Upload 3 files

app.py

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+import streamlit as st
+import pandas as pd
+import pickle
+
+with open('gb_model_best.pkl', 'rb') as f:
+    gb_model_best = pickle.load(f)
+
+# Create a Streamlit app
+st.title("Movie Revenue Predictor")
+
+# Create input fields for the user
+popularity = st.number_input("Popularity (out of 10):")
+runtime = st.number_input("Runtime (in minutes):")
+is_original_en = st.selectbox("Is the movie in English?", ["Yes", "No"])
+
+# Create a conditional input field for budget
+budget_known = st.selectbox("Is the budget known?", ["Yes", "No"])
+if budget_known == "Yes":
+    budget_M = st.number_input("Budget (in millions of dollars):")
+else:
+    budget_M = 10
+
+# Create a submit button
+submitted = st.button("Predict Revenue")
+
+# When the user submits the form, make a prediction
+if submitted:
+    # Create a DataFrame with the user's input data
+    input_data = pd.DataFrame({
+        "popularity": [popularity],
+        "runtime": [runtime],
+        "budget_known": [int(budget_known == "Yes")],
+        "budget_M": [budget_M],
+        "is_original_en": [int(is_original_en == "Yes")]
+    })
+
+    # Make a prediction using the trained model
+    input_data = input_data[gb_model_best.feature_names_in_]
+    prediction = gb_model_best.predict(input_data)[0]
+
+    # Display the predicted revenue
+    st.write(f"Predicted Revenue: ${prediction:.2f} million")

gb_model_best.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0dd8f3c8049c95f288eb61610701723cefa1050fae801a44037d3f0a4bc9df8b
+size 620852