Create app.py

app.py

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+import gradio as gr
+import cv2
+import mediapipe as mp
+import torch
+import numpy as np
+import tempfile
+from transformers import pipeline
+from PIL import Image
+
+# Initialize MediaPipe Pose
+mp_pose = mp.solutions.pose
+
+# Hugging Face pretrained model for action recognition
+action_model = pipeline(
+    "image-classification",
+    model="rvv-karma/Human-Action-Recognition-VIT-Base-patch16-224"
+)
+
+def detect_pose_and_activity(video_file):
+    """
+    Process the uploaded video to detect human poses and classify activity.
+    Video is limited to 10 seconds. Returns annotated video and predicted action.
+    """
+    try:
+        # Save uploaded video temporarily
+        temp_video = tempfile.NamedTemporaryFile(delete=False, suffix=".mp4")
+        temp_video.write(open(video_file, "rb").read())
+        temp_video.close()
+
+        cap = cv2.VideoCapture(temp_video.name)
+        if not cap.isOpened():
+            return None, "Error: Could not open video."
+
+        fps = cap.get(cv2.CAP_PROP_FPS)
+        if fps == 0:
+            fps = 30  # fallback
+
+        total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+        max_frames = int(min(total_frames/fps, 10) * fps)  # limit 10s
+
+        output_frames = []
+        action_predictions = []
+
+        # Process frames
+        with mp_pose.Pose(static_image_mode=False, min_detection_confidence=0.5) as pose:
+            for _ in range(max_frames):
+                ret, frame = cap.read()
+                if not ret:
+                    break
+
+                # Pose detection
+                image_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+                results = pose.process(image_rgb)
+                if results.pose_landmarks:
+                    mp.solutions.drawing_utils.draw_landmarks(frame, results.pose_landmarks, mp_pose.POSE_CONNECTIONS)
+
+                output_frames.append(frame)
+
+                # Convert frame to PIL image for Hugging Face model
+                pil_image = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
+                pred = action_model(pil_image)
+                action_predictions.append(pred[0]['label'])
+
+        cap.release()
+
+        if len(output_frames) == 0:
+            return None, "Error: No frames to process."
+
+        # Take the most frequent predicted action
+        action_label = max(set(action_predictions), key=action_predictions.count)
+
+        # Save annotated video
+        output_file = tempfile.NamedTemporaryFile(delete=False, suffix=".mp4").name
+        height, width, _ = output_frames[0].shape
+        out = cv2.VideoWriter(output_file, cv2.VideoWriter_fourcc(*'mp4v'), fps, (width, height))
+        for f in output_frames:
+            out.write(f)
+        out.release()
+
+        return output_file, f"Predicted Action: {action_label}"
+
+    except Exception as e:
+        return None, f"Runtime Error: {str(e)}"
+
+# Gradio Interface
+iface = gr.Interface(
+    fn=detect_pose_and_activity,
+    inputs=gr.Video(label="Upload a Video (max 10s)"),
+    outputs=[gr.Video(label="Pose Detection Output"), gr.Textbox(label="Detected Action")],
+    title="Human Pose & Activity Recognition",
+    description="Upload a short video (max 10s). The app detects human poses and predicts the activity (e.g., dancing, cycling, running)."
+)
+
+iface.launch()