Create app.py

app.py

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+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()