Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,27 @@
-import altair as alt
-import numpy as np
+import pickle
 import pandas as pd
+import numpy as np
 import streamlit as st
+import sklearn
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
+model_file = "model.pkl"
+try:
+    with open(model_file,'rb') as file:
+        model = pickle.load(file)
+except FileNotFoundError:
+    st.error("The file was not found in the directory")
 
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
+st.title("FLower Classification using Streamlit on IRIS DATASET")
+st.header("Enter your flower features to get the classification prediction")
 
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
+sepal_length = st.number_input("Enter yuour sepal length") 
+sepal_width = st.number_input("Enter yuour sepal width")
+petal_length = st.number_input("Enter yuour petal length")
+petal_width = st.number_input("Enter yuour petal width")
 
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
+if st.button("PREDICT"):
+    features = np.array([[sepal_length,sepal_width,petal_length,petal_width]])
+    prediction = model.predict(features)[0]
 
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+    st.subheader("Prediction has been made")
+    st.write("Theprediction for your features is",predicton)