Update app.py

app.py

@@ -2,10 +2,21 @@ import os
 os.system("pip install git+https://github.com/openai/whisper.git")
 import gradio as gr
 import whisper
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+from transformers import pipeline
 
+
+#call tokenizer and NLP model for text classification
+tokenizer = AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment-latest")
+model_nlp = AutoModelForSequenceClassification.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment-latest")
+config = AutoConfig.from_pretrained(model_nlp)
+
+# call whisper model for audio/speech processing
 model = whisper.load_model("small")
 
-def inference(audio):
+
+
+def inference_audio(audio):
     audio = whisper.load_audio(audio)
     audio = whisper.pad_or_trim(audio)
     
@@ -19,6 +30,15 @@ def inference(audio):
     print(result.text)
     return result.text, gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)
 
+def inference_text(audio):
+    text,_,_,_ =inference_audio(audio)
+
+    sentiment_task = pipeline("sentiment-analysis", model=model_nlp, tokenizer=tokenizer)
+    result=sentiment_task(text)
+    return result
+
+
+
 block = gr.Blocks()
 with block:
     with gr.Group():
@@ -37,9 +57,14 @@ with block:
 
 
         
-        btn.click(inference, inputs=[audio], outputs=[text])
+        btn.click(inference_text, inputs=[audio], outputs=[text])
 
 
 block.launch()
 
 
+
+from transformers import pipeline
+sentiment_task = pipeline("sentiment-analysis", model=model_path, tokenizer=model_path)
+sentiment_task("Covid cases are increasing fast!")
+