Update app.py

app.py

@@ -1,133 +1,3 @@
-# without hand landmarking
-# import streamlit as st
-# import mediapipe as mp
-# import cv2
-# import os
-# import time
-# from queue import Queue
-
-# # Import necessary components from MediaPipe
-# BaseOptions = mp.tasks.BaseOptions
-# GestureRecognizer = mp.tasks.vision.GestureRecognizer
-# GestureRecognizerOptions = mp.tasks.vision.GestureRecognizerOptions
-# GestureRecognizerResult = mp.tasks.vision.GestureRecognizerResult
-# VisionRunningMode = mp.tasks.vision.RunningMode
-
-# # Correct path to the Gesture Recognizer model file
-# model_path = './model/model/gesture_recognizer.task'
-
-# # Check if file exists
-# if not os.path.exists(model_path):
-#     raise FileNotFoundError(f"Model file not found at {model_path}")
-
-# # Queue to share results between the callback and main thread
-# gesture_queue = Queue()
-
-# # Callback function to process results and add them to the queue
-# def print_result(result: GestureRecognizerResult, output_image: mp.Image, timestamp_ms: int):
-#     results = []  # Collect gesture results
-#     if result.gestures:
-#         for hand_gestures in result.gestures:
-#             for gesture in hand_gestures:
-#                 results.append(f"Gesture: **{gesture.category_name}**, Confidence: **{gesture.score:.2f}**")
-#     else:
-#         results.append("No gestures detected.")
-#     gesture_queue.put(results)
-
-# # Configure the Gesture Recognizer
-# options = GestureRecognizerOptions(
-#     base_options=BaseOptions(model_asset_path=model_path),
-#     running_mode=VisionRunningMode.LIVE_STREAM,
-#     result_callback=print_result
-# )
-
-# # Custom App Header
-# st.markdown("<h1 style='text-align: center; color: #4CAF50;'>Gesture Recognition App 🚀</h1>", unsafe_allow_html=True)
-# st.markdown("<p style='text-align: center; color: grey;'>Recognize hand gestures in real time with MediaPipe and Streamlit</p>", unsafe_allow_html=True)
-
-# # Sidebar for User Controls
-# st.sidebar.title("Control Panel")
-# run_app = st.sidebar.button("Start Gesture Recognition")
-# st.sidebar.write("Toggle the button above to start the app.")
-
-# # Placeholder for video feed and results
-# video_placeholder = st.empty()  # Placeholder for the video feed
-# result_placeholder = st.empty()  # Placeholder for gesture results
-
-# # Footer with branding
-# st.sidebar.markdown(
-#     "<hr><p style='text-align: center;'>Made with ❤️ using Streamlit</p>", unsafe_allow_html=True
-# )
-
-# if run_app:
-#     st.markdown("<h2 style='text-align: center;'>Processing Video Feed...</h2>", unsafe_allow_html=True)
-#     cap = cv2.VideoCapture(0)
-
-#     # Initialize a monotonically increasing timestamp
-#     start_time = time.time()
-
-#     with GestureRecognizer.create_from_options(options) as recognizer:
-#         while cap.isOpened():
-#             success, frame = cap.read()
-#             if not success:
-#                 st.warning("No frames available from the video feed.")
-#                 break
-
-#             # Compute the current timestamp in milliseconds
-#             current_time_ms = int((time.time() - start_time) * 1000)
-
-#             # Convert frame to a MediaPipe Image
-#             mp_image = mp.Image(image_format=mp.ImageFormat.SRGB, data=frame)
-
-#             # Perform gesture recognition asynchronously
-#             recognizer.recognize_async(mp_image, current_time_ms)
-
-#             # Display the frame in Streamlit
-#             frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
-#             video_placeholder.image(frame_rgb, channels="RGB", caption="Gesture Recognition", use_column_width=True)
-
-#             # Retrieve and display gesture results from the queue
-#             while not gesture_queue.empty():
-#                 results = gesture_queue.get()
-#                 result_placeholder.markdown(
-#                     "<h3 style='text-align: center; color: #FF5722;'>Detected Gestures</h3>",
-#                     unsafe_allow_html=True,
-#                 )
-#                 result_placeholder.markdown(
-#                     "<ul>" + "".join([f"<li>{result}</li>" for result in results]) + "</ul>",
-#                     unsafe_allow_html=True,
-#                 )
-
-#     cap.release()
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-# with hand landmark
 import streamlit as st
 import mediapipe as mp
 import cv2
@@ -148,8 +18,8 @@ GestureRecognizerResult = mp.tasks.vision.GestureRecognizerResult
 VisionRunningMode = mp.tasks.vision.RunningMode
 
 # Correct path to the Gesture Recognizer model file
-model_path = './model/model/gesture_recognizer.task'
-
+#model_path = './model/model/gesture_recognizer.task'
+model_path = 'model/gesture_recognizer.task'
 # Check if file exists
 if not os.path.exists(model_path):
     raise FileNotFoundError(f"Model file not found at {model_path}")
@@ -327,22 +197,3 @@ if st.session_state.run_app:
             video_placeholder.image(frame, channels="BGR", caption="Gesture & Hand Landmark Detection", use_column_width=True)
 
     cap.release()
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