Update requirements.txt

requirements.txt

@@ -1,36 +1,4 @@
-import joblib import pandas as pd import streamlit as st
-
-model = joblib.load("daimondx.joblib") unique_values = joblib.load("unique_values (1).joblib")
-
-unique_cut = unique_values["cut"] unique_color = unique_values["color"] unique_clarity = unique_values["clarity"]
-
-def main(): st.title("Diamond Prices")
-
-with st.form("questionaire"):
-    carat = st.slider("Carat",min_value=0.00,max_value=5.00) 
-    cut = st.selectbox("Cut", options=unique_cut)
-    color = st.selectbox("Color", options=unique_color)
-    clarity = st.selectbox("Clarity", options=unique_clarity) 
-    depth = st.slider("Depth",min_value=0.00,max_value=100.00)
-    table = st.slider("table",min_value=0.00,max_value=100.00)
-    x = st.slider("length(mm)",min_value=0.01,max_value=10.00) 
-    y = st.slider("width(mm)",min_value=0.01,max_value=10.00)
-    z = st.slider("depth(mm)",min_value=0.01,max_value=10.00)
-
-
-    # clicked==True only when the button is clicked
-    clicked = st.form_submit_button("Predict Price")
-    if clicked:
-        result=model.predict(pd.DataFrame({"carat": [carat],
-                                           "cut": [cut],
-                                           "color": [color],
-                                           "clarity": [clarity],
-                                           "depth":[depth],
-                                           "table": [table],
-                                           "size": [size],
-                                           "length(mm)":[x],
-                                           "width(mm)":[y],
-                                           "depth(mm)":[z]}))
-        # Show prediction
-        st.success("Your predicted income is"+result)
-if name == "main": main()
+joblib
+sklearn
+pandas
+xgboost