Initial commit for ai-digital-coach app

app.py

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+import gradio as gr
+import cv2
+import numpy as np
+import mediapipe as mp
+
+# --------------- MODULE 1: Utility Function to Calculate Angle ---------------
+def calculate_angle(a, b, c):
+    a = np.array(a)
+    b = np.array(b)
+    c = np.array(c)
+
+    ba = a - b
+    bc = c - b
+
+    cos_angle = np.dot(ba, bc) / (np.linalg.norm(ba) * np.linalg.norm(bc))
+    angle = np.degrees(np.arccos(np.clip(cos_angle, -1.0, 1.0)))
+
+    return angle
+
+# --------------- MODULE 2: Squat Counting Function (Refactored for Live Updates) ---------------
+def count_squats_live(video):
+    mp_pose = mp.solutions.pose
+    pose = mp_pose.Pose(min_detection_confidence=0.5, model_complexity=1)
+    mp_drawing = mp.solutions.drawing_utils
+
+    cap = cv2.VideoCapture(video)
+    squat_count = 0
+    is_squat_down = False
+
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if not ret:
+            break
+
+        frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+        results = pose.process(frame_rgb)
+
+        if results.pose_landmarks:
+            landmarks = results.pose_landmarks.landmark
+
+            hip = [landmarks[mp_pose.PoseLandmark.LEFT_HIP.value].x,
+                   landmarks[mp_pose.PoseLandmark.LEFT_HIP.value].y]
+            knee = [landmarks[mp_pose.PoseLandmark.LEFT_KNEE.value].x,
+                    landmarks[mp_pose.PoseLandmark.LEFT_KNEE.value].y]
+            ankle = [landmarks[mp_pose.PoseLandmark.LEFT_ANKLE.value].x,
+                     landmarks[mp_pose.PoseLandmark.LEFT_ANKLE.value].y]
+
+            squat_angle = calculate_angle(hip, knee, ankle)
+
+            if squat_angle < 80 and not is_squat_down:
+                is_squat_down = True
+            elif squat_angle > 150 and is_squat_down:
+                squat_count += 1
+                is_squat_down = False
+
+            mp_drawing.draw_landmarks(frame, results.pose_landmarks, mp_pose.POSE_CONNECTIONS)
+            
+            # Draw the angle at the knee
+            cv2.putText(frame, f"Angle: {int(squat_angle)}", (10, 140),
+                        cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
+            cv2.line(frame, (int(hip[0] * frame.shape[1]), int(hip[1] * frame.shape[0])),
+                     (int(knee[0] * frame.shape[1]), int(knee[1] * frame.shape[0])), (0, 0, 255), 2)
+            cv2.line(frame, (int(knee[0] * frame.shape[1]), int(knee[1] * frame.shape[0])),
+                     (int(ankle[0] * frame.shape[1]), int(ankle[1] * frame.shape[0])), (0, 0, 255), 2)
+
+            # Display counts and legend
+            cv2.putText(frame, f"Squats: {squat_count}", (10, 100),
+                        cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 0), 2)
+
+        yield cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+
+    cap.release()
+
+# --------------- MODULE 3: Pushup Counting Function (Refactored for Live Updates) ---------------
+def count_pushups_live(video):
+    mp_pose = mp.solutions.pose
+    pose = mp_pose.Pose(min_detection_confidence=0.5, model_complexity=1)
+    mp_drawing = mp.solutions.drawing_utils
+
+    cap = cv2.VideoCapture(video)
+    pushup_count = 0
+    is_pushup_down = False
+
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if not ret:
+            break
+
+        frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+        results = pose.process(frame_rgb)
+
+        if results.pose_landmarks:
+            landmarks = results.pose_landmarks.landmark
+
+            shoulder = [landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].x,
+                        landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].y]
+            elbow = [landmarks[mp_pose.PoseLandmark.LEFT_ELBOW.value].x,
+                     landmarks[mp_pose.PoseLandmark.LEFT_ELBOW.value].y]
+            wrist = [landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value].x,
+                     landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value].y]
+
+            pushup_angle = calculate_angle(shoulder, elbow, wrist)
+
+            if pushup_angle < 90 and not is_pushup_down:
+                is_pushup_down = True
+            elif pushup_angle > 160 and is_pushup_down:
+                pushup_count += 1
+                is_pushup_down = False
+
+            mp_drawing.draw_landmarks(frame, results.pose_landmarks, mp_pose.POSE_CONNECTIONS)
+            
+            # Draw the angle at the elbow
+            cv2.putText(frame, f"Angle: {int(pushup_angle)}", (10, 140),
+                        cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
+            cv2.line(frame, (int(shoulder[0] * frame.shape[1]), int(shoulder[1] * frame.shape[0])),
+                     (int(elbow[0] * frame.shape[1]), int(elbow[1] * frame.shape[0])), (0, 0, 255), 2)
+            cv2.line(frame, (int(elbow[0] * frame.shape[1]), int(elbow[1] * frame.shape[0])),
+                     (int(wrist[0] * frame.shape[1]), int(wrist[1] * frame.shape[0])), (0, 0, 255), 2)
+
+            # Display counts
+            cv2.putText(frame, f"Pushups: {pushup_count}", (10, 100),
+                        cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 0), 2)
+
+        yield cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+
+    cap.release()
+
+# --------------- MODULE 4: Gradio Interface ---------------
+def process_video_live(video, exercise):
+    if exercise == "squats":
+        generator = count_squats_live(video)
+    elif exercise == "pushups":
+        generator = count_pushups_live(video)
+
+    # Loop through the generator and yield each frame
+    for frame in generator:
+        yield frame
+
+interface = gr.Interface(
+    fn=process_video_live,
+    inputs=[
+        gr.Video(format="mp4", sources=["upload"], label="Upload Video", height=600),
+        gr.Radio(["squats", "pushups"], value="squats", label="Choose Exercise")
+    ],
+    outputs=gr.Image(label="Live analysis", height=600),
+    flagging_mode="never",
+    title="AI-Powered Digital Coach",
+    description="<div style='text-align: center;'>Upload a video to count squats or pushups!</div>",
+    live=False
+)
+
+# Launch the app
+if __name__ == "__main__":
+    interface.launch()

requirements.txt

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+mediapipe==0.10.18
+opencv-python==4.10.0.84
+numpy==1.26.4
+gradio==5.6.0