New branch name

.gitignore

@@ -17,4 +17,6 @@ dist/
 build/
 *.egg-info/
 helpers/*
-json_template/
+json_template/
+
+python_env

backend/add_reviews_method.py

@@ -0,0 +1,33 @@
+import json
+import os
+import pandas as pd
+
+
+directory = r'c:\Users\Nathan Palamuttam\Downloads\2_reviews_per_movie_raw'
+
+csv_names = []
+
+for filename in os.listdir(directory):
+    if filename.endswith('.csv'):
+        processed_name = filename[:-9]
+        print(processed_name)
+        csv_names.append((processed_name, filename))
+
+
+with open('init.json', 'r') as file:
+    data = json.load(file)
+
+    for item in data:
+        for item1 in csv_names:
+            if item['title'] is not None and item['title'] in item1[0]:
+                csv_file_path = os.path.join(directory, item1[1])
+                df = pd.read_csv(csv_file_path)
+                reviews = df['review'].head(10).tolist()
+                item['reviews'] = reviews
+                print(item['title'])
+                print(reviews)
+with open('init.json', 'w') as file:
+    json.dump(data, file, indent = 4)
+
+
+

backend/app.py

@@ -1,11 +1,22 @@
 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 ast
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.metrics.pairwise import linear_kernel
+from sklearn.model_selection import train_test_split
+from sklearn.feature_extraction.text import CountVectorizer
+from sklearn.naive_bayes import MultinomialNB
+from sklearn.pipeline import make_pipeline
+from sklearn.metrics import classification_report
+import numpy as np
 
-# ROOT_PATH for linking with all your files. 
+
+
+# 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))
 
@@ -18,29 +29,170 @@
 # 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'])
+    # episodes_df = pd.DataFrame(data['episodes'])
+    # reviews_df = pd.DataFrame(data['reviews'])
+    movies_df = pd.DataFrame(data)
 
 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 = movies_df[movies_df['title'].str.lower().str.contains(query.lower()) | movies_df['original_title'].str.lower().str.contains(query.lower())]
+    matches_filtered = matches[['title', 'overview', 'vote_average', 'reviews','image','popularity']]  # Adjusted to match relevant fields in the new JSON
+    matches_filtered_json = matches_filtered.to_json(orient='records')
+    return matches_filtered_json
+def genre_search(genre): 
+    matches = movies_df[movies_df['genres'].apply(lambda g: genre.lower() in (genre_name.lower() for genre_name in g))]
+    matches_filtered = matches[['title', 'overview', 'vote_average']]    
+    matches_filtered_json = matches_filtered.to_json(orient='records')
+    return matches_filtered_json
+def filter_movies_by_genre(genre):
+    def is_genre_present(genres_str, genre):
+        try:
+            genres_list = ast.literal_eval(genres_str)
+            for genre_dict in genres_list:
+                if genre.lower() == genre_dict['name'].lower():
+                    return True
+        except ValueError:
+            return False
+        return False
+
+    matches = movies_df[movies_df['genres'].apply(lambda g: is_genre_present(g, genre))]
+    matches_filtered = matches[['title', 'overview', 'vote_average', 'reviews','image','popularity']]  
     matches_filtered_json = matches_filtered.to_json(orient='records')
     return matches_filtered_json
 
