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app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import joblib
+
+# Load the trained models
+lr = joblib.load('linear_regression_model.pkl')
+dt = joblib.load('decision_tree_model.pkl')
+rf = joblib.load('random_forest_model.pkl')
+
+# Streamlit UI
+st.title("Indian Food Cook Time Prediction")
+st.write("Enter the features to predict the cook time of Indian food.")
+
+# Input fields
+diet = st.selectbox("Diet Type", options=["vegetarian", "non vegetarian"])
+prep_time = st.number_input("Preparation Time (minutes)", min_value=0, step=1)
+ingredients = st.number_input("Number of Ingredients", min_value=1, step=1)
+
+# Convert diet to numerical
+diet = 0 if diet == "vegetarian" else 1
+
+# Combine inputs into a DataFrame
+input_data = pd.DataFrame({
+    'diet': [diet],
+    'prep_time': [prep_time],
+    'num_ingredients': [ingredients]
+})
+
+# Model selection
+model_choice = st.selectbox("Select a Model", ["Linear Regression", "Decision Tree", "Random Forest"])
+if st.button("Predict Cook Time"):
+    if model_choice == "Linear Regression":
+        prediction = lr.predict(input_data)[0]
+    elif model_choice == "Decision Tree":
+        prediction = dt.predict(input_data)[0]
+    elif model_choice == "Random Forest":
+        prediction = rf.predict(input_data)[0]
+
+    st.write(f"Predicted Cook Time: {round(prediction, 2)} minutes")

decision_tree_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2608faee475773179048fd887066ec1760538900f37d8b783b10ed8f26de20f9
+size 5601

linear_regression_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:86dba6eee1ed5bb10b65d69244111c0a4df032b9cbd01832a2b84ae260edb363
+size 904

random_forest_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a56d767058dfcdac7aee5b436497a15962518d77d90bdaf81c61ae1720bff5b9
+size 367505