+    
+    with gr.Row():
+        with gr.Column():
+            image_input = gr.Image(type="pil", label="Upload Medical Image")
+            instruction_input = gr.Textbox(
+                label="Custom Instruction (optional)",
+                placeholder="You are an expert radiographer. Describe accurately what you see in this image.",
+                lines=2
+            )
+            submit_btn = gr.Button("Analyze Image")
+        
+        with gr.Column():
+            output_text = gr.Textbox(label="Analysis Result", lines=10)
+    
+    # Handle the submission
+    submit_btn.click(
+        fn=analyze_image,
+        inputs=[image_input, instruction_input],
+        outputs=output_text
+    )
+    
+    gr.Markdown("""
+    ### Notes:
+    - The model runs on CPU and may take a few moments to process each image
+    - For best results, upload clear, high-quality medical images
+    - Default instruction will be used if none is provided
+    """)
+
+# Launch the app
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+transformers>=4.31.0
+torch>=2.0.0
+gradio>=3.34.0
+accelerate>=0.26.0
+pillow