Create app.py

app.py

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+import gradio as gr
+import torch
+import torch.nn as nn
+from torchvision import models, transforms
+from PIL import Image
+
+# 1. SETUP MODEL ARCHITECTURE
+# Based on your logs, you used ResNet50. 
+# If you actually used ResNet18, change 'resnet50' to 'resnet18' below.
+model = models.resnet50(weights=None) 
+
+# 2. MATCH THE FINAL LAYER
+# ResNet50 has 2048 input features in the final layer. 
+# (If you used ResNet18, this number would be 512).
+num_ftrs = model.fc.in_features 
+model.fc = nn.Linear(num_ftrs, 2)
+
+# 3. LOAD WEIGHTS
+# Replace 'fire_detection_resnet18.pth' with the EXACT name of the file you uploaded
+model_path = "fire_detection_resnet18.pth" 
+
+# Load weights for CPU (since HF Spaces Free Tier uses CPU)
+state_dict = torch.load(model_path, map_location=torch.device('cpu'))
+model.load_state_dict(state_dict)
+model.eval()
+
+# 4. DEFINE PREPROCESSING
+# This must match what you used during training
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+])
+
+# 5. PREDICTION FUNCTION
+labels = ['Non-Fire', 'Fire'] # 0 is Non-Fire, 1 is Fire
+
+def predict(image):
+    if image is None:
+        return None
+    
+    # Preprocess
+    image = image.convert('RGB')
+    image_tensor = transform(image).unsqueeze(0) # Add batch dimension
+    
+    # Inference
+    with torch.no_grad():
+        outputs = model(image_tensor)
+        probabilities = torch.nn.functional.softmax(outputs[0], dim=0)
+    
+    # Return dictionary for Gradio Label
+    return {labels[i]: float(probabilities[i]) for i in range(len(labels))}
+
+# 6. LAUNCH GRADIO UI
+interface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil", label="Upload Image"),
+    outputs=gr.Label(num_top_classes=2, label="Prediction"),
+    title="Fire Detection System tements",
+    description="Upload an image to detect if fire is present. (Model: ResNet50)",
+    examples=["fire.jpg", "forest.jpg"] # Optional: Upload these images to your space for users to click
+)
+
+if __name__ == "__main__":
+    interface.launch()