Create taps.py

taps.py

@@ -0,0 +1,47 @@
+from transformers import pipeline
+import gradio as gr
+import numpy as np
+
+def get_sentiment(text):
+  sentiment_pipeline=pipeline('sentiment-analysis')
+  result=sentiment_pipeline(text)
+  output = gr.Textbox(label="Output Box")
+
+  return result[0]['label'],result[0]['score']
+
+def summraztion(text):
+  summary_pipe = pipeline('summarization',model="cnicu/t5-small-booksum")
+  result=summary_pipe(text)
+  output = gr.Textbox(label="Output Box")
+
+  return result[0]['summary_text']
+
+def chat_bot(text,histroy):
+  chat_pip=pipeline('text-generation')
+  mes=chat_pip(text)
+    
+  return mes[0]['generated_text']
+
+    transcriber = pipeline("automatic-speech-recognition", model="openai/whisper-base.en")
+
+def transcribe(audio):
+    sr, y = audio
+    y = y.astype(np.float32)
+    y /= np.max(np.abs(y))
+
+    return transcriber({"sampling_rate": sr, "raw": y})["text"]
+
+Audio = gr.Interface(
+    transcribe,
+    gr.Audio(sources=["microphone"]),
+    "text",
+)
+
+sentiment_a = gr.Interface(fn=get_sentiment , inputs=gr.Textbox(label="Enter the review ") , outputs=[gr.Textbox(label="sentiment") , gr.Textbox(label="Score")],description='sentiment-analysis')
+summraztion = gr.Interface(fn=summraztion , inputs=gr.Textbox(label="Enter the text ") , outputs=gr.Textbox(label="summraztion") ,description='summraztion')
+chatBot=gr.ChatInterface(chat_bot)
+
+
+demo = gr.TabbedInterface([sentiment_a, summraztion,chatBot,Audio], ["Sentiment Analysis", "Summraztion","ChatBot",'Audio'])
+
+demo.launch(debug=True)