Update app.py

app.py

@@ -1,301 +1,301 @@
-import tensorflow as tf
-from tensorflow.keras import layers
-
-import pandas as pd
-import numpy as np
-import joblib
-
-from sklearn.metrics.pairwise import cosine_similarity
-from transformers import T5Tokenizer, T5ForConditionalGeneration
-
-from fastapi import FastAPI, Request
-# from IPython.display import display
-
-app = FastAPI()
-
-@tf.keras.utils.register_keras_serializable()
-class RecommenderNet(tf.keras.Model):
-    def __init__(self, num_users, num_places, embedding_size, dropout_rate, **kwargs):
-        super().__init__(**kwargs)
-
-        self.num_users = num_users
-        self.num_places = num_places
-        self.embedding_size = embedding_size
-        self.dropout_rate = dropout_rate
-
-        self.user_embedding = layers.Embedding(
-            num_users,
-            embedding_size,
-            embeddings_initializer='he_normal',
-            embeddings_regularizer=tf.keras.regularizers.l2(1e-4)
-        )
-        self.user_bias = layers.Embedding(num_users, 1)
-
-        self.place_embedding = layers.Embedding(
-            num_places,
-            embedding_size,
-            embeddings_initializer='he_normal',
-            embeddings_regularizer=tf.keras.regularizers.l2(1e-4)
-        )
-        self.place_bias = layers.Embedding(num_places, 1)
-
-        self.dropout = layers.Dropout(dropout_rate)
-
-    def call(self, inputs):
-        user_vector = self.user_embedding(inputs[:, 0])
-        user_vector = self.dropout(user_vector)
-
-        user_bias = self.user_bias(inputs[:, 0])
-        place_vector = self.place_embedding(inputs[:, 1])
-        place_vector = self.dropout(place_vector)
-
-        place_bias = self.place_bias(inputs[:, 1])
-
-        dot_user_place = tf.reduce_sum(user_vector * place_vector, axis=1, keepdims=True)
-        x = dot_user_place + user_bias + place_bias
-        return tf.squeeze(x, axis=1)
-
-    def get_config(self):
-        config = super().get_config()
-        config.update({
-            'num_users': self.num_users,
-            'num_places': self.num_places,
-            'embedding_size': self.embedding_size,
-            'dropout_rate': self.dropout_rate,
-        })
-        return config
-
-    @classmethod
-    def from_config(cls, config):
-        return cls(**config)
-    
-destinasi_df = pd.read_csv('data/destinasi_df.csv')
-rating_df = pd.read_csv('data/rating_df.csv')
-cb_df = pd.read_csv('data/cb_df.csv')
-
-cosine_sim_df = joblib.load('models/cosine_sim_df.pkl')
-model_cf = tf.keras.models.load_model(
-    'models/collab_model.keras',
-    custom_objects={'RecommenderNet': RecommenderNet}
-)
-user_to_user_encoded = joblib.load('models/user_to_user_encoded.pkl')
-place_to_place_encoded = joblib.load('models/place_to_place_encoded.pkl')
-tfidf_vectorizer = joblib.load('models/tfidf_vectorizer.pkl')
-tfidf_matrix = tfidf_vectorizer.transform(cb_df['Combined_Features'])
-
-def content_based_recommendations(place_name, similarity_data=cosine_sim_df, items=cb_df, k=5):
-
-    if place_name not in items['Place_Name'].values:
-        return pd.DataFrame()
-
-    index = items[items['Place_Name'] == place_name].index[0]
-    sim_scores = list(enumerate(similarity_data.iloc[index]))
-    sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)
-    sim_scores = sim_scores[1:k+1]
-    place_indices = [i[0] for i in sim_scores]
-    place_ids = items.iloc[place_indices]['Place_Id'].tolist()
-
-    return place_ids
-
-def collaborative_filtering_recommendations(user_id, n=5):
-
-    if user_id not in user_to_user_encoded:
-        return pd.DataFrame()
-
-    user_encoded = user_to_user_encoded[user_id]
-    place_ids = rating_df['Place_Id'].unique()
-    visited_places = rating_df[rating_df['User_Id'] == user_id]['Place_Id']
-    place_ids_unvisited = [p for p in place_ids if p not in visited_places]
-    place_encoded_unvisited = [
-        place_to_place_encoded[p] for p in place_ids_unvisited
-        if p in place_to_place_encoded
-    ]
-
-    user_place_array = np.array([[user_encoded, p_enc] for p_enc in place_encoded_unvisited])
