Create app.py

app.py

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+import gradio as gr
+import cv2
+import mediapipe as mp
+import numpy as np
+
+# Initialize Mediapipe Pose Estimation
+mp_pose = mp.solutions.pose
+pose = mp_pose.Pose(static_image_mode=True, model_complexity=2)
+mp_drawing = mp.solutions.drawing_utils
+
+def estimate_pose(image):
+    # Convert image from BGR (OpenCV) to RGB
+    image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    # Perform pose detection
+    results = pose.process(image_rgb)
+    
+    if not results.pose_landmarks:
+        return image  # No pose found, return the original image
+
+    # Draw pose landmarks on the image
+    annotated_image = image.copy()
+    mp_drawing.draw_landmarks(
+        annotated_image,
+        results.pose_landmarks,
+        mp_pose.POSE_CONNECTIONS,
+        landmark_drawing_spec=mp_drawing.DrawingSpec(color=(0, 255, 0), thickness=2, circle_radius=2),
+        connection_drawing_spec=mp_drawing.DrawingSpec(color=(0, 0, 255), thickness=2, circle_radius=2),
+    )
+
+    return annotated_image
+
+# Gradio Interface
+interface = gr.Interface(
+    fn=estimate_pose,
+    inputs=gr.Image(type="numpy", label="Upload an Image"),
+    outputs=gr.Image(type="numpy", label="Pose Landmarks Image"),
+    title="Human Pose Estimation",
+    description="Upload an image to detect and visualize human pose landmarks.",
+)
+
+# Launch the Gradio app
+if __name__ == "__main__":
+    interface.launch()