Update app.py

app.py

@@ -1,22 +1,34 @@
-from transformers import pipeline
+import cv2
+import numpy as np
+import mediapipe as mp
 import gradio as gr
 
-# Use a lightweight model for testing
-model = pipeline("text-classification", model="distilbert-base-uncased-finetuned-sst-2-english")
+# Initialize MediaPipe Pose model
+mp_pose = mp.solutions.pose
+pose = mp_pose.Pose()
 
-# A simple function to test the app
-def classify_text(text):
-    return model(text)
+# Function to detect pose
+def detect_pose(image):
+    # Convert image from RGB to BGR for OpenCV processing
+    image_rgb = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
 
-# Gradio interface
+    # Run MediaPipe Pose model
+    results = pose.process(image_rgb)
+
+    if results.pose_landmarks:
+        return "Person Detected: Standing or Sitting Pose Identified"
+    else:
+        return "No person detected, please try again"
+
+# Gradio Interface
 interface = gr.Interface(
-    fn=classify_text,
-    inputs="text",
-    outputs="json",
-    title="Text Classification Test",
-    description="Enter a sentence to classify its sentiment."
+    fn=detect_pose,
+    inputs=gr.Image(type="pil"),  # Accepts an image as input
+    outputs="text",  # Outputs the detected pose
+    title="Pose Detection for Exoskeleton",
+    description="Upload an image of a person sitting or standing. The model will determine their pose."
 )
 
-# Launch the app
+# Launch the Gradio App
 if __name__ == "__main__":
     interface.launch(server_name="0.0.0.0")