+import math
+def compute_similarities(overviews, query):
+    combined_texts = overviews + [query]    
+    vectorizer = TfidfVectorizer(stop_words='english')
+    tfidf_matrix = vectorizer.fit_transform(combined_texts)    
+    cosine_similarities = linear_kernel(tfidf_matrix[-1], tfidf_matrix[:-1]).flatten()    
+    similarity_scores = [(score, idx) for idx, score in enumerate(cosine_similarities)]
+    similarity_scores = sorted(similarity_scores, reverse=True)
+    top_matches = similarity_scores[:50]  
+    return top_matches
+
+def tokenize(text):
+    if text is None: 
+        return ""
+    return text.lower().split()
+
+def build_vocabulary(descriptions):
+    vocab = set()
+    for description in descriptions:
+        vocab.update(tokenize(description))
+    return list(vocab)
+
+def vectorize(text, vocabulary):
+    word_counts = {word: 0 for word in vocabulary}
+    for word in tokenize(text):
+        if word in word_counts:
+            word_counts[word] += 1
+    return [word_counts[word] for word in vocabulary]
+
+def cosine_similarity(vec1, vec2):
+    dot_product = sum(v1 * v2 for v1, v2 in zip(vec1, vec2))
+    magnitude1 = math.sqrt(sum(v**2 for v in vec1))
+    magnitude2 = math.sqrt(sum(v**2 for v in vec2))
+    if magnitude1 == 0 or magnitude2 == 0:
+        return 0  
+    return dot_product / (magnitude1 * magnitude2)
+def genresuggests(genre): 
+    genres = set()
+    for item in data:
+        genre_list = json.loads(item['genres'].replace("'", "\""))
+        for genre in genre_list:
+            if genre in genre['name'].lower():
+                genres.add(genre['name'])
+
+    return jsonify(list(genres))
+
+
+train_data = pd.read_csv('train.tsv.zip', sep='\t')
+train_data['full_sentence'] = train_data.groupby('SentenceId')['Phrase'].transform(lambda x: ' '.join(x))
+train_data = train_data.drop_duplicates('SentenceId').reset_index(drop=True)
+
+model = make_pipeline(TfidfVectorizer(stop_words='english'), MultinomialNB())
+model.fit(train_data['full_sentence'], train_data['Sentiment'])
+
+def classify_and_score_reviews(json_data):
+    movies_df = pd.read_json(json_data)
+    results = []
+    for index, row in movies_df.iterrows():
+        movie_reviews = row['reviews']
+        if movie_reviews:
+            sentiments = model.predict(movie_reviews)
+            if len(sentiments) > 0:
+                average_sentiment = np.mean(sentiments)
+                results.append((row['title'], average_sentiment))
+            else:
+                results.append((row['title'], None))
+        else:
+            results.append((row['title'], None))
+
+    return dict(results)
+
+
 @app.route("/")
 def home():
     return render_template('base.html',title="sample html")
 
+def compute_cosine_similarities(texts):
+    vectorizer = TfidfVectorizer(stop_words='english')
+    tfidf_matrix = vectorizer.fit_transform(texts)
+    cosine_similarities = linear_kernel(tfidf_matrix[-1:], tfidf_matrix[:-1]).flatten()
+    return cosine_similarities
+
+
 @app.route("/episodes")
 def episodes_search():
     text = request.args.get("title")
-    return json_search(text)
+    query= request.args.get("query")
+    review=request.args.get("review")
+    print("Reivew", review)
+    movies_df = pd.read_json('init.json')
+
+    pure_json= filter_movies_by_genre(text)
+    json_text= json.loads(pure_json)
+    sentiment_scores = classify_and_score_reviews(pure_json)
+
+    overviews = [overview['overview'] if overview['overview'] is not None else "" for overview in json_text]
+    texts = overviews + ([query] if query is not None else [''])
+    query_sim= compute_cosine_similarities(texts)
+    reviews= [str(text['reviews']) if text['reviews'] is not None else "" for text in json_text]
+    review_text= reviews+([review] if review is not None else [''])
+    reviews_similarities=compute_cosine_similarities(review_text)
+    cosine_similarity=None
+    if review != "" and query != "": 
+        cosine_similarity= (query_sim+reviews_similarities)/2
+    if review != "" and query =="": 
+        cosine_similarity= reviews_similarities
+    if review == "" and query !="": 
+        cosine_similarity=query_sim
+    else: 
+        cosine_similarity=query_sim
+    movie_scores = list(enumerate(cosine_similarity))
+    sorted_movie_scores = sorted(movie_scores, key=lambda x: x[1], reverse=True)[:20]
+    combined_scores = [(index,value, sentiment_scores[json_text[index]['title']]) for index,value in sorted_movie_scores]
+    combined_scores_sorted = (sorted(combined_scores, key=lambda x: x[2] , reverse=True))
+    filtered_movies = [json_text[int(index)] for index,first,second in combined_scores_sorted]
+    return filtered_movies
+
+@app.route('/genre_suggestions')
+def genre_suggestions():
+    query = request.args.get('query', '').lower()
+    genres = set()
+
+    for item in data:
+        genre_list = json.loads(item['genres'].replace("'", "\""))
+        for genre in genre_list:
+            if query in genre['name'].lower():
+                genres.add(genre['name'])
+
+    return jsonify(list(genres))
 
 if 'DB_NAME' not in os.environ:
-    app.run(debug=True,host="0.0.0.0",port=5000)
+    app.run(debug=True,host="0.0.0.0",port=5000)
+

backend/checkjson.py

@@ -0,0 +1,6 @@
+import json
+
+with open('init.json', 'r') as file:
+    data = json.load(file)
+    for item in data:
+        print(item['popularity'], item['image'])