Update app.py

app.py

@@ -3,48 +3,33 @@ from PIL import Image
 import torch
 from transformers import AutoImageProcessor, AutoModelForImageClassification
 
-# ----------------------
-# Load model + processor
-# ----------------------
+# Load model
 processor = AutoImageProcessor.from_pretrained("prithivMLmods/Weather-Image-Classification")
 model = AutoModelForImageClassification.from_pretrained("prithivMLmods/Weather-Image-Classification")
 
-# ----------------------
 # Inference function
-# ----------------------
-def classify_weather(image_file):
+def classify_weather(image_input):
     try:
-        # Open the uploaded file
-        image = Image.open(image_file).convert("RGB")
+        # PIL image guaranteed by Gradio
+        inputs = processor(images=[image_input], return_tensors="pt")
 
-        # Preprocess
-        inputs = processor(images=[image], return_tensors="pt")
-
-        # Inference
         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 label -> probability dictionary
             return dict(zip(labels, probs))
-
-    except Exception as e:
-        # Safe fallback if something unexpected happens
+    except Exception:
         return {"Error": 1.0}
 
-# ----------------------
 # Gradio interface
-# ----------------------
 iface = gr.Interface(
     fn=classify_weather,
-    inputs=gr.File(file_types=[".jpg", ".png"]),  # Accept uploaded files
+    inputs=gr.Image(type="pil"),  # ✅ PIL 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)."
 )
 
-# Launch the Space with error reporting
 if __name__ == "__main__":
     iface.launch(show_error=True)