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import streamlit as st
import pandas as pd
import ast
def calculate_match(profile1, profile2):
interests1 = set(ast.literal_eval(profile1['Interests']))
interests2 = set(ast.literal_eval(profile2['Interests']))
shared_interests_score = len(interests1.intersection(interests2))
age_difference_score = max(0, 10 - abs(profile1['Age'] - profile2['Age']))
swiping_history_score = min(profile1['Swiping History'], profile2['Swiping History']) / 100
relationship_type_score = 1 if profile1['Looking For'] == profile2['Looking For'] else 0
total_score = (shared_interests_score + age_difference_score + swiping_history_score +
relationship_type_score)
return total_score
def recommend_profiles(user_profile, all_profiles):
matches = []
for _, profile in all_profiles.iterrows():
if profile['Gender'] != user_profile['Gender']:
score = calculate_match(user_profile, profile)
matches.append((profile, score))
matches.sort(key=lambda x: x[1], reverse=True)
return matches[:5]
def main():
st.set_page_config(page_title="Dating App Matcher", layout="centered", initial_sidebar_state="collapsed")
st.markdown("""
<style>
.stButton>button {
width: 100%;
background-color: #FF4B4B;
color: white;
font-size: 18px;
border-radius: 8px;
}
.stTextInput>div>div>input, .stSelectbox>div>div>select, .stNumberInput>div>div>input {
border-radius: 8px;
}
</style>
""", unsafe_allow_html=True)
st.title("💘 Dating App Matcher")
st.write("### Enter your details to find the best matches!")
with st.form("user_input_form"):
age = st.number_input("Enter your age", min_value=18, max_value=100, step=1)
gender = st.selectbox("Select your gender", ["Male", "Female"])
interests = st.text_area("Enter your interests (comma-separated)")
looking_for = st.multiselect("Looking for", ['Casual Dating', 'Friendship', 'Marriage', 'Long-term Relationship'])
swiping_history = st.slider("Swiping History", 0, 100, 50)
submitted = st.form_submit_button("🔍 Find Matches")
if submitted:
df = pd.read_csv('dating_app_dataset.csv')
if {'User ID', 'Age', 'Gender', 'Height', 'Interests', 'Looking For', 'Swiping History', 'Frequency of Usage'}.issubset(df.columns):
user_profile = {
'Age': age,
'Gender': gender,
'Interests': str(interests.split(',')),
'Looking For': looking_for,
'Swiping History': swiping_history
}
matches = recommend_profiles(user_profile, df)
st.write("### 💑 Top 5 Matches")
for profile, score in matches:
with st.expander(f"User ID {profile['User ID']} - Score: {score:.2f}"):
st.write(f"**Age:** {profile['Age']}")
st.write(f"**Gender:** {profile['Gender']}")
st.write(f"**Interests:** {profile['Interests']}")
st.write(f"**Looking For:** {profile['Looking For']}")
st.write(f"**Swiping History:** {profile['Swiping History']}")
else:
st.error("Dataset must contain the required columns.")
if __name__ == "__main__":
main() |