Update pages/Video Upload.py

pages/Video Upload.py

@@ -1,4 +1,72 @@
-    # --- Styled Upload Section ---
+import logging
+import queue
+from collections import deque
+from concurrent.futures import ThreadPoolExecutor
+
+import streamlit as st
+import cv2
+from streamlit_webrtc import WebRtcMode, webrtc_streamer
+from model import Predictor
+import openai
+
+# Настройки
+DEFAULT_WIDTH = 50
+openai.api_key = 'sk-proj-GDxupB1DFvTTWBg38VyST3BlbkFJ7MdcACLwu3u0U1QvWeMb'
+logger = logging.getLogger(__name__)
+
+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,
+    )
+    return response.choices[0].message['content'].strip()
+
+# Центрируем контент
+width = 50
+side = max((100 - width) / 1.2, 0.01)
+_, container, _ = st.columns([side, width, side])
+
+# Модель инференса
+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)
+
+# Основной интерфейс
+def main(config_path):
+    # --- Заголовок блока ---
     st.markdown("""
     <div class="upload-section">
         <h3>🎥 Sign Language Recognition Demo</h3>
@@ -6,6 +74,60 @@
     </div>
     """, unsafe_allow_html=True)
 
-    uploaded_file = st.file_uploader("Upload Video", type=["mp4", "avi", "mov", "gif"])
+    # --- Скрытый лейбл uploader'а в стилизованной обёртке ---
+    with st.container():
+        uploaded_file = st.file_uploader(" ", type=["mp4", "avi", "mov", "gif"], label_visibility="collapsed")
+
+    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()
+
+            # Вывод результата
+            text_output.markdown(
+                f'<div class="section"><p style="font-size:20px">🖐️ Detected gestures: <b>{" ".join(gestures)}</b></p></div>',
+                unsafe_allow_html=True
+            )
+
+            # Исправление текста
+            st.text(correct_text_gpt3(" ".join(gestures)))
+
+if __name__ == "__main__":
+    logging.basicConfig(level=logging.INFO)
+    main("configs/config.json")
+