Create app.py

app.py

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+from flask import Flask, request, jsonify
+from PIL import Image
+import torch
+from torchvision import transforms, models
+
+# Initialize Flask app
+app = Flask(__name__)
+
+# Load the trained model
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Define the model architecture
+model = models.resnet152()
+model.fc = torch.nn.Linear(model.fc.in_features, 26)  # Adjust for the number of classes
+model.load_state_dict(torch.load("model.pth", map_location=device))
+model = model.to(device)
+model.eval()
+
+# Define preprocessing for the input image
+preprocess = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+])
+
+# Class labels (replace with your dataset's classes)
+CLASS_LABELS = [
+"bluebell", "buttercup", "colts_foot", "corn_poppy", "cowslip", 
+"crocus", "daffodil", "daisy", "dandelion", "foxglove", 
+"fritillary", "geranium", "hibiscus", "iris", "lily_valley", 
+"pansy", "petunia", "rose", "snowdrop", "sunflower", 
+"tigerlily", "tulip", "wallflower", "water_lily", "wild_tulip", 
+"windflower"
+]
+
+@app.route("/predict", methods=["POST"])
+def predict():
+    if "file" not in request.files:
+        return jsonify({"error": "No file uploaded"}), 400
+
+    file = request.files["file"]
+
+    try:
+        # Load and preprocess the image
+        image = Image.open(file.stream).convert("RGB")
+        input_tensor = preprocess(image).unsqueeze(0).to(device)
+
+        # Perform inference
+        with torch.no_grad():
+            outputs = model(input_tensor)
+            _, predicted_class = torch.max(outputs, 1)
+        
+        predicted_label = CLASS_LABELS[predicted_class.item()]
+        return jsonify({"predicted_class": predicted_label})
+
+    except Exception as e:
+        return jsonify({"error": f"Error during prediction: {str(e)}"}), 500
+
+# Run the app (Hugging Face Spaces requires `app.run()` here)
+if __name__ == "__main__":
+    app.run(host="0.0.0.0", port=8080)