Update app.py

app.py

@@ -307,44 +307,4 @@ with gr.Blocks(title="CuMo", theme=gr.themes.Default(), css=block_css) as demo:
 demo.queue(
     status_update_rate=10,
     api_open=False
-).launch()tokenizer, model, image_processor, context_len = load_pretrained_model(
-    model_path, model_base, model_name, load_8bit, load_4bit, device, use_flash_attn=False
-)
-
-
-PROMPT = "What material is this item and how is it disposed of?"
-PROMPT_WITH_IMAGE = f"{DEFAULT_IMAGE_TOKEN} {PROMPT}"
-
-@spaces.GPU
-def classify_image(image):
-    if image is None:
-        return "Please upload an image."
-    if not isinstance(image, Image.Image):
-        image = Image.fromarray(image)
-    images = process_images([image], image_processor, model.config)
-    images = [img.to(device, dtype=torch.float16) for img in images]
-    image_args = {"images": images}
-    input_ids = tokenizer_image_token(PROMPT_WITH_IMAGE, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).to(device)
-    with torch.no_grad():
-        outputs = model.generate(
-            inputs=input_ids,
-            max_new_tokens=128,
-            pad_token_id=tokenizer.eos_token_id,
-            **image_args
-        )
-    output_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    answer = output_text[len(PROMPT):].strip() if output_text.startswith(PROMPT) else output_text
-    return answer
-
-iface = gr.Interface(
-    fn=classify_image,
-    inputs=gr.Image(type="pil", label="Upload an image of a waste item"),
-    outputs=gr.Textbox(label="Classification & Disposal Recommendation"),
-    title="CuMo Waste Classifier",
-    description="Upload a photo of a household waste item. The model will classify the material and recommend how to dispose of it."
-)
-
-if __name__ == "__main__":
-    iface.launch()
-
-
+).launch()