app.py

from flask import Flask, render_template, request, jsonify
import numpy as np
from PIL import Image
import io
import base64
from tensorflow.keras.models import load_model

app = Flask(__name__)
model = load_model("letter_cnn_model.h5")
labels = [chr(ord('a') + i) for i in range(26)]

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

@app.route("/predict", methods=["POST"])
def predict():
    data = request.json["image"]
    image_data = base64.b64decode(data.split(",")[1])
    img = Image.open(io.BytesIO(image_data)).convert("L").resize((28, 28))
    img = 255 - np.array(img)
    img = img / 255.0
    img = img.reshape(1, 28, 28, 1)
    pred = model.predict(img)
    index = np.argmax(pred[0])
    label = labels[index].upper()
    confidence = float(pred[0][index])
    return jsonify({
        "label": label,
        "confidence": confidence
    })

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