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import streamlit as st
import os
import cv2
import torch
from PIL import Image
from gtts import gTTS
from scenedetect import open_video, SceneManager, ContentDetector
from transformers import BlipProcessor, BlipForConditionalGeneration
from openai import OpenAI
# Load AI models
caption_processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
caption_model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
client = OpenAI(
api_key="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"
)
# Streamlit App UI
st.title("🎥 AI-Powered Video Summarization")
uploaded_file = st.file_uploader("📤 Upload a Video File", type=["mp4"])
if uploaded_file:
video_path = "input_video.mp4"
with open(video_path, "wb") as f:
f.write(uploaded_file.getbuffer())
st.video(video_path)
# Scene Detection & Frame Extraction
st.write("🔍 Detecting scene changes and extracting key frames...")
def extract_key_frames(video_path, output_folder="frames", frames_per_scene=3):
os.makedirs(output_folder, exist_ok=True)
video = open_video(video_path)
scene_manager = SceneManager()
scene_manager.add_detector(ContentDetector(threshold=27.0))
video.set_downscale_factor()
scene_manager.detect_scenes(video)
scenes = scene_manager.get_scene_list()
cap = cv2.VideoCapture(video_path)
for i, (start, end) in enumerate(scenes):
start_frame = start.get_frames()
end_frame = end.get_frames()
step = (end_frame - start_frame) // (frames_per_scene + 1)
for j in range(frames_per_scene):
frame_time = start_frame + step * (j + 1)
cap.set(cv2.CAP_PROP_POS_FRAMES, frame_time)
ret, frame = cap.read()
if ret:
frame_path = os.path.join(output_folder, f"scene_{i+1}_frame{j+1}.jpg")
cv2.imwrite(frame_path, frame)
cap.release()
extract_key_frames(video_path)
# Caption Generation
st.write("📝 Generating captions for extracted frames...")
def generate_caption(image_path):
image = Image.open(image_path).convert("RGB")
inputs = caption_processor(image, return_tensors="pt")
caption_ids = caption_model.generate(**inputs)
return caption_processor.decode(caption_ids[0], skip_special_tokens=True)
captions = []
for filename in sorted(os.listdir("frames")):
if filename.endswith(".jpg"):
image_path = os.path.join("frames", filename)
captions.append(generate_caption(image_path))
st.write("📄 Generated Captions:", captions)
# Summarization
st.write("📖 Summarizing captions using AI...")
def summarize_captions(captions):
prompt = f"Summarize the following sequence of video frames into a meaningful story:\n\n{captions}"
completion = client.chat.completions.create(
model="gpt-4o-mini",
messages=[{"role": "system", "content": "You are an AI that summarizes video content."},
{"role": "user", "content": prompt}]
)
return completion.choices[0].message.content
summary = summarize_captions(captions)
st.write("📌 Video Summary:", summary)
# Text-to-Speech
st.write("🔊 Generating voice narration...")
def text_to_speech(text, output_audio="summary_audio.mp3"):
tts = gTTS(text, lang="en")
tts.save(output_audio)
text_to_speech(summary)
st.audio('summary_audio.mp3')
# # Combine Audio & Video
# st.write("🎬 Merging audio with the video...")
# def add_audio_to_video(video_path, audio_path, output_video="final_video.mp4"):
# video = mp.VideoFileClip(video_path)
# audio = mp.AudioFileClip(audio_path)
# if audio.duration > video.duration:
# audio = audio.subclip(0, video.duration)
# final_video = video.set_audio(audio)
# final_video.write_videofile(output_video, codec="libx264", audio_codec="aac")
# add_audio_to_video(video_path, "summary_audio.mp3")
# st.video("final_video.mp4")
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