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app_gradio.py

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+import os
+import gradio as gr
+import torch
+from PIL import Image
+from fastai.learner import load_learner
+
+# Model loading function
+def load_model():
+    model_path = 'models/jimi_classifier'
+    try:
+        if os.path.isdir(model_path):
+            learn = load_learner(model_path)
+            return learn
+    except Exception as e:
+        print(f"Error loading model: {e}")
+        
+        # Fallback stub model for testing
+        class StubLearner:
+            def predict(self, img):
+                import random
+                is_jimis = random.choice([True, False])
+                pred_class = 'jimis' if is_jimis else 'not_jimis'
+                pred_idx = 0 if is_jimis else 1
+                probs = torch.tensor([0.8, 0.2]) if is_jimis else torch.tensor([0.2, 0.8])
+                return pred_class, pred_idx, probs
+        
+        return StubLearner()
+
+# Prediction function
+def predict_image(img):
+    if img is None:
+        return "Please upload an image", 0
+    
+    model = load_model()
+    
+    try:
+        # Process the image
+        pred_class, pred_idx, probs = model.predict(img)
+        confidence = float(probs[pred_idx]) * 100
+        
+        result = "Jimis" if str(pred_class).lower() == "jimis" else "Not Jimis"
+        return result, round(confidence, 2)
+    except Exception as e:
+        print(f"Error during prediction: {e}")
+        import traceback
+        traceback.print_exc()
+        return f"Error processing image: {str(e)}", 0
+
+# Example images for the demo
+examples = [
+    # You can add example image paths here if you have them
+]
+
+# Create the Gradio interface
+demo = gr.Interface(
+    fn=predict_image,
+    inputs=gr.Image(type="pil", label="Upload an image"),
+    outputs=[
+        gr.Label(label="Prediction"),
+        gr.Number(label="Confidence (%)")
+    ],
+    title="Jimis Classifier",
+    description="Upload an image to check if it contains Jimis",
+    examples=examples,
+    article="""
+    ## How it works
+    
+    This application uses a machine learning model trained to recognize Jimis in images.
+    The model was trained on a custom dataset of Jimis and non-Jimis images using the 
+    fastai library and a ResNet architecture.
+    
+    Simply upload an image, and the model will tell you whether it contains Jimis and 
+    how confident it is about its prediction.
+    """
+)
+
+# Launch the app
+if __name__ == "__main__":
+    demo.launch()

requirements_hf.txt

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+gradio>=4.0.0
+fastai>=2.7.9
+torch>=1.12.0
+torchvision>=0.13.0
+Pillow>=9.0.0