Update pages/Video Upload.py

pages/Video Upload.py

@@ -1,187 +1,126 @@
+import logging
+import queue
+from collections import deque
+from concurrent.futures import ThreadPoolExecutor
+
 import streamlit as st
-from PIL import Image
-import numpy as np
-
-# Page config
-st.set_page_config(
-    page_title="GestureGuru",
-    page_icon="🤖",
-    layout="wide",
-    initial_sidebar_state="expanded"
-)
-
-# --- Custom Style ---
-st.markdown("""
-<style>
-body {
-    background-image: url('https://images.unsplash.com/photo-1498050108023-c5249f4df085?auto=format&fit=crop&w=1920&q=80');
-    background-size: cover;
-    background-attachment: fixed;
-    background-position: center;
-    font-family: 'Segoe UI', sans-serif;
-    color: #222;
-    animation: fade-in 2s ease-out;
-}
-.sidebar .sidebar-content {
-    background-color: rgba(255, 255, 255, 0.8);
-    padding: 1rem;
-    border-radius: 16px;
-    box-shadow: 0 4px 12px rgba(0,0,0,0.1);
-    backdrop-filter: blur(8px);
-}
-.hero {
-    text-align: center;
-    padding: 5rem 2rem;
-    background: linear-gradient(135deg, #8e2de2, #4a00e0);
-    border-radius: 24px;
-    color: white;
-    margin-bottom: 3rem;
-    box-shadow: 0 20px 40px rgba(0,0,0,0.1);
-    animation: fade-in 2s ease-out;
-    position: relative;
-}
-.hero h1 {
-    font-size: 3.75em;
-    font-weight: 700;
-    margin-bottom: 0.3em;
-    animation: glow-text 2s infinite alternate;
-}
-.hero p {
-    font-size: 1.3em;
-    font-weight: 300;
-    margin-top: 0.5em;
-    animation: slide-in-bottom 1.2s ease-out;
-}
-.hero img.logo {
-    width: 120px;
-    position: absolute;
-    top: 20px;
-    left: 20px;
-    border-radius: 50%;
-    border: 2px solid white;
-    box-shadow: 0 4px 12px rgba(0,0,0,0.4);
-}
-.section {
-    background: rgba(255, 255, 255, 0.95);
-    border-radius: 20px;
-    padding: 2.5rem;
-    margin-bottom: 2.5rem;
-    box-shadow: 0 10px 30px rgba(0, 0, 0, 0.06);
-    backdrop-filter: blur(4px);
-    animation: fade-in-up 1s ease-out;
-}
-.upload-section {
-    background: linear-gradient(to right, #00c6ff, #0072ff);
-    color: white;
-    padding: 2rem;
-    border-radius: 20px;
-    margin-bottom: 2rem;
-    box-shadow: 0 8px 20px rgba(0, 0, 0, 0.2);
-    animation: fade-in-up 1s ease-out;
-}
-.upload-section h3 {
-    color: white;
-}
-h3 {
-    font-size: 1.8rem;
-    color: #2e2e2e;
-    margin-bottom: 1rem;
-}
-ul, ol {
-    font-size: 1.1em;
-    line-height: 1.6;
-    color: #444;
-}
-.contact-block {
-    text-align: center;
-    padding: 3rem;
-    border-radius: 20px;
-    background: linear-gradient(to right, #ff6a00, #ee0979);
-    color: white;
-    margin-top: 3rem;
-    box-shadow: 0 10px 30px rgba(0, 0, 0, 0.1);
-    animation: fade-in 2s ease-out;
-}
-.contact-block a {
-    color: #fff;
-    text-decoration: underline;
-}
-@keyframes fade-in {
-  from { opacity: 0; }
-  to { opacity: 1; }
-}
-@keyframes fade-in-up {
-  from { opacity: 0; transform: translateY(30px); }
-  to { opacity: 1; transform: translateY(0); }
-}
-@keyframes slide-in-top {
-  0% { transform: translateY(-100px); opacity: 0; }
-  100% { transform: translateY(0); opacity: 1; }
-}
-@keyframes slide-in-bottom {
-  0% { transform: translateY(100px); opacity: 0; }
-  100% { transform: translateY(0); opacity: 1; }
-}
-@keyframes glow-text {
-  from { text-shadow: 0 0 5px #fff, 0 0 10px #bca7ff, 0 0 20px #7e57ff; }
-  to { text-shadow: 0 0 10px #fff, 0 0 20px #b388ff, 0 0 30px #7e57ff; }
-}
-</style>
-""", unsafe_allow_html=True)
-
-# --- Hero Section ---
-st.markdown("""
-<div class="hero">
-    <img src="https://cdn-icons-png.flaticon.com/512/3062/3062634.png" class="logo" alt="logo">
-    <h1>GestureGuru</h1>
-    <p>AI for Sign Language Understanding & Accessibility</p>
-</div>
-""", unsafe_allow_html=True)
-
-# --- Features ---
-st.markdown("""
-<div class="section">
-<h3>✨ Key Features</h3>
-<ul>
-  <li>⚡ Real-time AI gesture recognition</li>
-  <li>🧠 Optimized for Russian Sign Language</li>
-  <li>🖥️ Lightweight and browser-based interface</li>
-  <li>🌍 Promoting accessibility and inclusive communication</li>
-</ul>
-</div>
-""", unsafe_allow_html=True)
-
-# --- How it works ---
-st.markdown("""
-<div class="section">
-<h3>🛠 How it works</h3>
-<ol>
-  <li>Camera captures hand gestures</li>
-  <li>Frame is processed via ONNX deep learning model</li>
-  <li>Text prediction appears instantly on screen</li>
-</ol>
-</div>
-""", unsafe_allow_html=True)
-
-# --- Upload Section ---
-st.markdown("""
-<div class="upload-section">
-    <h3>🎥 Sign Language Recognition Demo</h3>
-    <p>Upload a short video clip to detect sign gestures:</p>
-</div>
-""", unsafe_allow_html=True)
-
-uploaded_file = st.file_uploader("Upload Video", type=["mp4", "avi", "mov", "gif"])
-if uploaded_file:
-    st.video(uploaded_file)
-else:
-    st.warning("Please upload a video file for prediction.")
-
-# --- Contact block ---
-st.markdown("""
-<div class="contact-block">
-    <h3>📬 Let's collaborate!</h3>
-    <p>For demos, partnerships, or research opportunities:</p>
-    <p>Email: <a href="mailto:your@email.com">your@email.com</a></p>
-</div>
-""", unsafe_allow_html=True)
+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">
+        <h3>🎥 Sign Language Recognition Demo</h3>
+        <p>Upload a short video clip to detect sign gestures:</p>
+    </div>
+    """, unsafe_allow_html=True)
+
+    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")