app.py

from flask import Flask, request, jsonify, render_template
import tensorflow as tf
import numpy as np
from tensorflow import keras
from tensorflow.keras.preprocessing import image
import os
import gdown

app = Flask(__name__)

# Load trained model
MODEL_PATH = "brain_tumor_vgg16.keras"
MODEL_URL = "https://drive.google.com/uc?id=1ftUVldGPLHOWFLCvZg4iOm8DJMSWfNxM"  
# Download model if not already downloaded
if not os.path.exists(MODEL_PATH):
    print("Downloading model...")
    gdown.download(MODEL_URL, MODEL_PATH, quiet=False, use_cookies=False)

# Load the model
model = keras.models.load_model(MODEL_PATH)  # Fixed NameError

# Function to preprocess uploaded image
def preprocess_image(img_path):
    img = image.load_img(img_path, target_size=(150, 150))
    img_array = image.img_to_array(img) / 255.0  # Normalize
    img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension
    return img_array

@app.route("/")
def index():
    return render_template("index.html")

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

    file = request.files["file"]
    file_path = "temp.jpg"
    file.save(file_path)

    # Preprocess and predict
    img_array = preprocess_image(file_path)
    prediction = model.predict(img_array)[0][0]

    result = "Tumor Detected" if prediction > 0.5 else "No Tumor"
    confidence = float(prediction)

    return jsonify({"prediction": result, "confidence": confidence})


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