Create app.py

app.py

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+from flask import Flask, render_template, request, send_file, redirect
+import json
+import requests
+from urllib.request import urlopen
+from bs4 import BeautifulSoup
+import re
+import joblib
+from nltk.sentiment import SentimentIntensityAnalyzer
+import matplotlib.pyplot as plt
+from matplotlib import font_manager
+from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
+plt.switch_backend('Agg')
+import numpy as np
+from io import BytesIO
+import base64
+from dotenv import load_dotenv
+import os
+import threading
+
+import nltk
+nltk.download('vader_lexicon')
+
+def load_classifier_and_vectorizer():
+    global classifier, vectorizer
+    classifier = joblib.load('./data/sentiment_classifier.pkl')
+    vectorizer = joblib.load('./data/sentiment_vectorizer.pkl')
+
+# Start loading in a separate thread
+loading_thread = threading.Thread(target=load_classifier_and_vectorizer)
+loading_thread.start()
+
+app = Flask(__name__)
+load_dotenv()
+
+# Function to get Google search links
+def search_links(query):
+    base_url = "https://www.googleapis.com/customsearch/v1"
+    api_key = os.environ['api_key']
+    cx = os.environ['cx']
+    params = {
+        'q': query,
+        'key': api_key,
+        'cx': cx
+    }
+    Err = ""
+    response = requests.get(base_url, params=params)
+    if response.status_code == 429 or response.status_code == 403:
+        Err = "Daily API limit reached"
+        print("API limit reached.")
+        return []
+
+    results = response.json().get('items', [])
+    # Extract title and link from the results and create a list of dictionaries
+    links_with_titles = [[{item.get('title', ''): item.get('link', '')} for item in results], Err]
+
+    return links_with_titles
+
+# Function to fetch reviews
+def get_reviews(movie_url, review_type):
+    page = urlopen(movie_url)
+    html_bytes = page.read()
+    html = html_bytes.decode("utf-8")
+    emsId = re.search(r'"emsId":\s*"([^"]+)"', html, re.IGNORECASE).group(1)
+    title = re.search(r'"titleName":\s*"([^"]+)"', html, re.IGNORECASE).group(1)
+    title_type = re.search(r'"titleType":\s*"([^"]+)"', html, re.IGNORECASE).group(1)
+    vanity =  re.search(r'"vanity":\s*"([^"]+)"', html, re.IGNORECASE).group(1)
+
+    s_no = ""
+    if title_type == "Tv":
+        s_no = re.search(r'"tvSeason":\s*"([^"]+)"', html, re.IGNORECASE).group(1)
+    poster_page_url = f'https://www.rottentomatoes.com/{"tv" if title_type == "Tv" else "m"}/{vanity}/{f"{s_no}/" if s_no else ""}reviews'
+    poster_page = urlopen(poster_page_url)
+    soup = BeautifulSoup(poster_page, 'html.parser')
+    poster_link = soup.find('img', {'data-qa': 'sidebar-poster-img'})
+    if poster_link:
+        poster_link = poster_link.get('src')
+    else:
+        poster_link = '/data/favicon.ico'
+    
+    hasNextPage = True
+    reviews = []
+    after = ""
+    while hasNextPage and len(reviews) < 100:
+        url = f'https://www.rottentomatoes.com/napi/{"season" if title_type == "Tv" else "movie"}/{emsId}/reviews/{review_type}?after={after}'
+        response = urlopen(url).read().decode('utf-8')
+        response_object = json.loads(response)
+        responseArray = response_object['reviews']
+        for review in responseArray:
+            reviews.append(review['quote'])
+        hasNextPage = response_object['pageInfo']['hasNextPage']
+        if hasNextPage:
+            after = response_object['pageInfo']['endCursor']
+    global show_info
+    show_info = [title, poster_link]
+    return reviews
+
+
+# Function to clean text
+def clean(input_string):
+    cleaned_string = re.sub(r'\s+', ' ', input_string)
+    return cleaned_string.strip() 
+
+def analyze_sentiment(reviews):
+    loading_thread.join()
+    
+    # Clean and analyze reviews
+    cleaned_reviews = [clean(phrase) for phrase in reviews]
+    new_vector = vectorizer.transform(cleaned_reviews)
+    predictions = classifier.predict(new_vector)
+
+    # Return predictions
+    return predictions
+
+# Function to get polarity scores using NLTK
+def get_polarity_scores(reviews):
+    cleaned_reviews = [clean(phrase) for phrase in reviews]
