Running 2 ML models in the same page


Hello! I’m trying to create a single-page app that runs 2 ML models on the same input data. For example, 2 Random Forest models, each with a different number of trees. When I execute the predict() method for both models and print the results, the results for the first model don’t match what I get when running the program in my python console.

Steps to reproduce

Code snippet:

import numpy as np
import pandas as pd
import streamlit as st

from sklearn.datasets import fetch_california_housing
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor

def load_data():
    california = fetch_california_housing()
    x =
    y = * 100000
    x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=0)
    return x_train, x_test, y_train, y_test

X_train, X_test, Y_train, Y_test = load_data()

def show_predict_page():
    st.title(f"California Housing Price Prediction")
    st.write("""### Enter House Features""")

    med_inc = st.slider("Median Income", min_value=int(np.min(X_train[:, 0])), max_value=int(np.max(X_train[:, 0])))
    house_age = st.slider("House Age", min_value=int(np.min(X_train[:, 1])), max_value=int(np.max(X_train[:, 1])))
    ave_rooms = st.slider("Avg Rooms", min_value=int(np.min(X_train[:, 2])), max_value=int(np.max(X_train[:, 2])))
    ave_bedrms = st.slider("Avg Bedrooms", min_value=int(np.min(X_train[:, 3])), max_value=int(np.max(X_train[:, 3])))
    population = st.slider("Population", min_value=int(np.min(X_train[:, 4])), max_value=int(np.max(X_train[:, 4])))
    ave_occup = st.slider("Average Occupancy", min_value=int(np.min(X_train[:, 5])), max_value=int(np.max(X_train[:, 5])))
    latitude = st.slider("Latitude", min_value=int(np.min(X_train[:, 6])), max_value=int(np.max(X_train[:, 6])))
    longitude = st.slider("Longitude", min_value=int(np.min(X_train[:, 7])), max_value=int(np.max(X_train[:, 7])))

    predict_price = st.button("Predict Price")
    if predict_price:
        pred_json = {
            'MedInc': med_inc,
            'HouseAge': house_age,
            'AveRooms': ave_rooms,
            'AveBedrms': ave_bedrms,
            'Population': population,
            'AveOccup': ave_occup,
            'Latitude': latitude,
            'longitude': longitude

        pred_json_df = pd.DataFrame([pred_json])

        def load_model(num_trees, random_state):
            model = RandomForestRegressor(n_estimators=num_trees, random_state=random_state)
  , Y_train)
            return model

        rf_50_trees = load_model(50, 0)
        rf_50_trees_price = rf_50_trees.predict(pred_json_df.values)
        st.write("Random Forest (50 Trees)", rf_50_trees_price[0])  # Should be 293976.22.

        rf_100_trees = load_model(100, 0)
        rf_100_trees_price = rf_100_trees.predict(pred_json_df.values)
        st.write("Random Forest (100 Trees)", rf_100_trees_price[0])  # Should be 285688.19

I’ve experimented with different caching options (like @st.cache_resource) when loading the 2 models but can’t get it to work. Could you please provide an example of how to run two ML models within the same single-page app?

Thank you so much! Streamlit is an absolutely AMAZING framework!

Could you post a minimal reproducible code. Something that we can run and test.

There is also a guide on how to request help with solving an issue.

I doubt that your code snippet is even executable because this function call does not contain any parameters.
If it does, that’s the problem.
Also, I would use @st.cache_resource to load a model.

I don’t think your issue is related to running two models instead of just one.

What if you only execute the predict() method for the first model and print the results? Do the results match?

Additionally, you might need to ensure the salts / random seeds for the RandomForest are identical in every environment in which you run your models. Else, there will be stochasticity in the output.

Thank you for responding, I really appreciate it! Unfortunately, I can’t share the exact model I’m using. It’s a custom model that follows the sklearn API. I updated the code snippet above to match my app’s structure as closely as possible (swapping in a sklearn RandomForest for my custom model). Interestingly, this works using sklearn’s RandomForest object. There must be something with how my object is being cached. I did notice a strange behavior where, when hitting the “Predict Price” button in my app (without changing any of the inputs), the prediction for the first model changes while the prediction for the 2nd model stays the same. This seems strange since the first model is cached and nothing changed as far as I can tell.

I ran your code in a streamlit app and got the expected result:

Random Forest (50 Trees) 293976.22
Random Forest (100 Trees) 285688.19

Then I changed it so that it could run without streamlit, same result:

Random Forest (50 Trees) 293976.22
Random Forest (100 Trees) 285688.19