Update app.py

app.py

@@ -3,75 +3,91 @@ from transformers import pipeline
 import tempfile
 import torch
 import os
+import shutil
 from moviepy.editor import VideoFileClip
 import srt
 import datetime
 
-# Load Hugging Face models
+# Select CPU or GPU
 device = 0 if torch.cuda.is_available() else -1
+
+# Load Hugging Face pipelines
 whisper = pipeline("automatic-speech-recognition", model="openai/whisper-large", device=device)
 punctuate = pipeline("text2text-generation", model="vennify/t5-base-grammar-correction")
 summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
 
-# Utility: Extract audio
+# Extract audio from uploaded video file
 def extract_audio(video_path):
     video = VideoFileClip(video_path)
     audio_path = tempfile.mktemp(suffix=".wav")
     video.audio.write_audiofile(audio_path, verbose=False, logger=None)
     return audio_path
 
-# Utility: Create .srt subtitles
+# Generate basic subtitle file
 def generate_srt(transcript_text):
     lines = transcript_text.strip().split(". ")
     subs = []
     for i, line in enumerate(lines):
-        start = datetime.timedelta(seconds=i*2)
-        end = datetime.timedelta(seconds=(i+1)*2)
-        subs.append(srt.Subtitle(index=i+1, start=start, end=end, content=line.strip()))
+        start = datetime.timedelta(seconds=i * 2)
+        end = datetime.timedelta(seconds=(i + 1) * 2)
+        subs.append(srt.Subtitle(index=i + 1, start=start, end=end, content=line.strip()))
     srt_data = srt.compose(subs)
     srt_path = tempfile.mktemp(suffix=".srt")
     with open(srt_path, "w") as f:
         f.write(srt_data)
     return srt_path
 
-# Full pipeline
+# Main pipeline
 def transcribe_pipeline(video_file):
-    with tempfile.NamedTemporaryFile(delete=False, suffix=".mp4") as tmp:
-        tmp.write(video_file.read())
-        video_path = tmp.name
+    try:
+        # Copy the uploaded file path to a temp location
+        video_path = tempfile.mktemp(suffix=".mp4")
+        shutil.copy(video_file, video_path)
+
+        # Extract audio from video
+        audio_path = extract_audio(video_path)
+
+        # Transcribe with Whisper
+        result = whisper(audio_path)
+        raw_text = result["text"]
 
-    audio_path = extract_audio(video_path)
-    result = whisper(audio_path)
-    raw_text = result["text"]
+        # Add punctuation
+        punctuated = punctuate(raw_text)[0]["generated_text"]
 
-    punctuated = punctuate(raw_text)[0]["generated_text"]
-    summary = summarizer(punctuated, max_length=130, min_length=30, do_sample=False)[0]['summary_text']
-    srt_path = generate_srt(punctuated)
+        # Summarize
+        summary = summarizer(punctuated, max_length=130, min_length=30, do_sample=False)[0]["summary_text"]
 
-    # Save raw text, punctuated text, and summary
-    raw_txt_path = tempfile.mktemp(suffix=".txt")
-    punct_txt_path = tempfile.mktemp(suffix=".txt")
-    summary_txt_path = tempfile.mktemp(suffix=".txt")
+        # Generate subtitle file
+        srt_path = generate_srt(punctuated)
 
-    with open(raw_txt_path, "w") as f:
-        f.write(raw_text)
-    with open(punct_txt_path, "w") as f:
-        f.write(punctuated)
-    with open(summary_txt_path, "w") as f:
-        f.write(summary)
+        # Save files for download
+        raw_txt_path = tempfile.mktemp(suffix=".txt")
+        punct_txt_path = tempfile.mktemp(suffix=".txt")
+        summary_txt_path = tempfile.mktemp(suffix=".txt")
 
-    return raw_text, punctuated, summary, punct_txt_path, summary_txt_path, srt_path
+        with open(raw_txt_path, "w") as f:
+            f.write(raw_text)
+        with open(punct_txt_path, "w") as f:
+            f.write(punctuated)
+        with open(summary_txt_path, "w") as f:
+            f.write(summary)
+
+        return raw_text, punctuated, summary, punct_txt_path, summary_txt_path, srt_path
+
+    except Exception as e:
+        print("❌ Pipeline Error:", e)
+        return "Error", "Error", "Error", None, None, None
 
 # Gradio UI
 with gr.Blocks(theme=gr.themes.Soft()) as iface:
     gr.Markdown("# 🎥 EchoScribe: Smart Video Transcriber")
-    gr.Markdown("Upload a video to extract transcript with punctuation and summary using Hugging Face models. Powered by Whisper, BART, and punctuation restoration.")
+    gr.Markdown("Upload a video to extract transcript, add punctuation, and generate a summary. You can also download the .srt subtitle file.")
 
     with gr.Row():
         video_input = gr.Video(label="🎬 Upload your video")
 
     with gr.Row():
-        raw_output = gr.Textbox(label="🧾 Raw Transcript (Whisper)", lines=6)
+        raw_output = gr.Textbox(label="🧾 Raw Transcript", lines=6)
         punct_output = gr.Textbox(label="📄 Punctuated Transcript", lines=6)
 
     summary_output = gr.Textbox(label="📝 Summary", lines=4)
@@ -83,14 +99,20 @@ with gr.Blocks(theme=gr.themes.Soft()) as iface:
 
     submit_btn = gr.Button("🚀 Transcribe & Summarize")
 
-    def run_all(video):
-        return transcribe_pipeline(video)
-
-    submit_btn.click(fn=run_all, inputs=video_input,
-                     outputs=[raw_output, punct_output, summary_output,
-                              download_transcript, download_summary, download_srt])
+    submit_btn.click(
+        fn=transcribe_pipeline,
+        inputs=video_input,
+        outputs=[
+            raw_output,
+            punct_output,
+            summary_output,
+            download_transcript,
+            download_summary,
+            download_srt,
+        ],
+    )
 
     gr.Markdown("---")
-    gr.Markdown("🔧 Built with ❤️ by **Snigdha’s AI Lab**")
+    gr.Markdown("Built with ❤️ by Snigdha’s AI Lab")
 
-iface.launch()
+iface.launch()