-    ratings = model_cf.predict(user_place_array).flatten()
-    top_ratings_indices = ratings.argsort()[-n:][::-1]
-    recommended_place_ids = [place_ids_unvisited[i] for i in top_ratings_indices]
-
-    return recommended_place_ids
-
-def get_travel_recommendations(user_id, favorite_place=None):
-
-    all_recommendations = []
-    cf_recs = collaborative_filtering_recommendations(user_id)
-    all_recommendations.extend(cf_recs)
-
-    if favorite_place:
-        cb_recs = content_based_recommendations(favorite_place)
-        all_recommendations.extend(cb_recs)
-
-    unique_recommendations = list(set(all_recommendations))
-    recommendations_df = destinasi_df[
-        destinasi_df['Place_Id'].isin(unique_recommendations)
-    ].copy()
-
-    recommendations_df['Recommendation_Source'] = 'Hybrid'
-    recommendations_df.loc[
-        recommendations_df['Place_Id'].isin(cf_recs), 'Recommendation_Source'
-    ] = 'Collaborative'
-
-    if favorite_place:
-        recommendations_df.loc[
-            recommendations_df['Place_Id'].isin(cb_recs), 'Recommendation_Source'
-        ] = 'Content-Based'
-
-    return recommendations_df
-
-
-#IMPLEMENTASI DENGAN MENGGGABUNGKAN 2 PENDEKATAN YANG LEBIH FLEKSIBEL
-
-# new_user_recs = get_travel_recommendations(user_id=1)
-# user_recs = get_travel_recommendations(
-#     user_id= 3,
-#     favorite_place= "Monumen Nasional"
-# )
-
-# print("Rekomendasi untuk user dengan favorite place 'Monumen Nasional':")
-# from IPython.display import display
-# display(user_recs)
-
-# print("Rekomendasi untuk user baru (tanpa favorite place):")
-# display(new_user_recs)
-
-
-
-#IMPLEMENTASI HANYA BERDASARKAN CONTENT DESTINASINYA DENGAN INPUT KATEGORI NAMA ATAU KOTA
-
-def infer_cbf_search(query, top_k=10):
-    """
-    Fungsi inference Content-Based Filtering menggunakan cosine similarity
-    antara query dan TF-IDF matrix dari Combined_Features.
-    Juga menyesuaikan skor berdasarkan City & Category.
-    """
-    weight_city = 0.15
-    weight_category = 0.05
-
-    query = query.lower().strip()
-    keywords = query.split()
-
-    query_vec = tfidf_vectorizer.transform([query])
-    similarity_scores = cosine_similarity(query_vec, tfidf_matrix).flatten()
-    top_indices = similarity_scores.argsort()[::-1][:top_k * 3]
-
-    unique_cities = cb_df['City'].str.lower().unique().tolist()
-    city_in_query = [c for c in unique_cities if c in query]
-
-    recommendations = []
-    for idx in top_indices:
-        place = cb_df.iloc[idx]
-        base_score = similarity_scores[idx]
-        adjusted_score = base_score
-
-        if city_in_query and place['City'].lower() in city_in_query:
-            adjusted_score += weight_city
-
-        if any(kw in place['Category'].lower() for kw in keywords):
-            adjusted_score += weight_category
-
-        rec = place[['Place_Id']].copy()
-        rec['Similarity_Score'] = round(adjusted_score, 4)
-        rec['Search_Match'] = query
-        recommendations.append(rec)
-
-    rec_df = pd.DataFrame(recommendations)
-    rec_df = rec_df.sort_values('Similarity_Score', ascending=False)
-    rec_df = rec_df.drop_duplicates(subset=['Place_Id']).head(top_k)
-
-    merged_df = pd.merge(rec_df, destinasi_df, on='Place_Id', how='left')
-    return merged_df.to_dict(orient='records')
-
-# hasil = infer_cbf_search("budaya yogyakarta ", top_k=5)
-# display(hasil)
-
-
-#GENERATIVE AI UNTUK TEKS REKOMENDASI SINGKAT
-
-model_dir = "mjamalm18/t5-finetuned-recommendation-final"
-tokenizer = T5Tokenizer.from_pretrained(model_dir, legacy=True)
-model = T5ForConditionalGeneration.from_pretrained(model_dir)
-
-# rekom_texts = []
-# for _, row in user_recs.iterrows():
-#     teks = f"{row['Place_Name']} di {row['City']}, kategori {row['Category']}, rating {row['Rating']}"
-#     rekom_texts.append(teks)
-# input_text = "Rekomendasi tempat wisata: " + "; ".join(rekom_texts)
-
-def generate_natural_recommendation(user_id, favorite_place=None, top_n=1):
-
-    user_recs = get_travel_recommendations(user_id=user_id, favorite_place=favorite_place)
-
-    if user_recs.empty:
-        return "Tidak ada rekomendasi tersedia untuk user ini."
