app.py

import streamlit as st
import json
import faiss
import numpy as np
from sentence_transformers import SentenceTransformer
import base64
from PIL import Image
import io
import cv2
from insightface.app import FaceAnalysis
from moviepy.editor import VideoFileClip
from sklearn.cluster import DBSCAN
from collections import defaultdict
import plotly.graph_objs as go
from sklearn.decomposition import PCA

# Load models
@st.cache_resource
def load_models():
    text_model = SentenceTransformer("all-MiniLM-L6-v2")
    image_model = SentenceTransformer("clip-ViT-B-32")
    face_app = FaceAnalysis(providers=['CPUExecutionProvider'])
    face_app.prepare(ctx_id=0, det_size=(640, 640))
    return text_model, image_model, face_app

text_model, image_model, face_app = load_models()

# Load data
@st.cache_data
def load_data(video_id):
    with open(f"{video_id}_summary.json", "r") as f:
        summary = json.load(f)
    with open(f"{video_id}_transcription.json", "r") as f:
        transcription = json.load(f)
    with open(f"{video_id}_text_metadata.json", "r") as f:
        text_metadata = json.load(f)
    with open(f"{video_id}_image_metadata.json", "r") as f:
        image_metadata = json.load(f)
    with open(f"{video_id}_face_metadata.json", "r") as f:
        face_metadata = json.load(f)
    return summary, transcription, text_metadata, image_metadata, face_metadata

video_id = "IMFUOexuEXw"
video_path = "avengers_interview.mp4"
summary, transcription, text_metadata, image_metadata, face_metadata = load_data(video_id)

# Load FAISS indexes
@st.cache_resource
def load_indexes(video_id):
    text_index = faiss.read_index(f"{video_id}_text_index.faiss")
    image_index = faiss.read_index(f"{video_id}_image_index.faiss")
    face_index = faiss.read_index(f"{video_id}_face_index.faiss")
    return text_index, image_index, face_index

text_index, image_index, face_index = load_indexes(video_id)

# Face clustering function
def cluster_faces(face_embeddings, eps=0.5, min_samples=3):
    clustering = DBSCAN(eps=eps, min_samples=min_samples, metric='cosine').fit(face_embeddings)
    return clustering.labels_

# Face clustering visualization
def plot_face_clusters(face_embeddings, labels, face_metadata):
    pca = PCA(n_components=3)
    embeddings_3d = pca.fit_transform(face_embeddings)
    
    unique_labels = set(labels)
    colors = [f'rgb({int(r*255)},{int(g*255)},{int(b*255)})' 
              for r, g, b, _ in plt.cm.rainbow(np.linspace(0, 1, len(unique_labels)))]
    
    traces = []
    for label, color in zip(unique_labels, colors):
        cluster_points = embeddings_3d[labels == label]
        
        hover_text = []
        for i, point in enumerate(cluster_points):
            face = face_metadata[np.where(labels == label)[0][i]]
            hover_text.append(f"Cluster {label}<br>Time: {face['start']:.2f}s")
        
        trace = go.Scatter3d(
            x=cluster_points[:, 0],
            y=cluster_points[:, 1],
            z=cluster_points[:, 2],
            mode='markers',
            name=f'Cluster {label}',
            marker=dict(
                size=5,
                color=color,
                opacity=0.8
            ),
            text=hover_text,
            hoverinfo='text'
        )
        traces.append(trace)
    
    layout = go.Layout(
        title='Face Clusters Visualization',
        scene=dict(
            xaxis_title='PCA Component 1',
            yaxis_title='PCA Component 2',
            zaxis_title='PCA Component 3'
        ),
        margin=dict(r=0, b=0, l=0, t=40)
    )
    
    fig = go.Figure(data=traces, layout=layout)
    return fig

# Search functions
def combined_search(query, text_index, image_index, text_metadata, image_metadata, text_model, image_model, n_results=5):
    if isinstance(query, str):
        text_vector = text_model.encode([query], convert_to_tensor=True).cpu().numpy()
        image_vector = image_model.encode([query], convert_to_tensor=True).cpu().numpy()
    else:  # Assume it's an image
        image_vector = image_model.encode(query, convert_to_tensor=True).cpu().numpy()
        text_vector = image_vector  # Use the same vector for text search in this case
    
    text_D, text_I = text_index.search(text_vector, n_results)
    image_D, image_I = image_index.search(image_vector, n_results)
    
    text_results = [{'data': text_metadata[i], 'distance': d, 'type': 'text'} for i, d in zip(text_I[0], text_D[0])]
    image_results = [{'data': image_metadata[i], 'distance': d, 'type': 'image'} for i, d in zip(image_I[0], image_D[0])]
    
    combined_results = sorted(text_results + image_results, key=lambda x: x['distance'])
    return combined_results[:n_results]

def face_search(face_embedding, index, metadata, n_results=5):
    D, I = index.search(np.array(face_embedding).reshape(1, -1), n_results)
    results = [metadata[i] for i in I[0]]
    return results, D[0]

def detect_and_embed_face(image, face_app):
    img_array = np.array(image)
    faces = face_app.get(img_array)
    if len(faces) == 0:
        return None
    largest_face = max(faces, key=lambda x: (x.bbox[2] - x.bbox[0]) * (x.bbox[3] - x.bbox[1]))
    return largest_face.embedding

def create_video_clip(video_path, start_time, end_time, output_path):
    with VideoFileClip(video_path) as video:
        new_clip = video.subclip(start_time, end_time)
        new_clip.write_videofile(output_path, codec="libx264", audio_codec="aac")
    return output_path

