Create app.py

app.py

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+import pandas as pd
+from surprise import Dataset, Reader
+
+laptop_df = pd.read_csv('laptop_data.csv')
+user_df = pd.read_csv('user_data.csv')  
+
+laptop_df = laptop_df.fillna(0)
+user_df = user_df.fillna(0)
+
+# Create a Surprise Dataset
+reader = Reader(rating_scale=(1, 5))
+data = Dataset.load_from_df(user_df[['User_ID', 'Laptop_ID', 'Rating']], reader)
+
+from surprise.model_selection import train_test_split
+from surprise import SVD
+from surprise import accuracy
+
+# Train-test split
+trainset, testset = train_test_split(data, test_size=0.2, random_state=42)
+
+# Train the collaborative filtering model (SVD algorithm for example)
+model = SVD()
+model.fit(trainset)
+
+def recommend_laptops(age=None, category=None, gender=None, user_id=None, num_recommendations=5):
+    if user_id is not None:
+        # Existing user
+        user_ratings = user_interactions[user_interactions['User_ID'] == user_id]
+        user_unrated_laptops = laptop_df[~laptop_df['Laptop_ID'].isin(user_ratings['Laptop_ID'])]
+        user_unrated_laptops['Predicted_Rating'] = user_unrated_laptops['Laptop_ID'].apply(lambda x: model.predict(user_id, x).est)
+        recommendations = user_unrated_laptops.sort_values(by='Predicted_Rating', ascending=False).head(num_recommendations)
+    else:
+        # New user
+        new_user_data = pd.DataFrame({
+            'User_ID': [10002],  
+            'Age': [age],
+            'Category': [category],
+            'Gender': [gender]
+        })
+        new_user_data = new_user_data.merge(laptop_df, how='cross')
+        new_user_data['Predicted_Rating'] = new_user_data.apply(lambda row: model.predict(999, row['Laptop_ID']).est, axis=1)
+        recommendations = new_user_data.sort_values(by='Predicted_Rating', ascending=False).head(num_recommendations)
+
+    return recommendations[['Laptop_ID', 'Laptop_Name', 'Predicted_Rating']]
+
+
+import streamlit as st
+
+# Streamlit app
+st.title("Laptop Recommendation System")
+
+# User choice: New or Existing user
+user_type = st.radio("Are you a new user or an existing user?", ('New User', 'Existing User'))
+
+if user_type == 'New User':
+    # User input for new users
+    new_user_age = st.slider("Age:", min_value=12, max_value=89, value=25)
+    new_user_category = st.selectbox("What best describes you:", ['Student', 'Professor', 'Banker', 'Businessman', 'Programmer'])
+    new_user_gender = st.radio("Gender:", ['Male', 'Female'])
+
+# Button to get recommendations for new users
+    if st.button("Get Laptop Recommendations"):
+        recommendations = recommend_laptops(age=new_user_age, category=new_user_category, gender=new_user_gender)
+        st.subheader("Top 5 Recommended Laptops:")
+        st.dataframe(recommendations[['Laptop_ID', 'Laptop_Name', 'Predicted_Rating']], index=False)
+
+# User input for existing users
+elif user_type == 'Existing User':
+    # User input for existing users
+    existing_user_id = st.text_input("Enter your user ID:", "")
+
+    # Button to get recommendations
+    if st.button("Get aptop Recommendations"):
+        if existing_user_id:
+            recommendations = recommend_laptops(user_id=int(existing_user_id))
+            st.subheader(f"Top 5 Recommended Laptops for User ID {existing_user_id}:")
+            st.dataframe(recommendations[['Laptop_ID', 'Laptop_Name', 'Predicted_Rating']], index=False)
+        else:
+            st.warning("Please enter a valid user ID.")