u

app.py

@@ -1,78 +1,24 @@
 import streamlit as st
-from pytube import YouTube
-from moviepy.editor import *
-import torch
-from transformers import Wav2Vec2ForCTC, Wav2Vec2CTCTokenizer, pipeline, logging
-import librosa
+from transformers import pipeline, logging
 
 # Suppress warnings from transformers
 logging.set_verbosity_error()
 
 # Streamlit interface setup
-st.title("YouTube Video Summarizer")
-
-youtube_link = st.text_input("Enter YouTube Video Link:")
-
-if st.button('Summarize'):
-    if not youtube_link:
-        st.warning("Please enter a valid YouTube link.")
-    else:
-        with st.spinner("Processing..."):
-            try:
-                # Download YouTube Video
-                yt = YouTube(youtube_link)
-                video = yt.streams.first()
-                download_path = video.download(skip_existing=True)
-
-                # Show progress
-                st.progress(25)
-
-                # Extract Audio
-                video_clip = AudioFileClip(download_path)
-                audio_path = download_path.replace('.mp4', '.wav')
-                video_clip.write_audiofile(audio_path)
-
-                # Show progress
-                st.progress(50)
-
-                # Speech to Text
-                tokenizer = Wav2Vec2CTCTokenizer.from_pretrained("facebook/wav2vec2-base-960h")
-                model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h")
-
-                # Load and process the audio
-                speech, _ = librosa.load(audio_path, sr=16000)
-                input_values = tokenizer(speech, return_tensors="pt").input_values
-                logits = model(input_values).logits
-                predicted_ids = torch.argmax(logits, dim=-1)
-
-                # Decode the speech
-                transcription = tokenizer.batch_decode(predicted_ids, skip_special_tokens=True)[0]
-
-                # Ensure transcription is a string and not empty
-                if isinstance(transcription, str) and transcription.strip():
-                    st.write("Transcription:", transcription)  # For debugging
-
-                    # Show progress
-                    st.progress(75)
-
-                    # Initialize the summarizer
-                    summarizer = pipeline("summarization")
-
-                    # Summarization
-                    try:
-                        # Ensure the transcription is a string
-                        transcription_text = str(transcription)
-                        summary = summarizer(transcription_text, max_length=130, min_length=30, do_sample=False)
-                        st.success("Done!")
-                        st.write("### Summary:")
-                        st.write(summary[0]['summary_text'])
-
-                        # Final progress
-                        st.progress(100)
-                    except Exception as summarization_error:
-                        st.error(f"Error during summarization: {summarization_error}")
-                else:
-                    st.error("Could not transcribe audio or transcription is empty.")
-
-            except Exception as general_error:
-                st.error(f"An error occurred: {general_error}")
+st.title("Summarization Test")
+
+# Initialize the summarizer
+summarizer = pipeline("summarization")
+
+# Test summarization with a hardcoded string
+test_text = "This is a simple test sentence to verify the functionality of the summarization pipeline. The goal is to ensure that the pipeline can process input text correctly and produce a summary without encountering the input type error."
+
+if st.button('Test Summarization'):
+    try:
+        # Attempt to summarize the hardcoded test text
+        summary = summarizer(test_text, max_length=130, min_length=30, do_sample=False)
+        st.success("Summarization succeeded!")
+        st.write("### Summary:")
+        st.write(summary[0]['summary_text'])
+    except Exception as e:
+        st.error(f"Summarization test failed: {e}")