-
-    user_recs = user_recs.head(top_n)
-    input_template = "User menyukai kategori: {category}; lokasi: {city}; tempat: {place}; rating: {rating}"
-
-    parts = []
-    for _, row in user_recs.iterrows():
-        part = input_template.format(
-            category=row['Category'],
-            city=row['City'],
-            place=row['Place_Name'],
-            rating=row['Rating']
-        )
-        parts.append(part)
-
-    input_text = " ; ".join(parts)
-    inputs = tokenizer(input_text, return_tensors="pt", max_length=512, truncation=True)
-    outputs = model.generate(**inputs, max_length=150)
-    result_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
-
-    return result_text
-
-# hasil = generate_natural_recommendation(user_id=1,favorite_place="Kampung Wisata Taman Sari")
-# print(hasil)
-
-@app.post("/recommendations")
-async def recommendations(request: Request):
-    body = await request.json()
-    user_id = body.get("user_id")
-    favorite_place = body.get("favorite_place")
-    print(user_id)
-    print(favorite_place)
-
-    try:
-        user_id = int(user_id)
-    except (ValueError, TypeError):
-        return {"user_id": user_id, "recommendations": []}
-
-    result = get_travel_recommendations(user_id, favorite_place)
-    return {"user_id": user_id, "recommendations": result.to_dict(orient='records')}
-
-@app.post("/search")
-async def search(request: Request):
-    body = await request.json()
-    place = body.get("place")
-    result = infer_cbf_search(place)
-    return {"query": place, "results": result}
-
-@app.post("/textgen")
-async def textgen(request: Request):
-    body = await request.json()
-    user_id = body.get("user_id")
-    favorite_place = body.get("favorite_place")
-
-    try:
-        user_id = int(user_id)
-    except (ValueError, TypeError):
-        return {
-            "user_id": user_id,
-            "gen_text": "User ID tidak valid."
-        }
-
-    # Panggil fungsi generate_natural_recommendation
-    gen_text = generate_natural_recommendation(user_id, favorite_place)
-
-    return {
-        "user_id": user_id,
-        "favorite_place": favorite_place,
-        "gen_text": gen_text
+import tensorflow as tf
+from tensorflow.keras import layers
+
+import pandas as pd
+import numpy as np
+import joblib
+
+from sklearn.metrics.pairwise import cosine_similarity
+from transformers import T5Tokenizer, T5ForConditionalGeneration
+
+from fastapi import FastAPI, Request
+# from IPython.display import display
+
+app = FastAPI()
+
+@tf.keras.utils.register_keras_serializable()
+class RecommenderNet(tf.keras.Model):
+    def __init__(self, num_users, num_places, embedding_size, dropout_rate, **kwargs):
+        super().__init__(**kwargs)
+
+        self.num_users = num_users
+        self.num_places = num_places
+        self.embedding_size = embedding_size
+        self.dropout_rate = dropout_rate
+
+        self.user_embedding = layers.Embedding(
+            num_users,
+            embedding_size,
+            embeddings_initializer='he_normal',
+            embeddings_regularizer=tf.keras.regularizers.l2(1e-4)
+        )
+        self.user_bias = layers.Embedding(num_users, 1)
+
+        self.place_embedding = layers.Embedding(
+            num_places,
+            embedding_size,
+            embeddings_initializer='he_normal',
+            embeddings_regularizer=tf.keras.regularizers.l2(1e-4)
+        )
+        self.place_bias = layers.Embedding(num_places, 1)
+
+        self.dropout = layers.Dropout(dropout_rate)
+
+    def call(self, inputs):
+        user_vector = self.user_embedding(inputs[:, 0])
+        user_vector = self.dropout(user_vector)
+
+        user_bias = self.user_bias(inputs[:, 0])
+        place_vector = self.place_embedding(inputs[:, 1])
+        place_vector = self.dropout(place_vector)
+
+        place_bias = self.place_bias(inputs[:, 1])
+
+        dot_user_place = tf.reduce_sum(user_vector * place_vector, axis=1, keepdims=True)
+        x = dot_user_place + user_bias + place_bias
+        return tf.squeeze(x, axis=1)
+
+    def get_config(self):
