UI ml

backend/app.py

@@ -1,46 +1,48 @@
 import json
 import os
-from flask import Flask, render_template, request
+from flask import Flask, render_template, request, jsonify
 from flask_cors import CORS
-from helpers.MySQLDatabaseHandler import MySQLDatabaseHandler
-import pandas as pd
+import ml  
 
-# ROOT_PATH for linking with all your files. 
-# Feel free to use a config.py or settings.py with a global export variable
-os.environ['ROOT_PATH'] = os.path.abspath(os.path.join("..",os.curdir))
+# ROOT_PATH for linking with all your files.
+os.environ['ROOT_PATH'] = os.path.abspath(os.path.join("..", os.curdir))
 
-# Get the directory of the current script
-current_directory = os.path.dirname(os.path.abspath(__file__))
-
-# Specify the path to the JSON file relative to the current script
-json_file_path = os.path.join(current_directory, 'init.json')
-
-# Assuming your JSON data is stored in a file named 'init.json'
-with open(json_file_path, 'r') as file:
-    data = json.load(file)
-    episodes_df = pd.DataFrame(data['episodes'])
-    reviews_df = pd.DataFrame(data['reviews'])
 
 app = Flask(__name__)
 CORS(app)
 
-# Sample search using json with pandas
-def json_search(query):
-    matches = []
-    merged_df = pd.merge(episodes_df, reviews_df, left_on='id', right_on='id', how='inner')
-    matches = merged_df[merged_df['title'].str.lower().str.contains(query.lower())]
-    matches_filtered = matches[['title', 'descr', 'imdb_rating']]
-    matches_filtered_json = matches_filtered.to_json(orient='records')
-    return matches_filtered_json
-
 @app.route("/")
 def home():
-    return render_template('base.html',title="sample html")
-
-@app.route("/episodes")
-def episodes_search():
-    text = request.args.get("title")
-    return json_search(text)
-
-if 'DB_NAME' not in os.environ:
-    app.run(debug=True,host="0.0.0.0",port=5000)
+    return render_template('base.html', title="WanderingMelody")
+
+@app.route("/recommendations")
+def recommendations():
+    mood = request.args.get("mood")
+    location = request.args.get("location")
+    age = request.args.get("age", default=18, type=int)  # default age is 18 if not provided
+    genre = request.args.get("genre")
+
+    # Validate inputs (at least mood or genre should be provided)
+    if not mood:
+        return jsonify({"error": "Please provide at least a mood description or a genre."}), 400
+
+    # Preprocess the lyric data and build necessary indices (using ML functions)
+    dict_of_lyrics = ml.lyric_df[['text']].to_dict(orient="index")
+    cleaned_tokenized_lyrics = ml.tokenize_lyrics(dict_of_lyrics, ml.tokenize)
+    cleaned_tokenized_lyrics = ml.remove_stop_words(ml.custom_stopwords, cleaned_tokenized_lyrics)
+    inverted_index = ml.build_inverted_index(cleaned_tokenized_lyrics)
+    clean_song_count = ml.compute_idf(inverted_index, len(cleaned_tokenized_lyrics))
+
+    # Call the recommendation function from ml.py
+    recommended_songs, synonyms = ml.recommend_songs(mood, cleaned_tokenized_lyrics, clean_song_count)
+
+    if not recommended_songs:
+        return jsonify([])  
+
+    # Get song details (e.g., title, artist, album, genre, rating)
+    song_details = ml.get_song_details(recommended_songs)
+
+    return jsonify(song_details)
+
+if __name__ == "__main__":
+    app.run(debug=True, host="0.0.0.0", port=5000)