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app.py

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+import streamlit as st
+import pandas as pd
+import requests
+import json
+
+st.write(""" # Mobile Price-Range Prediction""")
+st.sidebar.header("Choose Phone Specs")
+
+def input_features():
+    battery_power = st.sidebar.slider("battery power",500,2000,1000)
+    pix_height = st.sidebar.slider("pix height",0,2000,1000)
+    pix_width = st.sidebar.slider("pix width",0,2000,1000)
+    ram = st.sidebar.slider("ram",400,4000,2000)
+    data = {"battery_power":battery_power,
+            "px_height":pix_height,
+            "px_width":pix_width,
+            "ram":ram}
+    feats = pd.DataFrame(data,index=[0])
+    return feats, data
+
+feats, data = input_features()
+
+st.subheader("Phone Specs")
+st.write(feats)
+
+if st.button("Result"):
+    res = requests.post(url = "http://0.0.0.0:8008/predict", data=json.dumps(data)).text
+    st.subheader(f"Predicted Price Range: {res}")

requirements.txt

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+pandas==1.4.2
+scikit-learn==1.1.2
+streamlit==1.14.0
+requests==2.27.1