Update app.py

app.py

@@ -1,61 +1,121 @@
 import os
 import streamlit as st
-from pytube import YouTube
 from moviepy.video.io.VideoFileClip import VideoFileClip
 from pydub import AudioSegment
 import whisper
 from transformers import pipeline, MarianMTModel, MarianTokenizer
+import yt_dlp as youtube_dl
 
 
 # App Configuration
-st.set_page_config(page_title="Video-to-Text Summarizer", layout="centered")
+st.set_page_config(page_title="Video-to-Text Summarization", layout="centered")
 
-# Welcome Page
+# Header
 st.title("🎥 Video-to-Text Summarization App")
 st.markdown("""
 This app helps you:
 - Convert videos into text and summarize them.
 - Extract multilingual transcriptions and translations.
 - Process videos with multiple speakers.
-
-Navigate using the sidebar!
 """)
 
-# Sidebar for Navigation
-st.sidebar.header("Navigation")
-nav_option = st.sidebar.radio("Go to", ["Welcome", "Upload Video", "Process Video", "Results"])
-
-# Initialize session state for storing video path
+# Temporary video storage
 if "video_path" not in st.session_state:
     st.session_state.video_path = None
 
-# Upload Video Section
-if nav_option == "Upload Video":
-    st.header("Upload Your Video")
-    upload_option = st.selectbox("Select Upload Method", ["Local", "YouTube URL"])
-
-    if upload_option == "Local":
-        video_file = st.file_uploader("Upload your video file", type=["mp4", "mkv", "avi"])
-        if video_file:
-            with open("uploaded_video.mp4", "wb") as f:
-                f.write(video_file.read())
-            st.session_state.video_path = "uploaded_video.mp4"
-            st.success("Video uploaded successfully!")
-
-    elif upload_option == "YouTube URL":
-        youtube_url = st.text_input("Enter YouTube URL")
-        if youtube_url:
+# 1. Upload Video Section
+st.header("Upload Your Video")
+
+# Choose upload option
+upload_option = st.selectbox("Select Upload Method", ["Local", "YouTube URL"])
+
+# Upload Local File
+if upload_option == "Local":
+    video_file = st.file_uploader("Upload your video file", type=["mp4", "mkv", "avi"])
+    if video_file:
+        with open("uploaded_video.mp4", "wb") as f:
+            f.write(video_file.read())
+        st.session_state.video_path = "uploaded_video.mp4"
+        st.success("Video uploaded successfully!")
+
+# Download Video from YouTube
+elif upload_option == "YouTube URL":
+    youtube_url = st.text_input("Enter YouTube URL")
+    if youtube_url:
+        try:
             os.system(f"yt-dlp -o video.mp4 {youtube_url}")
             st.session_state.video_path = "video.mp4"
             st.success("YouTube video downloaded successfully!")
+        except Exception as e:
+            st.error(f"Error downloading video: {str(e)}")
 
-# Process Video Section
-if nav_option == "Process Video" and st.session_state.video_path:
+# 2. Process Video Section (After Upload)
+if st.session_state.video_path:
     st.header("Process Your Video")
+    st.write(f"Processing {st.session_state.video_path}...")
 
-    # Video Chunking (Optional)
-    def chunk_video(video_path, chunk_duration=300):
+    # Extract Audio from Video
+    def extract_audio(video_path):
         try:
-            video = VideoFileClip(video_path)
-            total_duration = int(video.duration)
-            for start in range(0
+            audio = AudioSegment.from_file(video_path)
+            audio.export("extracted_audio.mp3", format="mp3")
+            st.success("Audio extracted successfully!")
+            return "extracted_audio.mp3"
+        except Exception as e:
+            st.error(f"Error in extracting audio: {str(e)}")
+            return None
+
+    audio_path = extract_audio(st.session_state.video_path)
+
+    # Real-time Audio Transcription
+    def transcribe_audio(audio_path):
+        try:
+            model = whisper.load_model("base")
+            result = model.transcribe(audio_path)
+            st.text_area("Transcription", result['text'], height=200)
+            return result['text']
+        except Exception as e:
+            st.error(f"Error in transcription: {str(e)}")
+            return None
+
+    if audio_path:
+        transcription = transcribe_audio(audio_path)
+
+# 3. Summarize and Translate
+if 'transcription' in locals():
+    st.header("Results")
+
+    # Summarize Text
+    def summarize_text(text):
+        try:
+            summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
+            summary = summarizer(text, max_length=150, min_length=30, do_sample=False)
+            st.text_area("Summary", summary[0]['summary_text'], height=150)
+            return summary[0]['summary_text']
+        except Exception as e:
+            st.error(f"Error in summarization: {str(e)}")
+            return None
+
+    summary = summarize_text(transcription)
+
+    # Translate Text
+    def translate_text(text, src_lang="en", tgt_lang="es"):
+        try:
+            model_name = f"Helsinki-NLP/opus-mt-{src_lang}-{tgt_lang}"
+            tokenizer = MarianTokenizer.from_pretrained(model_name)
+            model = MarianMTModel.from_pretrained(model_name)
+            translated = model.generate(**tokenizer(text, return_tensors="pt", padding=True))
+            translated_text = tokenizer.decode(translated[0], skip_special_tokens=True)
+            st.text_area("Translated Summary", translated_text, height=150)
+            return translated_text
+        except Exception as e:
+            st.error(f"Error in translation: {str(e)}")
+            return None
+
+    target_language = st.selectbox("Select Translation Language", ["es", "fr", "de", "zh"])
+    if target_language:
+        translated_summary = translate_text(summary, tgt_lang=target_language)
+
+else:
+    st.info("Please upload a video to start the process.")
+