import cv2
import mediapipe as mp
import numpy as np
import gradio as gr

mp_pose = mp.solutions.pose
pose = mp_pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.5)
mp_drawing = mp.solutions.drawing_utils

def calculate_angle(a, b, c):
    a, b, c = np.array(a), np.array(b), np.array(c)
    radians = np.arctan2(c[1]-b[1], c[0]-b[0]) - np.arctan2(a[1]-b[1], a[0]-b[0])
    return np.abs(radians * 180.0 / np.pi)

def check_pullup_feedback(landmarks):
    shoulder = [landmarks[mp_pose.PoseLandmark.RIGHT_SHOULDER.value].x,
                landmarks[mp_pose.PoseLandmark.RIGHT_SHOULDER.value].y]
    elbow = [landmarks[mp_pose.PoseLandmark.RIGHT_ELBOW.value].x,
             landmarks[mp_pose.PoseLandmark.RIGHT_ELBOW.value].y]
    wrist = [landmarks[mp_pose.PoseLandmark.RIGHT_WRIST.value].x,
             landmarks[mp_pose.PoseLandmark.RIGHT_WRIST.value].y]
    
    angle = calculate_angle(shoulder, elbow, wrist)
    target_angle = 70
    tolerance = 20
    accuracy = max(0, min(100, (1 - abs(angle - target_angle) / tolerance) * 100))
    
    if angle > (target_angle + tolerance / 2):
        feedback = ("Incomplete Pull-up - Your elbows remain too extended. Engage your back muscles to pull higher.")
    elif angle < (target_angle - tolerance / 2):
        feedback = ("Over-bending - Your elbows are too flexed. Try to control your descent.")
    else:
        feedback = "Correct Pull-up - Great form!"
    return feedback, int(accuracy)

def draw_accuracy_bar(image, accuracy):
    bar_x, bar_y = 50, image.shape[0] - 50
    bar_width, bar_height = 200, 20
    fill_width = int((accuracy / 100) * bar_width)
    color = (0, 255, 0) if accuracy >= 80 else (0, 0, 255) if accuracy < 50 else (0, 255, 255)
    cv2.rectangle(image, (bar_x, bar_y), (bar_x + bar_width, bar_y + bar_height), (200,200,200), 2)
    cv2.rectangle(image, (bar_x, bar_y), (bar_x + fill_width, bar_y + bar_height), color, -1)
    cv2.putText(image, f"Accuracy: {accuracy}%", (bar_x, bar_y-10),
                cv2.FONT_HERSHEY_DUPLEX, 0.6, (255,255,255), 2)

def analyze_pullups(video_path):
    cap = cv2.VideoCapture(video_path)
    frame_width, frame_height = int(cap.get(3)), int(cap.get(4))
    fps = cap.get(cv2.CAP_PROP_FPS) if cap.get(cv2.CAP_PROP_FPS) > 0 else 30

    output_video = "output_pullups.mp4"
    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
    out = cv2.VideoWriter(output_video, fourcc, fps, (frame_width, frame_height))
    
    while cap.isOpened():
        ret, frame = cap.read()
        if not ret:
            break
        image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
        results = pose.process(image)
        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
        if results.pose_landmarks:
            mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS)
            landmarks = results.pose_landmarks.landmark
            feedback, accuracy = check_pullup_feedback(landmarks)
            draw_accuracy_bar(image, accuracy)
            text_color = (0, 255, 0) if "Correct" in feedback else (0, 0, 255)
            cv2.putText(image, feedback, (50, 50),
                        cv2.FONT_HERSHEY_COMPLEX, 1, text_color, 3)
        out.write(image)
    cap.release()
    out.release()
    return output_video

gr.Interface(
    fn=analyze_pullups,
    inputs=gr.Video(),
    outputs=gr.Video(),
    title="Pull-ups Form Analyzer",
    description="Upload a video of your pull-ups and receive form feedback!"
).launch()