Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,40 @@
-import altair as alt
 import numpy as np
 import pandas as pd
-import streamlit as st
-
-"""
-# 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)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-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])),
-    ))
+import streamlit as st 
+import joblib
+from apify_client import ApifyClient
+model = joblib.load("classifier.pkl")
+client = ApifyClient("apify_api_nscRkHOyMh3mytIWftXpHpZlIzBhgF4mZyPV")
+st.title("Fake Instagram Profile Detection")
+st.write("Plaese provide instagram account details you would like to predict")
+n = st.text_input("Enter username ")
+run_input = { "usernames": [n] }
+run = client.actor("dSCLg0C3YEZ83HzYX").call(run_input=run_input)
+m = client.dataset(run["defaultDatasetId"])
+for item in m.iterate_items():
+        postsCount= item.get('postsCount')
+        followersCount = item.get('followersCount')
+        followsCount = item.get('followsCount')
+        private=item.get('private')
+        verified=item.get('verified')
+
+def predictor(postsCount,followersCount,followsCount,private,verified):
+    prediction = model.predict([[postsCount,followersCount,followsCount,private,verified]])
+    print(prediction)
+    return prediction
+
+
+if st.button("Predict"):
+    result = predictor(postsCount,followersCount,followsCount,private,verified)
+    st.write("The number of posts : " , postsCount)
+    st.write("The number of followers : " ,followersCount)
+    st.write("The number of following : " ,followsCount)
+    st.write("Private : " ,private)
+    st.write("Verified : " ,verified)
+    if postsCount == None:
+        st.error("The User Doesn't exist")
+    elif result == 0 and postsCount != None:
+        st.error("The Account is Likely to be Fake ")
+    else:
+
+        st.success("The Account is Likely to be Real")