Update app.py

app.py

@@ -1,49 +1,17 @@
-import joblib
-import pandas as pd
 import streamlit as st
+from transformers import pipeline
 
-
-
-model = joblib.load('model_a.joblib')
-unique_values = unique_values = joblib.load('unique_values.joblib')
-    
-unique_store_city =  unique_values["store_city"]
-unique_promotion_name=  unique_values["promotion_name"]
-unique_media_type =  unique_values["media_type"]
-unique_store_type =  unique_values["store_type"]
-unique_sales_country=  unique_values["sales_country"]
-unique_store_state =  unique_values["store_state"]
-
+classifier = pipeline("text-generation", model="distilbert-base-uncased-finetuned-sst-2-english")
 def main():
-    st.title("Adult Income")
-
-    with st.form("questionaire"):
-        store_sqft = st.slider('store_sqft',min_value=10,max_value=100)
-        store_city = st.selectbox('store_city',options=unique_store_city)
-        promotion_name = st.selectbox('promotion_name',options=unique_promotion_name)
-        media_type  = st.selectbox('media_type ',options=unique_media_type)
-        store_type = st.selectbox('Occupation',options=unique_store_type)
-        sales_country = st.selectbox('Relationship',options=sales_country)
-        store_state = st.selectbox('sales_country',options=unique_store_state)
-        meat_sqft = st.slider('meat_sqft',min_value=1,max_value=100)
-        frozen_sqft = st.slider('frozen_sqft',min_value=1,max_value=100)
+    st.title("text-generation")
 
+    with st.form("text_field"):
+        text = st.text_area('enter some text:')
         # clicked==True only when the button is clicked
-        clicked = st.form_submit_button("Predict income")
+        clicked = st.form_submit_button("Submit")
         if clicked:
-            result=model.predict(pd.DataFrame({"store_sqft": [store_sqft],
-                                               "store_city": [store_city],
-                                               "promotion_name": [promotion_name],
-                                               "media_type": [media_type],
-                                               "store_type ": [store_type ],
-                                               "sales_country": [sales_country],
-                                               "store_state": [store_state],
-                                               "meat_sqft": [meat_sqft],
-                                               "frozen_sqft": [frozen_sqft]}))
+          results = classifier([text])
+          st.json(results)
 
-            # Show prediction
-           
-            st.success('Your Predicted income is' + result)
-# Run main()
 if __name__ == "__main__":
     main()