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import streamlit as st
import pandas as pd
# Load Hackathon and Team Data
hackathon_csv = "Hackathon_Dataset.csv"
team_csv = "Team_Dataset.csv"
df_hackathons = pd.read_csv(hackathon_csv)
df_teams = pd.read_csv(team_csv)
# Convert Event Date and Registration Deadline to datetime
df_hackathons["Event Date"] = pd.to_datetime(df_hackathons["Event Date"], errors='coerce')
df_hackathons["Registration Deadline"] = pd.to_datetime(df_hackathons["Registration Deadline"], errors='coerce')
# Ensure numerical columns are correctly formatted
if "Prize Pool" in df_hackathons.columns:
df_hackathons.drop(columns=["Prize Pool"], inplace=True) # Remove Prize Pool Column
# Get unique team names
team_list = df_teams["Team Name"].unique()
# Streamlit UI Setup
st.set_page_config(page_title="Hackathon Finder", layout="wide")
# Sidebar - Filters
st.sidebar.header("🔍 Filter Hackathons")
mode = st.sidebar.selectbox("Select Mode", ["All", "Online", "Offline"])
date_range = st.sidebar.slider("Select Event Date Range",
min_value=df_hackathons["Event Date"].min().date(),
max_value=df_hackathons["Event Date"].max().date(),
value=(df_hackathons["Event Date"].min().date(), df_hackathons["Event Date"].max().date()))
# Sorting Option (Keep only this)
sort_option = st.sidebar.selectbox("Sort Hackathons By:", ["Registration Deadline (Soonest First)"])
# Team Selection
st.title("🚀 Hackathon Recommendation System")
st.write("**Select your team to find the best hackathons for you!**")
selected_team = st.selectbox("Select Team:", team_list)
# Fetch Skills of Selected Team
team_skills = df_teams[df_teams["Team Name"] == selected_team]["Skills"].tolist()
unique_skills = list(set(skill.strip() for sublist in team_skills for skill in sublist.split(",")))
# Function to Recommend Hackathons
def recommend_hackathons(skills):
recommendations = []
for _, row in df_hackathons.iterrows():
required_skills = row["Required Skills"].split(",")
required_skills = [skill.strip().title() for skill in required_skills]
matched_skills = [skill for skill in skills if skill in required_skills]
if matched_skills:
# Apply Filters
if mode != "All" and row["Mode"] != mode:
continue
if not (date_range[0] <= row["Event Date"].date() <= date_range[1]):
continue
recommendations.append({
"Hackathon Name": row["Hackathon Name"],
"Organizer": row["Organizer"],
"Registration Deadline": row["Registration Deadline"].strftime('%Y-%m-%d'),
})
# Sorting by Registration Deadline
recommendations.sort(key=lambda x: x["Registration Deadline"])
return pd.DataFrame(recommendations).head(10) # Show only top 10 results
# Display Recommendations
if unique_skills:
recommendations_df = recommend_hackathons(unique_skills)
if not recommendations_df.empty:
st.success(f"### ✅ Recommended Hackathons for **{selected_team}**")
st.dataframe(recommendations_df)
else:
st.warning("⚠️ No matching hackathons found.")
else:
st.warning("⚠️ No skills found for this team.")
st.markdown("---")
st.write("🎯 **Built with Streamlit | AI-Powered Hackathon Finder**")
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