Update app.py

app.py

@@ -1,34 +1,23 @@
-import cv2
-import numpy as np
-import mediapipe as mp
+from transformers import pipeline
 import gradio as gr
 
-# Initialize MediaPipe Pose model
-mp_pose = mp.solutions.pose
-pose = mp_pose.Pose()
+# Load a pre-trained image classification model
+model = pipeline("image-classification", model="google/vit-base-patch16-224")
 
-# 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)
+# Define a function for detecting actions
+def classify_image(image):
+    predictions = model(image)
+    return {pred["label"]: round(pred["score"], 4) for pred in predictions}
 
-    # 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
+# Gradio interface
 interface = gr.Interface(
-    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."
+    fn=classify_image,
+    inputs=gr.Image(type="pil"),  # Accepts image input
+    outputs="json",  # Outputs predictions
+    title="Action Classifier",
+    description="Upload an image, and the model will classify actions (e.g., standing, sitting)."
 )
 
-# Launch the Gradio App
+# Launch the app
 if __name__ == "__main__":
     interface.launch(server_name="0.0.0.0")