Upload app.py with huggingface_hub

app.py

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+import os
+import shutil
+from huggingface_hub import hf_hub_download
+import gradio as gr
+import torch
+import torch.nn.functional as F
+import numpy as np
+from PIL import Image
+import json
+import cv2
+import matplotlib
+matplotlib.use('Agg')
+import matplotlib.pyplot as plt
+import albumentations as A
+from albumentations.pytorch import ToTensorV2
+
+from models.hybrid_model import HybridFoodClassifier
+
+# MONKEY PATCH: Completely disable API generation
+import gradio.routes as routes_module
+def empty_api_info(*args, **kwargs):
+    return {"api": {}}
+routes_module.api_info = empty_api_info
+
+REPO_ID = os.getenv("MODEL_REPO_ID", "codealchemist01/food-image-classifier-hybrid")
+
+class FoodClassifier:
+    def __init__(self, model_path: str):
+        self.device = 'cpu'
+        checkpoint = torch.load(model_path, map_location=self.device, weights_only=False)
+        num_classes = checkpoint.get('num_classes', 101)
+        
+        try:
+            with open('real_class_mapping.json', 'r') as f:
+                self.class_names = json.load(f)['real_class_names']
+        except:
+            self.class_names = [f"class_{i}" for i in range(num_classes)]
+        
+        self.model = HybridFoodClassifier(num_classes=num_classes, pretrained=False)
+        self.model.load_state_dict(checkpoint['model_state_dict'])
+        self.model = self.model.to(self.device)
+        self.model.eval()
+        
+        self.transform = A.Compose([
+            A.Resize(224, 224),
+            A.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+            ToTensorV2()
+        ])
+        
+        print(f"Model loaded! Classes: {num_classes}")
+    
+    def predict(self, image, top_k=5):
+        if image is None:
+            return "", None
+        
+        if image.mode != 'RGB':
+            image = image.convert('RGB')
+        
+        img_tensor = self.transform(image=np.array(image))['image'].unsqueeze(0).to(self.device)
+        
+        with torch.no_grad():
+            outputs = self.model(img_tensor, return_features=True)
+            probs = F.softmax(outputs['logits'], dim=1).cpu().numpy()[0]
+            attention_maps = self.model.get_attention_maps(img_tensor)
+        
+        top_indices = np.argsort(probs)[::-1][:int(top_k)]
+        results = "\n".join([
+            f"{i+1}. {self.class_names[idx]}: {probs[idx]:.3f}"
+            for i, idx in enumerate(top_indices)
+        ])
+        
+        # Attention viz
+        img_np = np.array(image.resize((224, 224)))
+        cnn_att = cv2.resize(attention_maps['cnn_attention'].cpu().numpy()[0, 0], (224, 224))
+        cnn_att = (cnn_att - cnn_att.min()) / (cnn_att.max() - cnn_att.min() + 1e-8)
+        vit_att = cv2.resize(attention_maps['vit_attention'].cpu().numpy()[0, 0], (224, 224))
+        vit_att = (vit_att - vit_att.min()) / (vit_att.max() - vit_att.min() + 1e-8)
+        
+        fig, axes = plt.subplots(1, 3, figsize=(15, 5))
+        axes[0].imshow(img_np)
+        axes[0].set_title('Original')
+        axes[0].axis('off')
+        axes[1].imshow(img_np)
+        axes[1].imshow(cnn_att, alpha=0.6, cmap='jet')
+        axes[1].set_title('CNN Attention')
+        axes[1].axis('off')
+        axes[2].imshow(img_np)
+        axes[2].imshow(vit_att, alpha=0.6, cmap='jet')
+        axes[2].set_title('ViT Attention')
+        axes[2].axis('off')
+        plt.tight_layout()
+        
+        import io
+        buf = io.BytesIO()
+        plt.savefig(buf, format='png', dpi=100, bbox_inches='tight')
+        buf.seek(0)
+        attention_img = Image.open(buf)
+        plt.close(fig)
+        
+        return results, attention_img
+
+print("Downloading model...")
+ckpt_path = hf_hub_download(repo_id=REPO_ID, filename="best_model.pth")
+mapping_path = hf_hub_download(repo_id=REPO_ID, filename="real_class_mapping.json")
+shutil.copy(mapping_path, "real_class_mapping.json")
+
+classifier = FoodClassifier(ckpt_path)
+
+demo = gr.Interface(
+    fn=classifier.predict,
+    inputs=[
+        gr.Image(type="pil", label="Upload Food Image"),
+        gr.Slider(1, 10, 5, step=1, label="Top K")
+    ],
+    outputs=[
+        gr.Textbox(label="Results", lines=10),
+        gr.Image(label="Attention Maps", height=400)
+    ],
+    title="Food Classifier",
+    description="Upload food images for classification"
+)
+
+demo.launch(server_name="0.0.0.0", server_port=7860)