Create app.py

app.py

@@ -0,0 +1,38 @@
+import json
+import ffmpeg
+from subprocess import run
+import gradio as gr
+import whisper_timestamped as whisper
+from transformers import pipeline
+
+model = whisper.load_model("model/small.pt", device="cpu")
+sentiment_analysis = pipeline("sentiment-analysis", framework="pt", model="SamLowe/roberta-base-go_emotions", use_fast=True)
+
+def analyze_sentiment(text):
+    results = sentiment_analysis(text)
+    sentiment_results = {result['label']: result['score'] for result in results}
+    return sentiment_results
+
+
+def transcribe(audio):
+    audio = whisper.load_audio(audio)
+    result = whisper.transcribe(model, audio)
+    print(json.dumps(result, indent=2, ensure_ascii=False))
+    sent_res = analyze_sentiment(result.text)
+
+    return sent_res
+
+
+def video_to_audio(input_video, output_audio):
+    audio_file = f"test.wav"
+    run(["ffmpeg", "-i", 'test_video_1.mp4', audio_file])
+
+    response = transcribe(audio=audio_file)
+
+    return response
+
+gr.Interface(
+    fn=video_to_audio,
+    inputs=gr.Video(),
+    outputs=gr.Textbox()
+).launch()