Create app.py

app.py

@@ -0,0 +1,58 @@
+
+from flask import Flask, request, jsonify
+from transformers import AutoModelForImageClassification
+from PIL import Image
+import torch
+import torchvision.transforms as transforms
+
+app = Flask(__name__)
+
+# Load model safely (disable safetensors conversion)
+model_name = "SanketJadhav/PlantDiseaseClassifier-Resnet50"
+try:
+    model = AutoModelForImageClassification.from_pretrained(
+        model_name,
+        use_safetensors=False
+    )
+    print(f"✅ Model '{model_name}' loaded successfully")
+except Exception as e:
+    print(f"❌ Error loading model: {e}")
+    model = None
+
+# Manual preprocessing
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406],
+                         std=[0.229, 0.224, 0.225])
+])
+
+@app.route("/predict", methods=["POST"])
+def predict():
+    if model is None:
+        return jsonify({"error": "Model not loaded"}), 500
+
+    if "image" not in request.files:
+        return jsonify({"error": "No image file provided"}), 400
+
+    try:
+        file = request.files["image"]
+        image = Image.open(file.stream).convert("RGB")
+
+        inputs = transform(image).unsqueeze(0)
+
+        with torch.no_grad():
+            outputs = model(inputs)
+            logits = outputs.logits
+            predicted_class = logits.argmax(-1).item()
+
+        label = model.config.id2label[predicted_class]
+        return jsonify({"prediction": label})
+
+    except Exception as e:
+        return jsonify({"error": str(e)}), 500
+
+if __name__ == "__main__":
+    # Hugging Face hamesha port 7860 use karta hai
+    app.run(host="0.0.0.0", port=7860)
+