Update app.py

app.py

@@ -1,95 +1,85 @@
+import gradio as gr 
+import pandas as pd 
+import yt_dlp
 import os
-import spaces
-import uuid
-import yt_dlp as youtube_dl
-from typing import Generator
-from faster_whisper import WhisperModel
-import pandas as pd
-from typing import Generator
-from faster_whisper import WhisperModel
-import pandas as pd
-import gradio as gr
+from transcriber import gen_csv()
+df = pd.read_csv("./final.csv")
+transcripts = df.to_dict(orient='records')
 
-class YouTubeTranscriber:
-    def __init__(self, model_path: str):
-        self.model = WhisperModel(model_path)
-        
-    def download_audio(self, url: str, preferred_quality: str = "192") -> str:
-        file_name = f"{uuid.uuid4()}"
-        output_path = os.path.join("/tmp", file_name)  # Use /tmp directory for temporary storage
+# Function to download video using yt-dlp and generate transcript HTML
+def download_video(youtube_url):
+    # Define download options
+    ydl_opts = {
+        'format': 'mp4',
+        'outtmpl': 'downloaded_video.%(ext)s',
+        'quiet': True
+    }
 
-        ydl_opts = {
-            'format': 'bestaudio/best',
-            'postprocessors': [{
-                'key': 'FFmpegExtractAudio',
-                'preferredcodec': 'mp3',
-                'preferredquality': preferred_quality,
-            }],
-            'outtmpl': output_path,  # Specify the output path and file name
-        }
+    # Extract video ID to check if already downloaded
+    with yt_dlp.YoutubeDL({'quiet': True}) as ydl:
+        info_dict = ydl.extract_info(youtube_url, download=False)
+        video_ext = info_dict.get('ext')
+        video_path = f'downloaded_video.mp4'
 
-        try:
-            with youtube_dl.YoutubeDL(ydl_opts) as ydl:
-                info_dict = ydl.extract_info(url, download=False)
-                video_title = info_dict.get('title', 'Unknown title')
-                print(f"Downloading audio for: {video_title}")
+    # Check if video already downloaded
+    if not os.path.exists(video_path):
+        # Download the video
+        with yt_dlp.YoutubeDL(ydl_opts) as ydl:
+            ydl.download([youtube_url])
 
-                ydl.download([url])
-                print(f"Audio file saved as: {output_path}")
-
-            return output_path
-
-        except youtube_dl.utils.DownloadError as e:
-            print(f"Error downloading audio: {e}")
-            return None
+    # Generate HTML for the transcript
+    transcript_html = ""
+    for t in transcripts:
+        transcript_html += f'<div class="transcript-block"><a href="#" onclick="var video = document.getElementById(\'video-player\').querySelector(\'video\'); video.currentTime={t["start_time"]}; return false;">' \
+                           f'[{t["start_time"]:.2f} - {t["end_time"]:.2f}]<br>{t["text"]}</a></div>'
+    
+    return video_path, transcript_html
 
-    def transcribe_audio(self, path: str) -> Generator:
-        print(f"Reading {path}")
-        segments, _ = self.model.transcribe(path)
-        return segments
+# Function to search the transcript
+def search_transcript(keyword):
+    search_results = ""
+    for t in transcripts:
+        if keyword.lower() in t['text'].lower():
+            search_results += f'<div class="transcript-block"><a href="#" onclick="var video = document.getElementById(\'video-player\').querySelector(\'video\'); video.currentTime={t["start_time"]}; return false;">' \
+                              f'[{t["start_time"]:.2f} - {t["end_time"]:.2f}]<br>{t["text"]}</a></div>'
+    return search_results
 
-    def process_segments(self, segments: Generator) -> pd.DataFrame:
-        result = []
-        for i, segment in enumerate(segments):
-            result.append({
-                'chunk_id': f"chunk_{i}",
-                'chunk_length': segment.end - segment.start,
-                'text': segment.text,
-                'start_time': segment.start,
-                'end_time': segment.end
-            })
+# CSS for styling
+css = """
+.fixed-video { width: 480px !important; height: 270px !important; }
+.fixed-transcript { width: 480px !important; height: 270px !important; overflow-y: auto; }
+.transcript-block { margin: 10px 0; padding: 10px; border: 1px solid #ddd; border-radius: 5px; background-color: #f9f9f9; }
+.transcript-block a { text-decoration: none; color: #007bff; }
+.transcript-block a:hover { text-decoration: underline; }
+"""
 
-        df = pd.DataFrame(result)
-        return df
+# Gradio interface
+with gr.Blocks(css=css) as demo:
+    gr.Markdown("# YouTube Video Player with Clickable Transcript")
 
-# Function to be called by the Gradio interface
-@spaces.GPU
-def transcribe_youtube_video(url: str, model_path: str = "distil-large-v2") -> str:
-    yt_transcriber = YouTubeTranscriber(model_path)
-    audio_path = yt_transcriber.download_audio(url)
+    with gr.Row():
+        youtube_url = gr.Textbox(label="YouTube URL", placeholder="Enter YouTube video link here")
+        download_button = gr.Button("Download and Display Transcript")
     
-    if audio_path:
-        segments = yt_transcriber.transcribe_audio(audio_path)
-        df = yt_transcriber.process_segments(segments)
-        output_csv = os.path.join("/tmp", f"{uuid.uuid4()}.csv")
-        df.to_csv(output_csv, index=False)
-        return output_csv
-    else:
-        return "Failed to download audio."
+    with gr.Row():
+        video = gr.Video(label="Video Player", elem_id="video-player", elem_classes="fixed-video")
+        transcript_display = gr.HTML(label="Transcript", elem_classes="fixed-transcript")
+
+    with gr.Row():
+        search_box = gr.Textbox(label="Search Transcript", placeholder="Enter keyword to search")
+        search_button = gr.Button("Search")
+        search_results_display = gr.HTML(label="Search Results", elem_classes="fixed-transcript")
 
+    # On button click, download the video and display the transcript
+    def display_transcript(youtube_url):
+        video_path, transcript_html = download_video(youtube_url)
+        # Ensure the video path is correctly passed to the Gradio video component
+        return video_path, transcript_html
 
-import gradio as gr
+    download_button.click(fn=display_transcript, inputs=youtube_url, outputs=[video, transcript_display])
 
-interface = gr.Interface(
-    fn=transcribe_youtube_video,
-    inputs=[
-        gr.Textbox(lines=1, placeholder="Enter YouTube URL here...", label="YouTube URL"),
-        gr.Textbox(lines=1, label="Whisper Model Path")
-    ],
-    outputs=gr.File(label="Transcribed Segments CSV"),  # Use gr.File directly
-    title="YouTube Audio Transcriber",
-    description="Enter a YouTube URL to download the audio and transcribe it using Whisper."
-)
+    # On search button click, search the transcript and display results
+    search_button.click(fn=search_transcript, inputs=search_box, outputs=search_results_display)
 
 # Launch the interface
-interface.launch()
+demo.launch()