# Streamlit UI
st.title("Video Analysis Dashboard")

# Sidebar with full video and scrollable transcript
st.sidebar.header("Full Video")
st.sidebar.video(video_path)

st.sidebar.header("Video Transcript")
transcript_text = transcription['transcription']
st.sidebar.text_area("Full Transcript", transcript_text, height=300)

# Main content
st.header("Video Summary")
col1, col2 = st.columns(2)

with col1:
    st.subheader("Prominent Faces")
    for face in summary['prominent_faces']:
        st.write(f"Face ID: {face['id']}, Appearances: {face['appearances']}")
        if 'thumbnail' in face:
            image = Image.open(io.BytesIO(base64.b64decode(face['thumbnail'])))
            st.image(image, caption=f"Face ID: {face['id']}", width=100)

with col2:
    st.subheader("Themes")
    for theme in summary['themes']:
        st.write(f"Theme ID: {theme['id']}, Keywords: {', '.join(theme['keywords'])}")

# Face Clustering
st.header("Face Clustering")
face_embeddings = face_index.reconstruct_n(0, face_index.ntotal)
face_labels = cluster_faces(face_embeddings)

# Update face clusters in summary
face_clusters = defaultdict(list)
for i, label in enumerate(face_labels):
    face_clusters[label].append(face_metadata[i])

summary['face_clusters'] = [
    {
        'cluster_id': f'cluster_{label}',
        'faces': cluster
    } for label, cluster in face_clusters.items()
]

# Visualize face clusters
st.subheader("Face Cluster Visualization")
fig = plot_face_clusters(face_embeddings, face_labels, face_metadata)
st.plotly_chart(fig)

# Search functionality
st.header("Search")

search_type = st.selectbox("Select search type", ["Combined", "Face"])

if search_type == "Combined":
    search_method = st.radio("Choose search method", ["Text", "Image"])
    
    if search_method == "Text":
        query = st.text_input("Enter your search query")
        if st.button("Search"):
            results = combined_search(query, text_index, image_index, text_metadata, image_metadata, text_model, image_model)
            st.subheader("Search Results")
            for result in results:
                st.write(f"Type: {result['type']}, Time: {result['data']['start']:.2f}s - {result['data']['end']:.2f}s, Distance: {result['distance']:.4f}")
                if 'text' in result['data']:
                    st.write(f"Text: {result['data']['text']}")
                clip_path = create_video_clip(video_path, result['data']['start'], result['data']['end'], f"temp_clip_{result['data']['start']}.mp4")
                st.video(clip_path)
                st.write("---")
    else:
        uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
        if uploaded_file is not None:
            image = Image.open(uploaded_file)
            st.image(image, caption="Uploaded Image", use_column_width=True)
            if st.button("Search"):
                results = combined_search(image, text_index, image_index, text_metadata, image_metadata, text_model, image_model)
                st.subheader("Image Search Results")
                for result in results:
                    st.write(f"Type: {result['type']}, Time: {result['data']['start']:.2f}s - {result['data']['end']:.2f}s, Distance: {result['distance']:.4f}")
                    clip_path = create_video_clip(video_path, result['data']['start'], result['data']['end'], f"temp_clip_{result['data']['start']}.mp4")
                    st.video(clip_path)
                    st.write("---")

elif search_type == "Face":
    face_search_type = st.radio("Choose face search method", ["Select from clusters", "Upload image"])
    
    if face_search_type == "Select from clusters":
        cluster_id = st.selectbox("Select a face cluster", [f'cluster_{label}' for label in set(face_labels) if label != -1])
        if st.button("Search"):
            selected_cluster = next(cluster for cluster in summary['face_clusters'] if cluster['cluster_id'] == cluster_id)
            st.subheader("Face Cluster Search Results")
            for face in selected_cluster['faces']:
                st.write(f"Time: {face['start']:.2f}s - {face['end']:.2f}s")
                clip_path = create_video_clip(video_path, face['start'], face['end'], f"temp_face_clip_{face['start']}.mp4")
                st.video(clip_path)
                st.write("---")
    else:
        uploaded_file = st.file_uploader("Choose a face image...", type=["jpg", "jpeg", "png"])
        if uploaded_file is not None:
            image = Image.open(uploaded_file)
            st.image(image, caption="Uploaded Image", use_column_width=True)
            if st.button("Search"):
                face_embedding = detect_and_embed_face(image, face_app)
                if face_embedding is not None:
                    face_results, face_distances = face_search(face_embedding, face_index, face_metadata)
                    st.subheader("Face Search Results")
                    for result, distance in zip(face_results, face_distances):
                        st.write(f"Time: {result['start']:.2f}s - {result['end']:.2f}s, Distance: {distance:.4f}")
                        clip_path = create_video_clip(video_path, result['start'], result['end'], f"temp_face_clip_{result['start']}.mp4")
                        st.video(clip_path)
                        st.write("---")
                else:
                    st.error("No face detected in the uploaded image. Please try another image.")