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import streamlit as st
import pandas as pd
# Load Hackathon and Team Data from CSV
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
df_hackathons["Prize Pool"] = pd.to_numeric(df_hackathons["Prize Pool"], errors='coerce')
# 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")
difficulty = st.sidebar.selectbox("Select Difficulty", ["All"] + df_hackathons["Difficulty Level"].dropna().unique().tolist())
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
sort_option = st.sidebar.selectbox("Sort Hackathons By:",
["Prize Pool (High to Low)", "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, hackathon_data, difficulty, mode, date_range, sort_option):
recommendations = []
for _, row in hackathon_data.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 difficulty != "All" and row["Difficulty Level"] != difficulty:
continue
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"],
"Prize Pool ($)": f"${row['Prize Pool']:,.0f}",
"Registration Deadline": row["Registration Deadline"].strftime('%Y-%m-%d'),
"Matched Skills": len(matched_skills) # Used for sorting but not displayed
})
# Sorting by Matched Skills (Highest First)
recommendations.sort(key=lambda x: x["Matched Skills"], reverse=True)
# Apply additional sorting if needed
if sort_option == "Prize Pool (High to Low)":
recommendations.sort(key=lambda x: float(x["Prize Pool ($)"].replace("$", "").replace(",", "")), reverse=True)
elif sort_option == "Registration Deadline (Soonest First)":
recommendations.sort(key=lambda x: x["Registration Deadline"])
# Convert to DataFrame and Remove Matched Skills Column
df_recommendations = pd.DataFrame(recommendations).drop(columns=["Matched Skills"])
# Show only the top 10 hackathons
return df_recommendations.head(10)
# Display Recommendations as Table
if unique_skills:
recommendations_df = recommend_hackathons(unique_skills, df_hackathons, difficulty, mode, date_range, sort_option)
if not recommendations_df.empty:
st.success(f"### ✅ Recommended Hackathons for **{selected_team}**")
st.dataframe(recommendations_df)
else:
st.warning("⚠️ No matching hackathons found. Try different filters.")
else:
st.warning("⚠️ No skills found for this team.")
# Footer
st.markdown("---")
st.write("🎯 **Built with Streamlit | AI-Powered Hackathon Finder**")
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