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@@ -8,6 +8,6 @@ RUN pip install -r requirements.txt
 
 COPY . /app
 
-EXPOSE 8008
+EXPOSE 8511
 
-CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8008"]
+ENTRYPOINT ["streamlit","run","app.py"]

app.py

@@ -0,0 +1,28 @@
+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

@@ -1,6 +1,4 @@
 pandas==1.4.2
-uvicorn==0.18.3
-pydantic==1.10.2
-pickle-mixin==1.0.2
-fastapi==0.85.1
-scikit-learn==1.1.2
+scikit-learn==1.1.2
+streamlit==1.14.0
+requests==2.27.1