Update app.py

app.py

@@ -3,7 +3,7 @@ import json
 import math
 import tempfile
 from pathlib import Path
-from typing import Dict, List, Tuple
+from typing import Dict, Tuple
 
 import cv2
 import numpy as np
@@ -11,7 +11,7 @@ import mediapipe as mp
 import gradio as gr
 
 # ------------------------------
-# Configuration & Reference Poses
+# Configuration
 # ------------------------------
 REFERENCE_POSES_FILE = "reference_poses.json"
 DEFAULT_TOLERANCE = 15.0
@@ -112,20 +112,6 @@ def compare_angles(detected, reference, tolerance=DEFAULT_TOLERANCE):
     return overall, per_joint_score, per_joint_diff
 
 
-def suggest_corrections(per_joint_diff, tol=DEFAULT_TOLERANCE):
-    tips = []
-    for joint, diff in per_joint_diff.items():
-        if diff is None:
-            tips.append(f"{joint}: not detected clearly.")
-            continue
-        if abs(diff) <= tol:
-            tips.append(f"{joint}: good (within ±{tol}°).")
-        elif diff > 0:
-            tips.append(f"{joint}: decrease angle by {abs(diff):.0f}° (bend more).")
-        else:
-            tips.append(f"{joint}: increase angle by {abs(diff):.0f}° (straighten more).")
-    return tips
-
 # ------------------------------
 # Video processing
 # ------------------------------
@@ -195,9 +181,14 @@ def process_video(input_path: str, pose_name: str, tolerance: float = DEFAULT_TO
     pose.close()
 
     avg_score = float(np.mean(aggregate_scores)) if aggregate_scores else 0.0
-    suggestions = ["Try maintaining stability and proper alignment."]
+    return out_path, {
+        "pose": pose_name,
+        "score_percent": avg_score,
+        "suggestions": [
+            f"Try maintaining stability. Overall correctness: {avg_score:.1f}%."
+        ]
+    }
 
-    return out_path, {"pose": pose_name, "score_percent": avg_score, "suggestions": suggestions}
 
 # ------------------------------
 # Gradio Interface
@@ -206,42 +197,31 @@ ref_poses = load_reference_poses()
 pose_list = list(ref_poses.keys())
 
 with gr.Blocks(title="Yoga Pose Correctness Checker") as demo:
-    gr.Markdown(
-        """
-        # 🧘 Yoga Pose Correctness Checker
-        Upload or record a short video. The app will analyze your yoga pose and report:
-        - ✅ How correct your pose is (percentage)
-        - 📊 Joint-by-joint feedback
-        - 💡 Suggestions for improvement
-        """
-    )
-
-    with gr.Tab("Upload Video"):
-        video_in = gr.Video(label="Upload a video file (MP4/MOV)")
-
-    with gr.Tab("Record with Webcam"):
-        webcam_in = gr.Camera(label="Record using webcam")
-
+    gr.Markdown("""
+    # 🧘 Yoga Pose Correctness Checker
+    Upload a short video of your yoga pose.  
+    The app will analyze:
+    - ✅ Pose correctness percentage  
+    - 📊 Joint-by-joint feedback  
+    - 💡 Suggestions for improvement
+    """)
+
+    video_in = gr.Video(label="Upload a video (MP4/MOV)")
     pose_dropdown = gr.Dropdown(choices=pose_list, value=pose_list[0], label="Select Pose")
     tol_slider = gr.Slider(5, 40, value=DEFAULT_TOLERANCE, step=1, label="Tolerance (degrees)")
     run_btn = gr.Button("Analyze Pose")
     output_video = gr.Video(label="Annotated Video Output")
     output_json = gr.JSON(label="Results and Suggestions")
 
-    def analyze(video_path, webcam_path, pose_name, tolerance):
-        path = video_path or webcam_path
-        if not path:
-            return None, {"error": "Please upload or record a video first."}
-        annotated_path, result = process_video(path, pose_name, tolerance)
+    def analyze(video_path, pose_name, tolerance):
+        if not video_path:
+            return None, {"error": "Please upload a video first."}
+        annotated_path, result = process_video(video_path, pose_name, tolerance)
         if annotated_path is None:
             return None, {"error": result}
         return annotated_path, result
 
-    run_btn.click(
-        analyze,
-        inputs=[video_in, webcam_in, pose_dropdown, tol_slider],
-        outputs=[output_video, output_json]
-    )
+    run_btn.click(analyze, inputs=[video_in, pose_dropdown, tol_slider], outputs=[output_video, output_json])
 
 if __name__ == "__main__":
     demo.launch(server_name="0.0.0.0", server_port=int(os.environ.get("PORT", 7860)))