Update app.py

app.py

@@ -1,3 +1,51 @@
+import gradio as gr
+import json
+import torch
+from torch import nn
+from torchvision import models, transforms
+from huggingface_hub import hf_hub_download
+from PIL import Image
+import requests
+import base64
+from io import BytesIO
+import os
+
+# Define the number of classes
+num_classes = 2
+
+# Download model from Hugging Face
+def download_model():
+    try:
+        model_path = hf_hub_download(repo_id="jays009/Restnet50", filename="pytorch_model.bin")
+        return model_path
+    except Exception as e:
+        print(f"Error downloading model: {e}")
+        return None
+
+# Load the model from Hugging Face
+def load_model(model_path):
+    try:
+        model = models.resnet50(pretrained=False)
+        model.fc = nn.Linear(model.fc.in_features, num_classes)
+        model.load_state_dict(torch.load(model_path, map_location=torch.device("cpu")))
+        model.eval()
+        return model
+    except Exception as e:
+        print(f"Error loading model: {e}")
+        return None
+
+# Download the model and load it
+model_path = download_model()
+model = load_model(model_path) if model_path else None
+
+# Define the transformation for the input image
+transform = transforms.Compose([
+    transforms.Resize(256),
+    transforms.CenterCrop(224),
+    transforms.ToTensor(),
+    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
+])
+
 def predict(data):
     try:
         image_input = data.get('image', None)