app.py

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)