Create app.py

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)