Update app.py

app.py

@@ -8,24 +8,24 @@ processor = AutoImageProcessor.from_pretrained("prithivMLmods/Weather-Image-Clas
 model = AutoModelForImageClassification.from_pretrained("prithivMLmods/Weather-Image-Classification")
 
 # Inference function
-def classify_weather(image_input):
+def classify_weather(image_path):
     try:
-        # PIL image guaranteed by Gradio
-        inputs = processor(images=[image_input], return_tensors="pt")
+        image = Image.open(image_path).convert("RGB")
 
+        inputs = processor(images=[image], return_tensors="pt")
         with torch.no_grad():
             outputs = model(**inputs)
             logits = outputs.logits.squeeze()
             probs = torch.softmax(logits, dim=-1).tolist()
             labels = [model.config.id2label[i] for i in range(len(probs))]
             return dict(zip(labels, probs))
-    except Exception:
-        return {"Error": 1.0}
+    except Exception as e:
+        return {"Error": str(e)}
 
 # Gradio interface
 iface = gr.Interface(
     fn=classify_weather,
-    inputs=gr.Image(type="pil"),  # ✅ PIL input
+    inputs=gr.Image(type="filepath"),  # ✅ File path input
     outputs=gr.Label(num_top_classes=5, label="Weather Condition"),
     title="Weather Image Classification",
     description="Upload an image to classify the weather condition (sun, rain, snow, fog, or clouds)."