Initial Commit

Dockerfile

@@ -0,0 +1,12 @@
+FROM python:3.11-slim
+
+WORKDIR /app
+
+COPY requirements.txt requirements.txt
+RUN pip install --upgrade pip && pip install -r requirements.txt
+
+COPY . .
+
+ENV FLASK_APP=app.py
+
+CMD ["flask", "run", "--host=0.0.0.0", "--port=7860"]

Rekomendasi User to Item (CBF)/rekomendasi_deploy.py

@@ -55,4 +55,4 @@ def recommend():
 
 # === Run Server ===
 if __name__ == "__main__":
-    app.run(debug=True)
+    app.run(debug=True, host='0.0.0.0', port=5000)

app.py

@@ -0,0 +1,105 @@
+from flask import Flask, request, jsonify
+import pandas as pd
+import numpy as np
+import pickle
+from scipy.sparse import load_npz
+from sklearn.metrics.pairwise import cosine_similarity
+import os
+
+# === Inisialisasi Flask ===
+app = Flask(__name__)
+
+# === BASE PATH ===
+base_dir = os.path.dirname(os.path.abspath(__file__))
+
+# ======================================
+# === Load Model & Data USER to ITEM ===
+# ======================================
+with open(os.path.join(base_dir, "tfidf_vectorizer.pkl"), "rb") as f:
+    tfidf = pickle.load(f)
+
+tfidf_matrix = load_npz(os.path.join(base_dir, "tfidf_matrix.npz"))
+df_user = pd.read_csv(os.path.join(base_dir, "Dataset", "data_Processed.csv"))
+df_history = pd.read_csv(os.path.join(base_dir, "Dataset", "userHistory.csv"))
+
+# === Fungsi rekomendasi USER to ITEM ===
+def recommend_for_user(user_id, top_n=5):
+    user_history = df_history[df_history['userID'] == user_id]['namaWisata'].tolist()
+    visited_indices = df_user[df_user['title'].isin(user_history)].index
+
+    if len(visited_indices) == 0:
+        return []
+
+    user_profile_matrix = tfidf_matrix[visited_indices].mean(axis=0)
+    user_profile = np.asarray(user_profile_matrix).reshape(1, -1)
+    similarities = cosine_similarity(user_profile, tfidf_matrix).flatten()
+
+    df_temp = df_user.copy()
+    df_temp['similarity'] = similarities
+    rekomendasi = df_temp[~df_temp['title'].isin(user_history)]
+    rekomendasi = rekomendasi.sort_values(by='similarity', ascending=False)
+
+    return rekomendasi[['title', 'similarity']].head(top_n).to_dict(orient="records")
+
+# === Endpoint USER to ITEM ===
+@app.route("/recommenduti", methods=["GET"])
+def recommend_user_to_item():
+    user_id = request.args.get("user_id", type=int)
+
+    if user_id is None:
+        return jsonify({"error": "Parameter user_id diperlukan."}), 400
+
+    hasil = recommend_for_user(user_id)
+
+    if not hasil:
+        return jsonify({"message": f"Tidak ada data history untuk user ID {user_id}."}), 404
+
+    return jsonify({"user_id": user_id, "rekomendasi": hasil})
+
+
+# ======================================
+# === Load Model & Data ITEM to ITEM ===
+# ======================================
+df_item = pd.read_csv(os.path.join(base_dir, "Dataset", "data_Processed.csv"))
+
+with open(os.path.join(base_dir, "similarity_matrix.pkl"), "rb") as f:
+    similarity_matrix = pickle.load(f)
+
+# === Fungsi rekomendasi ITEM to ITEM ===
+def rekomendasi_tempat(tempat_id, top_n=5):
+    if tempat_id < 0 or tempat_id >= len(df_item):
+        return None
+
+    sim_scores = list(enumerate(similarity_matrix[tempat_id]))
+    sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)[1:top_n+1]
+    input_title = df_item.iloc[tempat_id]['title']
+    rekomendasi = []
+    for i, score in sim_scores:
+        rekomendasi.append({
+            'title': df_item.iloc[i]['title'],
+            'index': i,
+            'kategori': df_item.iloc[i]['kategori'],
+            'similarity': round(score, 3)
+        })
+
+    return {
+        "tempat_id": tempat_id,
+        "rekomendasi": rekomendasi,
+        "input_title": input_title
+    }
+
+# === Endpoint ITEM to ITEM ===
+@app.route("/recommenditi", methods=["GET"])
+def rekomendasi_api():
+    tempat_id = request.args.get("tempat_id", type=int)
+
+    if tempat_id is None:
+        return jsonify({"error": "Parameter 'tempat_id' diperlukan."}), 400
+
+    hasil = rekomendasi_tempat(tempat_id)
+
+    if hasil is None:
+        return jsonify({"message": f"Tempat '{tempat_id}' tidak ditemukan."}), 404
+
+    return jsonify(hasil)
+

requirements.txt

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+flask
+pandas
+numpy
+scikit-learn
+scipy

similarity_matrix.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ebc5a936cae12829abf36556df1e2b72e0f649a1dfd99eab7cd1f84023dc27bb
+size 148130

tfidf_matrix.npz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:faedb0b4226402f3a7ca92b0a889fd056870b9749bfd38104e5a48f387aa9a66
+size 37767

tfidf_vectorizer.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ff55d00fd0f9f1db48e35f1b7f09d677f5c9174bbdb06da49440f4fee87ae947
+size 18864