Create app.py

app.py

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+import cv2
+import torch
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import streamlit as st
+import numpy as np
+import mediapipe as mp
+
+# Initialize MediaPipe Pose
+mp_pose = mp.solutions.pose
+pose = mp_pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.5)
+
+# Initialize MediaPipe Drawing
+mp_drawing = mp.solutions.drawing_utils
+
+# Load the Hugging Face model and tokenizer
+model_name = "your-huggingface-model-name"
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# Yoga Pose Classification Function using Hugging Face model
+def classify_pose(landmarks):
+    # Prepare input for the model
+    landmark_list = [landmark.x for landmark in landmarks] + [landmark.y for landmark in landmarks] + [landmark.z for landmark in landmarks]
+    inputs = tokenizer(landmark_list, return_tensors="pt")
+    
+    # Get model predictions
+    outputs = model(**inputs)
+    predictions = torch.argmax(outputs.logits, dim=1)
+    
+    # Map predictions to pose names (adjust this mapping according to your model)
+    pose_names = ["Mountain Pose", "Tree Pose", "Warrior Pose", "Unknown Pose"]
+    return pose_names[predictions.item()]
+
+def main():
+    st.title("Live Yoga Pose Detection with Hugging Face")
+    
+    # Start video capture
+    cap = cv2.VideoCapture(0)
+    
+    stframe = st.empty()
+    
+    while cap.isOpened():
+        success, image = cap.read()
+        if not success:
+            st.error("Ignoring empty camera frame.")
+            continue
+        
+        # Convert the BGR image to RGB
+        image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+        
+        # Process the image and detect the pose
+        results = pose.process(image_rgb)
+        
+        # Draw the pose annotation on the image
+        if results.pose_landmarks:
+            mp_drawing.draw_landmarks(
+                image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS)
+            
+            # Classify the detected pose
+            landmarks = results.pose_landmarks.landmark
+            pose_name = classify_pose(landmarks)
+            
+            # Display the classification result on the image
+            cv2.putText(image, pose_name, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 2, cv2.LINE_AA)
+        
+        # Convert the image back to BGR for OpenCV
+        image_bgr = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+        
+        # Display the image in Streamlit
+        stframe.image(image_bgr, channels='BGR')
+        
+        # Break the loop if 'q' is pressed
+        if cv2.waitKey(5) & 0xFF == ord('q'):
+            break
+    
+    cap.release()
+    cv2.destroyAllWindows()
+
+if _name_ == "_main_":
+    main()