Create app.py

app.py

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+import streamlit as st
+import os
+import cv2
+import torch
+import moviepy.editor as mp
+from PIL import Image
+from gtts import gTTS
+from scenedetect import open_video, SceneManager, ContentDetector
+from transformers import BlipProcessor, BlipForConditionalGeneration, AutoModelForCausalLM, AutoTokenizer
+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)
+
+    # 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")