Update app.py

app.py

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+import streamlit as st
+import torch
+from PIL import Image
+import face_recognition
+import faiss
+from sentence_transformers import SentenceTransformer
+from transformers import pipeline
+import cv2
+import numpy as np
+import subprocess
+import tempfile
+import os
+import yt_dlp
+from moviepy.editor import VideoFileClip
+
+# Helper functions
+def get_video_id(url):
+    return url.split("v=")[1].split("&")[0]
+
+def download_youtube_video(url, output_path):
+    ydl_opts = {
+        'format': 'bestvideo[ext=mp4]+bestaudio[ext=m4a]/best[ext=mp4]/best',
+        'outtmpl': os.path.join(output_path, '%(id)s.%(ext)s'),
+    }
+    with yt_dlp.YoutubeDL(ydl_opts) as ydl:
+        info = ydl.extract_info(url, download=True)
+        filename = ydl.prepare_filename(info)
+    return filename
+
+def process_video(video_url, output_dir, video_id):
+    # Placeholder for video processing logic
+    # This should include face detection, object detection, transcription, etc.
+    # For now, we'll just download the video
+    video_path = download_youtube_video(video_url, output_dir)
+    
+    # Extract frames (simplified version)
+    video = cv2.VideoCapture(video_path)
+    fps = video.get(cv2.CAP_PROP_FPS)
+    frame_count = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
+    duration = frame_count / fps
+    
+    frames = []
+    frame_times = []
+    for i in range(0, frame_count, int(fps)):  # Extract one frame per second
+        video.set(cv2.CAP_PROP_POS_FRAMES, i)
+        ret, frame = video.read()
+        if ret:
+            frames.append(frame)
+            frame_times.append(i / fps)
+    
+    video.release()
+    
+    return {
+        'video_path': video_path,
+        'frames': frames,
+        'frame_times': frame_times,
+        'duration': duration,
+        'fps': fps
+    }
+
+def search(query, index_path, metadata_path, model):
+    # Placeholder for search functionality
+    # This should use FAISS for efficient similarity search
+    return [], []
+
+# Load models
+@st.cache_resource
+def load_models():
+    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+    clip_model, preprocess = torch.hub.load('openai/CLIP', 'clip_vit_b32', device=device)
+    text_model = SentenceTransformer("all-MiniLM-L6-v2").to(device)
+    qa_model = pipeline("question-answering", model="distilbert-base-cased-distilled-squad", device=0 if torch.cuda.is_available() else -1)
+    return clip_model, preprocess, text_model, qa_model
+
+clip_model, preprocess, text_model, qa_model = load_models()
+
+# Streamlit UI
+st.title("Enhanced YouTube Video Analysis")
+
+video_url = st.text_input("Enter YouTube Video URL")
+if st.button("Analyze"):
+    with st.spinner("Processing video..."):
+        video_id = get_video_id(video_url)
+        results = process_video(video_url, "output_dir", video_id)
+    
+    if results:
+        st.success("Video processed successfully!")
+        
+        # Text search and question answering
+        st.subheader("Text Search and Q&A")
+        query = st.text_input("Enter a search query or question")
+        if query:
+            # Placeholder for text search and QA
+            st.write("Text search and QA functionality to be implemented")
+        
+        # Image upload and similarity search
+        st.subheader("Image Search")
+        uploaded_image = st.file_uploader("Upload an image to find similar frames", type=["jpg", "jpeg", "png"])
+        if uploaded_image:
+            # Placeholder for image search
+            st.write("Image search functionality to be implemented")
+        
+        # Face upload and recognition
+        st.subheader("Face Search")
+        uploaded_face = st.file_uploader("Upload a face image to find appearances", type=["jpg", "jpeg", "png"])
+        if uploaded_face:
+            face_image = face_recognition.load_image_file(uploaded_face)
+            face_encoding = face_recognition.face_encodings(face_image)[0]
+            
+            face_appearances = []
+            face_frames = []
+            
+            for i, frame in enumerate(results['frames']):
+                face_locations = face_recognition.face_locations(frame)
+                face_encodings = face_recognition.face_encodings(frame, face_locations)
+                
+                for encoding in face_encodings:
+                    if face_recognition.compare_faces([face_encoding], encoding)[0]:
+                        face_appearances.append(results['frame_times'][i])
+                        face_frames.append(frame)
+            
+            st.write(f"Face appearances found at {len(face_appearances)} timestamps.")
+            
+            if face_frames:
+                # Create a temporary directory to store frames
+                with tempfile.TemporaryDirectory() as temp_dir:
+                    # Save frames as images
+                    for i, frame in enumerate(face_frames):
+                        cv2.imwrite(os.path.join(temp_dir, f"frame_{i:04d}.jpg"), frame)
+                    
+                    # Use FFmpeg to create a video from the frames
+                    output_video = "face_appearances.mp4"
+                    ffmpeg_command = [
+                        "ffmpeg",
+                        "-framerate", str(results['fps']),
+                        "-i", os.path.join(temp_dir, "frame_%04d.jpg"),
+                        "-c:v", "libx264",
+                        "-pix_fmt", "yuv420p",
+                        output_video
+                    ]
+                    subprocess.run(ffmpeg_command, check=True)
+                
+                # Display the generated video
+                st.video(output_video)
+                
+                # Provide download link for the video
+                with open(output_video, "rb") as file:
+                    btn = st.download_button(
+                        label="Download Face Appearances Video",
+                        data=file,
+                        file_name="face_appearances.mp4",
+                        mime="video/mp4"
+                    )
+            else:
+                st.write("No frames with the uploaded face were found in the video.")
+        
+        # Display original video
+        st.subheader("Original Video")
+        st.video(results['video_path'])
+
+else:
+    st.warning("Please enter a valid YouTube URL and click 'Analyze'")