Create app.py

app.py

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+# https://www.youtube.com/watch?v=SYHPQ0rXzWM
+
+import yt_dlp
+import gradio as gr
+from transformers import BertTokenizer, BertForSequenceClassification
+import torch
+
+# Load the fine-tuned BERT model and tokenizer
+model = BertForSequenceClassification.from_pretrained('./results/checkpoint-150')
+tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
+model.eval()
+
+import yt_dlp
+
+def download_youtube_video(video_url, output_path='Youtube Videos/Recent.mp4'):
+    ydl_opts = {'format': 'bestaudio/best', 'outtmpl': output_path}
+    with yt_dlp.YoutubeDL(ydl_opts) as ydl:
+        ydl.download([video_url])
+
+    return output_path
+
+from pydub import AudioSegment
+
+def mp4_to_wav(mp4_path):
+    # Convert MP4 to WAV
+    sound = AudioSegment.from_file(mp4_path)
+    wav_path = "Audios/pydub_output.wav"
+    sound.export(wav_path, format="wav")
+    return wav_path
+
+import speech_recognition as sr
+
+def transcribe_audio(audio_path):
+
+    # Transcribe audio
+    recognizer = sr.Recognizer()
+    with sr.AudioFile(audio_path) as source:
+        audio = recognizer.record(source)
+    text = recognizer.recognize_google(audio)
+    
+    return text
+
+# Function to classify the transcription
+def classify_transcription(transcription):
+    inputs = tokenizer(transcription, truncation=True, padding=True, max_length=512, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model(**inputs)
+        logits = outputs.logits
+        predicted_class = torch.argmax(logits, dim=1).item()
+    return 'positive' if predicted_class == 1 else 'negative'
+
+# Complete pipeline function
+def classify_youtube_video(video_url):
+    video_mp4_path = download_youtube_video(video_url)
+    audio_wav_path = mp4_to_wav(video_mp4_path)
+    transcription = transcribe_audio(audio_wav_path)
+    sentiment = classify_transcription(transcription)
+    return transcription, sentiment
+
+# Create Gradio Interface
+interface = gr.Interface(
+    fn=classify_youtube_video,
+    inputs=["text"],
+    outputs=["text", "text"],
+    title="YouTube Video Sentiment Classifier",
+    description="Input a YouTube video URL to transcribe the audio and classify the sentiment as positive or negative."
+)
+
+# Launch the interface
+interface.launch(inline=False)