app.py

import os

# 🔥 HARD CPU ENFORCEMENT (BEFORE ANY IMPORTS)
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
os.environ["MEDIAPIPE_DISABLE_GPU"] = "1"

import cv2
import numpy as np
import math
from flask import Flask, request, jsonify
import mediapipe as mp
from cvzone.ClassificationModule import Classifier

# ---------------- CONFIG ----------------

MODEL_PATH = "Model_old/keras_model.h5"
LABELS_PATH = "Model_old/labels.txt"

IMG_SIZE = 300
OFFSET = 20
CONFIDENCE_THRESHOLD = 0.5
STABILITY_THRESHOLD = 3

# ---------------- APP ----------------

app = Flask(__name__)

# ---------------- MEDIAPIPE (CPU-ONLY, CLOUD SAFE) ----------------

mp_hands = mp.solutions.hands

hands = mp_hands.Hands(
    static_image_mode=True,     # REQUIRED for server inference
    max_num_hands=1,
    model_complexity=0,         # CPU graph only
    min_detection_confidence=0.6,
    min_tracking_confidence=0.6
)

# ---------------- CLASSIFIER ----------------

classifier = Classifier(MODEL_PATH, LABELS_PATH)

with open(LABELS_PATH, "r") as f:
    labels = [line.strip() for line in f.readlines()]

stable_gesture = None
stable_count = 0

# ---------------- API ----------------

@app.route("/predict", methods=["POST"])
def predict():
    global stable_gesture, stable_count

    if "frame" not in request.files:
        return jsonify({"gesture": None, "confidence": 0})

    # ---- Decode image safely ----
    try:
        file = request.files["frame"]
        img_bytes = np.frombuffer(file.read(), np.uint8)
        frame = cv2.imdecode(img_bytes, cv2.IMREAD_COLOR)

        if frame is None:
            return jsonify({"gesture": None, "confidence": 0})

        frame = cv2.flip(frame, 1)
    except Exception:
        return jsonify({"gesture": None, "confidence": 0})

    # ---- MediaPipe inference ----
    try:
        rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
        result = hands.process(rgb)
    except Exception:
        stable_gesture = None
        stable_count = 0
        return jsonify({"gesture": None, "confidence": 0})

    if not result.multi_hand_landmarks:
        stable_gesture = None
        stable_count = 0
        return jsonify({"gesture": None, "confidence": 0})

    # ---- Bounding box from landmarks ----
    h, w, _ = frame.shape
    lm = result.multi_hand_landmarks[0].landmark

    x_vals = [int(p.x * w) for p in lm]
    y_vals = [int(p.y * h) for p in lm]

    x, y = min(x_vals), min(y_vals)
    bw, bh = max(x_vals) - x, max(y_vals) - y

    if bw <= 0 or bh <= 0:
        return jsonify({"gesture": None, "confidence": 0})

    # ---- Image preprocessing ----
    imgWhite = np.ones((IMG_SIZE, IMG_SIZE, 3), np.uint8) * 255

    imgCrop = frame[
        max(0, y - OFFSET): y + bh + OFFSET,
        max(0, x - OFFSET): x + bw + OFFSET
    ]

    if imgCrop.size == 0:
        return jsonify({"gesture": None, "confidence": 0})

    aspectRatio = bh / bw

    try:
        if aspectRatio > 1:
            k = IMG_SIZE / bh
            wCal = max(1, math.ceil(k * bw))
            imgResize = cv2.resize(imgCrop, (wCal, IMG_SIZE))
            wGap = (IMG_SIZE - wCal) // 2
            imgWhite[:, wGap:wGap + wCal] = imgResize
        else:
            k = IMG_SIZE / bw
            hCal = max(1, math.ceil(k * bh))
            imgResize = cv2.resize(imgCrop, (IMG_SIZE, hCal))
            hGap = (IMG_SIZE - hCal) // 2
            imgWhite[hGap:hGap + hCal, :] = imgResize
    except Exception:
        return jsonify({"gesture": None, "confidence": 0})

    # ---- Classification ----
    try:
        prediction, index = classifier.getPrediction(imgWhite, draw=False)
        index = int(index)

        if index < 0 or index >= len(labels):
            return jsonify({"gesture": None, "confidence": 0})

        confidence = float(prediction[index])
        confidence = max(0.0, min(confidence, 1.0))
        gesture = labels[index]
    except Exception:
        return jsonify({"gesture": None, "confidence": 0})

    # ---- Stability logic ----
    if gesture == stable_gesture:
        stable_count += 1
    else:
        stable_gesture = gesture
        stable_count = 1

    if stable_count >= STABILITY_THRESHOLD and confidence >= CONFIDENCE_THRESHOLD:
        return jsonify({
            "gesture": gesture,
            "confidence": confidence
        })

    return jsonify({"gesture": None, "confidence": 0})


# ---------------- ENTRY ----------------

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