Update pages/Video Upload.py

pages/Video Upload.py

@@ -1,73 +1,3 @@
-import logging
-import queue
-from collections import deque
-from concurrent.futures import ThreadPoolExecutor
-
-import streamlit as st
-from streamlit_webrtc import WebRtcMode, webrtc_streamer
-
-import cv2
-from model import Predictor  # Import Predictor from your model file
-import openai
-
-DEFAULT_WIDTH = 50
-
-# Initialize the OpenAI client
-openai.api_key = 'sk-proj-GDxupB1DFvTTWBg38VyST3BlbkFJ7MdcACLwu3u0U1QvWeMb'
-
-def correct_text_gpt3(input_text):
-    prompt = f"Исправь грамматические ошибки в тексте: '{input_text}'"
-    response = openai.ChatCompletion.create(
-        model="gpt-3.5-turbo",
-        messages=[
-            {"role": "system", "content": "You are a helpful assistant that corrects grammatical errors."},
-            {"role": "user", "content": prompt}
-        ],
-        max_tokens=50,
-        n=1,
-        stop=None,
-        temperature=0.5,
-    )
-    corrected_text = response.choices[0].message['content'].strip()
-    return corrected_text
-
-# Center content layout
-width = 50
-side = max((100 - width) / 1.2, 0.01)
-_, container, _ = st.columns([side, width, side])
-
-logger = logging.getLogger(__name__)
-
-class SLInference:
-    def __init__(self, config_path):
-        self.config = self.load_config(config_path)
-        self.predictor = Predictor(self.config)
-        self.input_queue = deque(maxlen=32)
-        self.pred = ''
-
-    def load_config(self, config_path):
-        import json
-        with open(config_path, 'r') as f:
-            return json.load(f)
-
-    def start(self):
-        pass
-
-    def predict(self, frames):
-        frames_resized = [cv2.resize(frame, (224, 224)) for frame in frames]
-        while len(frames_resized) < 32:
-            frames_resized.append(frames_resized[-1])
-        result = self.predictor.predict(frames_resized)
-        if result:
-            return result["labels"][0]
-        return 'no'
-
-def process_batch(inference_thread, frames, gestures):
-    gesture = inference_thread.predict(frames)
-    if gesture not in ['no', ''] and gesture not in gestures:
-        gestures.append(gesture)
-
-def main(config_path):
     # --- Styled Upload Section ---
     st.markdown("""
     <div class="upload-section">
@@ -78,50 +8,4 @@ def main(config_path):
 
     uploaded_file = st.file_uploader("Upload Video", type=["mp4", "avi", "mov", "gif"])
 
-    if uploaded_file is not None:
-        video_bytes = uploaded_file.read()
-        container.video(data=video_bytes)
-
-        inference_thread = SLInference(config_path)
-        inference_thread.start()
-
-        text_output = st.empty()
-
-        if st.button("🔍 Predict Gestures"):
-            import tempfile
-            tfile = tempfile.NamedTemporaryFile(delete=False)
-            tfile.write(video_bytes)
-            cap = cv2.VideoCapture(tfile.name)
-
-            gestures = []
-            frames = []
-            batch_size = 32
-
-            def process_frames(batch):
-                process_batch(inference_thread, batch, gestures)
-
-            with ThreadPoolExecutor() as executor:
-                while cap.isOpened():
-                    ret, frame = cap.read()
-                    if not ret:
-                        break
-                    frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
-                    frames.append(frame)
-                    if len(frames) == batch_size:
-                        executor.submit(process_frames, frames)
-                        frames = []
-
-                if frames:
-                    executor.submit(process_frames, frames)
-
-            cap.release()
-            text_output.markdown(
-                f'<div class="section"><p style="font-size:20px">🖐️ Detected gestures: <b>{" ".join(gestures)}</b></p></div>',
-                unsafe_allow_html=True
-            )
-            st.text(correct_text_gpt3(" ".join(gestures)))
-
-if __name__ == "__main__":
-    logging.basicConfig(level=logging.INFO)
-    main("configs/config.json")