import streamlit as st from huggingface_hub import HfApi import os import json from datetime import datetime import cv2 import random from PIL import Image import io # Initialize the Hugging Face API with the token api = HfApi(token=os.getenv("HF_API_TOKEN")) def extract_thumbnail(video_path, thumbnail_path): video = cv2.VideoCapture(video_path) # Get total number of frames total_frames = int(video.get(cv2.CAP_PROP_FRAME_COUNT)) # Choose a random frame random_frame = random.randint(0, total_frames - 1) # Set the frame position video.set(cv2.CAP_PROP_POS_FRAMES, random_frame) # Read the frame success, frame = video.read() if success: cv2.imwrite(thumbnail_path, frame) video.release() return success def save_custom_thumbnail(thumbnail_file, thumbnail_path): img = Image.open(thumbnail_file) img.save(thumbnail_path) return True def generate_metadata(video_name, title, description, uploader, file_location, thumbnail_location): return { "fileName": video_name, "title": title, "description": description, "uploader": uploader, "uploadTimestamp": datetime.now().isoformat(), "fileLocation": file_location, "thumbnailLocation": thumbnail_location, "views": 0, "likes": 0 } def upload_video_to_hf(video_file, video_name, title, description, uploader, custom_thumbnail=None): # Create temp paths temp_dir = "temp" if not os.path.exists(temp_dir): os.makedirs(temp_dir) video_path = os.path.join(temp_dir, video_name) thumbnail_name = f"{os.path.splitext(video_name)[0]}_thumb.jpg" thumbnail_path = os.path.join(temp_dir, thumbnail_name) json_name = f"{os.path.splitext(video_name)[0]}-index.json" json_path = os.path.join(temp_dir, json_name) # Write the video content to a file with open(video_path, "wb") as f: f.write(video_file.read()) # Handle thumbnail if custom_thumbnail: thumbnail_extracted = save_custom_thumbnail(custom_thumbnail, thumbnail_path) else: thumbnail_extracted = extract_thumbnail(video_path, thumbnail_path) if not thumbnail_extracted: st.error("Failed to process thumbnail") return None # Upload the video video_location = f"videos/{video_name}" api.upload_file( path_or_fileobj=video_path, path_in_repo=video_location, repo_id="vericudebuget/ok4231", repo_type="space", ) # Upload the thumbnail thumbnail_location = f"thumbnails/{thumbnail_name}" api.upload_file( path_or_fileobj=thumbnail_path, path_in_repo=thumbnail_location, repo_id="vericudebuget/ok4231", repo_type="space", ) # Generate and upload metadata JSON metadata = generate_metadata(video_name, title, description, uploader, video_location, thumbnail_location) with open(json_path, "w") as f: json.dump(metadata, f, indent=2) api.upload_file( path_or_fileobj=json_path, path_in_repo=f"metadata/{json_name}", repo_id="vericudebuget/ok4231", repo_type="space", ) # Cleanup temp files for file_path in [video_path, thumbnail_path, json_path]: if os.path.exists(file_path): os.remove(file_path) return metadata # Streamlit app interface st.title("Upload your video") st.markdown("---") # File uploader for video input uploaded_video = st.file_uploader("Choose video file", type=["mp4", "avi", "mov"]) if uploaded_video: # Show the video details form with st.form("video_details"): st.write("Video Details") title = st.text_input("Title", placeholder="Enter video title") description = st.text_area("Description", placeholder="Enter video description") uploader = st.text_input("Uploader Name", placeholder="Enter your name") # Optional custom thumbnail uploader custom_thumbnail = st.file_uploader("Upload custom thumbnail (optional)", type=["jpg", "jpeg", "png"]) # Upload button within the form submit_button = st.form_submit_button("Upload Video") if submit_button: if not title or not uploader: st.error("Please fill in the title and uploader name.") else: with st.spinner("Uploading video, generating thumbnail and metadata..."): metadata = upload_video_to_hf( uploaded_video, uploaded_video.name, title, description, uploader, custom_thumbnail ) if metadata: st.success("Upload completed successfully!") st.json(metadata) else: st.info("Please upload a video file to begin.")