| import gradio as gr | |
| from transformers import pipeline | |
| try: | |
| classifier = pipeline("image-classification", model="prithivMLmods/Recycling-Net-11") | |
| except Exception as e: | |
| print("🚨 Error loading model:", e) | |
| classifier = None | |
| def classify_image(image): | |
| if classifier is None: | |
| return {"error": "Model failed to load"} | |
| try: | |
| results = classifier(image) | |
| return {r["label"]: float(r["score"]) for r in results} | |
| except Exception as e: | |
| print("🚨 Error during classification:", e) | |
| return {"error": str(e)} | |
| demo = gr.Interface( | |
| fn=classify_image, | |
| inputs=gr.Image(type="pil"), | |
| outputs=gr.Label(num_top_classes=3), | |
| title="♻️ Recycling Classifier", | |
| description="Upload an image of waste material and the model will classify it." | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() | |