+        config = super().get_config()
+        config.update({
+            'num_users': self.num_users,
+            'num_places': self.num_places,
+            'embedding_size': self.embedding_size,
+            'dropout_rate': self.dropout_rate,
+        })
+        return config
+
+    @classmethod
+    def from_config(cls, config):
+        return cls(**config)
+    
+destinasi_df = pd.read_csv('data/destinasi_df.csv')
+rating_df = pd.read_csv('data/rating_df.csv')
+cb_df = pd.read_csv('data/cb_df.csv')
+
+cosine_sim_df = joblib.load('models/cosine_sim_df.pkl')
+model_cf = tf.keras.models.load_model(
+    'models/collab_model.keras',
+    custom_objects={'RecommenderNet': RecommenderNet}
+)
+user_to_user_encoded = joblib.load('models/user_to_user_encoded.pkl')
+place_to_place_encoded = joblib.load('models/place_to_place_encoded.pkl')
+tfidf_vectorizer = joblib.load('models/tfidf_vectorizer.pkl')
+tfidf_matrix = tfidf_vectorizer.transform(cb_df['Combined_Features'])
+
+def content_based_recommendations(place_name, similarity_data=cosine_sim_df, items=cb_df, k=5):
+
+    if place_name not in items['Place_Name'].values:
+        return pd.DataFrame()
+
+    index = items[items['Place_Name'] == place_name].index[0]
+    sim_scores = list(enumerate(similarity_data.iloc[index]))
+    sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)
+    sim_scores = sim_scores[1:k+1]
+    place_indices = [i[0] for i in sim_scores]
+    place_ids = items.iloc[place_indices]['Place_Id'].tolist()
+
+    return place_ids
+
+def collaborative_filtering_recommendations(user_id, n=5):
+
+    if user_id not in user_to_user_encoded:
+        return pd.DataFrame()
+
+    user_encoded = user_to_user_encoded[user_id]
+    place_ids = rating_df['Place_Id'].unique()
+    visited_places = rating_df[rating_df['User_Id'] == user_id]['Place_Id']
+    place_ids_unvisited = [p for p in place_ids if p not in visited_places]
+    place_encoded_unvisited = [
+        place_to_place_encoded[p] for p in place_ids_unvisited
+        if p in place_to_place_encoded
+    ]
+
+    user_place_array = np.array([[user_encoded, p_enc] for p_enc in place_encoded_unvisited])
+    ratings = model_cf.predict(user_place_array).flatten()
+    top_ratings_indices = ratings.argsort()[-n:][::-1]
+    recommended_place_ids = [place_ids_unvisited[i] for i in top_ratings_indices]
+
+    return recommended_place_ids
+
+def get_travel_recommendations(user_id, favorite_place=None):
+
+    all_recommendations = []
+    cf_recs = collaborative_filtering_recommendations(user_id)
+    all_recommendations.extend(cf_recs)
+
+    if favorite_place:
+        cb_recs = content_based_recommendations(favorite_place)
+        all_recommendations.extend(cb_recs)
+
+    unique_recommendations = list(set(all_recommendations))
+    recommendations_df = destinasi_df[
+        destinasi_df['Place_Id'].isin(unique_recommendations)
+    ].copy()
+
+    recommendations_df['Recommendation_Source'] = 'Hybrid'
+    recommendations_df.loc[
+        recommendations_df['Place_Id'].isin(cf_recs), 'Recommendation_Source'
+    ] = 'Collaborative'
+
+    if favorite_place:
+        recommendations_df.loc[
+            recommendations_df['Place_Id'].isin(cb_recs), 'Recommendation_Source'
+        ] = 'Content-Based'
+
+    return recommendations_df
+
+
+#IMPLEMENTASI DENGAN MENGGGABUNGKAN 2 PENDEKATAN YANG LEBIH FLEKSIBEL
+
+# new_user_recs = get_travel_recommendations(user_id=1)
+# user_recs = get_travel_recommendations(
+#     user_id= 3,
+#     favorite_place= "Monumen Nasional"
+# )
+
+# print("Rekomendasi untuk user dengan favorite place 'Monumen Nasional':")
+# from IPython.display import display
+# display(user_recs)
+
+# print("Rekomendasi untuk user baru (tanpa favorite place):")
+# display(new_user_recs)
+
+
+
+#IMPLEMENTASI HANYA BERDASARKAN CONTENT DESTINASINYA DENGAN INPUT KATEGORI NAMA ATAU KOTA
+
+def infer_cbf_search(query, top_k=10):
+    """
+    Fungsi inference Content-Based Filtering menggunakan cosine similarity
+    antara query dan TF-IDF matrix dari Combined_Features.
+    Juga menyesuaikan skor berdasarkan City & Category.
