from flask import Flask, render_template, Response
import cv2
import gradio as gr
from cvzone.HandTrackingModule import HandDetector
from cvzone.ClassificationModule import Classifier
import numpy as np
import math

app = Flask(__name__)

# Load the HandDetector and Classifier
detector = HandDetector(maxHands=1)
classifier = Classifier("keras_model.h5", "labels.txt")
offset = 20
imgSize = 300

# Define labels
labels = ["iam", "ok", "going", "no", "yes", "hi"]

def process_frame(frame):
    imgOutput = frame.copy()
    hands, _ = detector.findHands(frame)

    if hands:
        hand = hands[0]
        x, y, w, h = hand['bbox']

        imgWhite = np.ones((imgSize, imgSize, 3), np.uint8) * 255
        imgCrop = frame[y - offset:y + h + offset, x - offset:x + w + offset]

        if imgCrop.size != 0:
            imgCropShape = imgCrop.shape
            aspectRatio = h / w

            if aspectRatio > 1:
                k = imgSize / h
                wCal = math.ceil(k * w)
                imgResize = cv2.resize(imgCrop, (wCal, imgSize))
                imgResizeShape = imgResize.shape
                wGap = math.ceil((imgSize - wCal) / 2)
                imgWhite[:, wGap: wCal + wGap] = imgResize

            else:
                k = imgSize / w
                hCal = math.ceil(k * h)
                imgResize = cv2.resize(imgCrop, (imgSize, hCal))
                imgResizeShape = imgResize.shape
                hGap = math.ceil((imgSize - hCal) / 2)
                imgWhite[hGap: hCal + hGap, :] = imgResize

            prediction, index = classifier.getPrediction(imgWhite, draw=False)

            cv2.rectangle(imgOutput, (x - offset, y - offset - 70), (x - offset + 400, y - offset + 60 - 50), (0, 255, 0),
                          cv2.FILLED)
            cv2.putText(imgOutput, labels[index], (x, y - 30), cv2.FONT_HERSHEY_COMPLEX, 2, (0, 0, 0), 2)
            cv2.rectangle(imgOutput, (x - offset, y - offset), (x + w + offset, y + h + offset), (0, 255, 0), 4)

    _, img_encoded = cv2.imencode('.jpg', imgOutput)
    return img_encoded.tobytes()

def generate_frames():
    cap = cv2.VideoCapture(0)
    while True:
        success, frame = cap.read()
        if not success:
            break
        else:
            frame = process_frame(frame)
            yield (b'--frame\r\n'
                    b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n')

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

@app.route('/video_feed')
def video_feed():
    return Response(generate_frames(), mimetype='multipart/x-mixed-replace; boundary=frame')

if __name__ == '__main__':
    app.run(debug=True)