app.py

from flask import Flask, request, jsonify
import tensorflow as tf
from flask_cors import CORS
from utils import predict_image
import os
import requests

app = Flask(__name__)
CORS(app)

# ------------------------------
# MODEL CONFIG
# ------------------------------

MODEL_PATH = "model.h5"
MODEL_URL = "https://huggingface.co/bakhili/stroke-classification-resnet-model/resolve/main/stroke_classification_model.h5"

# ------------------------------
# DOWNLOAD MODEL IF NOT EXISTS
# ------------------------------

if not os.path.exists(MODEL_PATH):
    print("Downloading model from Hugging Face...")
    r = requests.get(MODEL_URL, stream=True)

    with open(MODEL_PATH, "wb") as f:
        for chunk in r.iter_content(chunk_size=8192):
            if chunk:
                f.write(chunk)

    print("Model downloaded successfully!")

# ------------------------------
# LOAD MODEL
# ------------------------------

print("Loading model...")
model = tf.keras.models.load_model(MODEL_PATH)
print("Model loaded successfully!")

# ------------------------------
# ROUTES
# ------------------------------

@app.route("/")
def home():
    return "Stroke Detection Backend Running"

@app.route("/predict", methods=["POST"])
def predict():
    try:
        if "file" not in request.files:
            return jsonify({"error": "No file uploaded"}), 400

        file = request.files["file"]

        if file.filename == "":
            return jsonify({"error": "Empty filename"}), 400

        result = predict_image(model, file)

        return jsonify(result)

    except Exception as e:
        print("Error during prediction:", str(e))
        return jsonify({"error": "Prediction failed"}), 500


# ------------------------------
# RUN SERVER
# ------------------------------

if __name__ == "__main__":
    app.run(host="0.0.0.0", port=7860)