Update requirements.txt

requirements.txt

@@ -1,4 +1,39 @@
-joblib
-scikit-learn==1.0.2
-pandas 
-LinearRegression
+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()