First commit for the app

README.md

@@ -1,13 +1,29 @@
 ---
-title: ImagenetResnetModel
-emoji: ⚡
-colorFrom: indigo
-colorTo: gray
+title: ResNet50 ImageNet Classifier
+emoji: 🖼️
+colorFrom: blue
+colorTo: red
 sdk: gradio
-sdk_version: 5.9.1
+sdk_version: 4.12.0
 app_file: app.py
 pinned: false
-short_description: Imagenet Resent Model
+license: mit
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# ResNet-50 ImageNet Classifier
+
+This is a demo for ResNet-50 model trained on ImageNet dataset.
+
+## Model Details
+
+- **Architecture:** ResNet-50
+- **Framework:** PyTorch
+- **Input:** Images (224x224 RGB)
+- **Output:** 1000 class probabilities
+
+## Usage
+
+1. Upload an image
+2. Get top-5 class predictions with probabilities
+
+## Model Configuration

app.py

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+import gradio as gr
+from transformers import AutoModelForImageClassification
+import torch
+import torchvision.transforms as transforms
+from PIL import Image
+
+# Load model from Hub instead of local file
+def load_model():
+    model = AutoModelForImageClassification.from_pretrained(
+        "YOUR_USERNAME/resnet-imagenet",
+        trust_remote_code=True
+    )
+    model.eval()
+    return model
+
+# Preprocessing
+transform = transforms.Compose([
+    transforms.Resize(256),
+    transforms.CenterCrop(224),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], 
+                       std=[0.229, 0.224, 0.225])
+])
+
+# Inference function
+def predict(image):
+    model = load_model()
+    
+    # Preprocess image
+    img = Image.fromarray(image)
+    img = transform(img).unsqueeze(0)
+    
+    # Inference
+    with torch.no_grad():
+        output = model(img)
+        probabilities = torch.nn.functional.softmax(output[0], dim=0)
+        
+    # Get top 5 predictions
+    top5_prob, top5_catid = torch.topk(probabilities, 5)
+    return {f"Class {i}": float(prob) for i, prob in zip(top5_catid, top5_prob)}
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(),
+    outputs=gr.Label(num_top_classes=5),
+    title="ResNet Image Classification",
+    description="Upload an image to classify it using ResNet"
+)
+
+iface.launch() 

requirements.txt

@@ -0,0 +1,5 @@
+torch
+torchvision
+transformers
+gradio
+Pillow