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Create app.py
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from transformers import pipeline
import streamlit as st
classifier = pipeline("text-classification", model="rxh1/Finetune_2")
text2text = pipeline("text2text-generation", model="facebook/blenderbot_small-90M")
# Streamlit application title
st.title("Text Sentiment Classification and Response Generation")
st.write("Create auto reply for three sentiment: positive, neutral, negative")
# Text input for user to enter the text to classify
text = st.text_area("Enter the text to reply", "")
# Perform text classification when the user clicks the "Classify" button
if st.button("Reply"):
# Perform text classification on the input text
result = classifier(text)[0]
# Display the classification result
prediction = result['label']
st.write("Text:", text)
st.write("Sentiment:", prediction)
# Generate a response based on the classification result
if prediction == "negative":
answer = text2text(f"You are the owner of Starbucks and I am the customer and my feeling sentiment is bad.")[0]["generated_text"]
elif prediction == "neutral":
answer = text2text(f"You are the owner of Starbucks and I am the customer and my feeling sentiment is peaceful.")[0]["generated_text"]
else:
answer = text2text(f"You are the owner of Starbucks and I am the customer and my feeling sentiment is good.")[0]["generated_text"]
st.write("Response:", answer)