Upload app.py

app.py

@@ -0,0 +1,99 @@
+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**")