+    """
+    weight_city = 0.15
+    weight_category = 0.05
+
+    query = query.lower().strip()
+    keywords = query.split()
+
+    query_vec = tfidf_vectorizer.transform([query])
+    similarity_scores = cosine_similarity(query_vec, tfidf_matrix).flatten()
+    top_indices = similarity_scores.argsort()[::-1][:top_k * 3]
+
+    unique_cities = cb_df['City'].str.lower().unique().tolist()
+    city_in_query = [c for c in unique_cities if c in query]
+
+    recommendations = []
+    for idx in top_indices:
+        place = cb_df.iloc[idx]
+        base_score = similarity_scores[idx]
+        adjusted_score = base_score
+
+        if city_in_query and place['City'].lower() in city_in_query:
+            adjusted_score += weight_city
+
+        if any(kw in place['Category'].lower() for kw in keywords):
+            adjusted_score += weight_category
+
+        rec = place[['Place_Id']].copy()
+        rec['Similarity_Score'] = round(adjusted_score, 4)
+        rec['Search_Match'] = query
+        recommendations.append(rec)
+
+    rec_df = pd.DataFrame(recommendations)
+    rec_df = rec_df.sort_values('Similarity_Score', ascending=False)
+    rec_df = rec_df.drop_duplicates(subset=['Place_Id']).head(top_k)
+
+    merged_df = pd.merge(rec_df, destinasi_df, on='Place_Id', how='left')
+    return merged_df.to_dict(orient='records')
+
+# hasil = infer_cbf_search("budaya yogyakarta ", top_k=5)
+# display(hasil)
+
+
+#GENERATIVE AI UNTUK TEKS REKOMENDASI SINGKAT
+
+model_dir = "t5-finetuned-recommendation-final"
+tokenizer = T5Tokenizer.from_pretrained(model_dir, legacy=True)
+model = T5ForConditionalGeneration.from_pretrained(model_dir)
+
+# rekom_texts = []
+# for _, row in user_recs.iterrows():
+#     teks = f"{row['Place_Name']} di {row['City']}, kategori {row['Category']}, rating {row['Rating']}"
+#     rekom_texts.append(teks)
+# input_text = "Rekomendasi tempat wisata: " + "; ".join(rekom_texts)
+
+def generate_natural_recommendation(user_id, favorite_place=None, top_n=1):
+
+    user_recs = get_travel_recommendations(user_id=user_id, favorite_place=favorite_place)
+
+    if user_recs.empty:
+        return "Tidak ada rekomendasi tersedia untuk user ini."
+
+    user_recs = user_recs.head(top_n)
+    input_template = "User menyukai kategori: {category}; lokasi: {city}; tempat: {place}; rating: {rating}"
+
+    parts = []
+    for _, row in user_recs.iterrows():
+        part = input_template.format(
+            category=row['Category'],
+            city=row['City'],
+            place=row['Place_Name'],
+            rating=row['Rating']
+        )
+        parts.append(part)
+
+    input_text = " ; ".join(parts)
+    inputs = tokenizer(input_text, return_tensors="pt", max_length=512, truncation=True)
+    outputs = model.generate(**inputs, max_length=150)
+    result_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+    return result_text
+
+# hasil = generate_natural_recommendation(user_id=1,favorite_place="Kampung Wisata Taman Sari")
+# print(hasil)
+
+@app.post("/recommendations")
+async def recommendations(request: Request):
+    body = await request.json()
+    user_id = body.get("user_id")
+    favorite_place = body.get("favorite_place")
+    print(user_id)
+    print(favorite_place)
+
+    try:
+        user_id = int(user_id)
+    except (ValueError, TypeError):
+        return {"user_id": user_id, "recommendations": []}
+
+    result = get_travel_recommendations(user_id, favorite_place)
+    return {"user_id": user_id, "recommendations": result.to_dict(orient='records')}
+
+@app.post("/search")
+async def search(request: Request):
+    body = await request.json()
+    place = body.get("place")
+    result = infer_cbf_search(place)
+    return {"query": place, "results": result}
+
+@app.post("/textgen")
+async def textgen(request: Request):
+    body = await request.json()
+    user_id = body.get("user_id")
+    favorite_place = body.get("favorite_place")
+
+    try:
+        user_id = int(user_id)
+    except (ValueError, TypeError):
+        return {
+            "user_id": user_id,
+            "gen_text": "User ID tidak valid."
+        }
+
+    # Panggil fungsi generate_natural_recommendation
+    gen_text = generate_natural_recommendation(user_id, favorite_place)
+
+    return {
+        "user_id": user_id,
+        "favorite_place": favorite_place,
+        "gen_text": gen_text
     }