+    sia = SentimentIntensityAnalyzer()
+    scores_list = []
+    for entry in reviews:
+        scores = sia.polarity_scores(entry)
+        scores_list.append(scores)
+    return scores_list
+  
+#Load Font
+custom_font_path = 'data/HPSimplified.ttf'
+font_manager.fontManager.addfont(custom_font_path)
+
+# Function to plot a bar chart
+def plot_bar(data, title):
+    # Extracting values for each sentiment
+    neg_values = [entry['neg'] for entry in data]
+    neu_values = [entry['neu'] for entry in data]
+    pos_values = [entry['pos'] for entry in data]
+    compound_values = [entry['compound'] for entry in data]
+
+    # Creating positions for bars
+    positions = range(len(data))
+
+    # Plotting the stacked bars
+    fig, ax = plt.subplots(figsize=(20,10))
+    width = 0.7
+
+    # Bottom bar (red)
+    ax.bar(positions, neg_values, width=width, color='#961e1e', label='Negative')
+
+    # Middle bar (gray)
+    ax.bar(positions, neu_values, width=width, bottom=neg_values, color='#999', label='Neutral')
+
+    # Top bar (green)
+    ax.bar(positions, pos_values, width=width, bottom=np.array(neg_values) + np.array(neu_values), color='#015501', label='Positive')
+
+    # Adding labels and title
+    plt.xlabel('Reviews', fontdict={'fontname': 'HP Simplified', 'fontsize': 30, 'weight':'bold', 'color':'#fff'}, labelpad=20)
+    plt.ylabel('Polarity Scores', fontdict={'fontname': 'HP Simplified', 'fontsize': 30, 'weight':'bold', 'color':'#fff'}, labelpad=20)
+    plt.title(title, fontdict={'fontname': 'HP Simplified', 'fontsize': 40, 'weight':'bold', 'color':'#fff'}, pad=20)
+    plt.yticks(fontname='HP Simplified', fontsize=24, color="#fff")
+
+    # Remove X-axis labels
+    ax.set_xticks([])
+    ax.set_facecolor('#000')
+
+    # Adding legend
+    legend = plt.legend(loc='upper right', bbox_to_anchor=(1.25, 1), prop={'family': 'HP Simplified', 'size': 32})
+
+    average = np.mean(compound_values)
+    text = f'Average Compound Score'
+    avg_text = f'\n{average:.2f}'
+    plt.text(0.91, 0.66, text, fontsize=20, fontname='HP Simplified', weight="bold", color="white", ha='center', va='center', transform=fig.transFigure)
+    plt.text(0.91, 0.64, avg_text, fontsize=36, fontname='HP Simplified', weight="bold", color="white", ha='center', va='center', transform=fig.transFigure)
+
+    # Set the background color
+    fig.set_facecolor('#1e1e1e')
+    plt.tight_layout()
+
+    # Convert the Matplotlib figure to a Flask response
+    output = BytesIO()
+    FigureCanvas(fig).print_png(output)
+    plt.close()
+    return base64.b64encode(output.getvalue()).decode('utf-8')
+
+def plot_pie(data, title):
+    unique, counts = np.unique(data, return_counts=True)
+
+    explode = ()
+    if len(unique) > 1:
+        explode = (0, 0.1)
+    else:
+        explode = (0,)
+    
+    #add colors
+    colors = []
+    if unique[0] == "Negative":
+        colors = ['#961e1e','#024d0f']
+    elif unique[0] == "Positive":
+        colors = ['#024d0f','#961e1e']
+    
+    fig, ax = plt.subplots()
+    fig.patch.set_facecolor('#1e1e1e')
+    ax.pie(counts, explode=explode, labels=unique, colors=colors, autopct='%1.1f%%', shadow=True, startangle=90)
+
+    # Set custom fonts for title, labels, and autopct
+    ax.set_title(title, fontdict={'family': 'HP Simplified', 'color': 'White', 'weight': 'bold', 'size': 28})
+    for text in ax.texts:
+        text.set_fontfamily('HP Simplified')
+        text.set_fontsize('20')
+        text.set_fontweight('bold')
+        text.set_color('White')
+        
+    # Equal aspect ratio ensures that pie is drawn as a circle
+    ax.axis('equal')
+    plt.tight_layout()
+
+    output = BytesIO()
+    FigureCanvas(fig).print_png(output)
+    plt.close()
+    return base64.b64encode(output.getvalue()).decode('utf-8')
+
+@app.route('/data/bg.png')
+def bg():
+    return send_file('data/bg.png')
+
+@app.route('/data/search-icon.svg')
+def search_icon():
+    return send_file('data/search-icon.svg')
+
+@app.route('/data/favicon.ico')
+def favicon():
+    return send_file('data/favicon.ico')
+
+@app.route('/data/HPSimplified.ttf')
+def font():
+    return send_file('data/HPSimplified.ttf')
+
+@app.route('/data/search.gif')
+def searchload():
+    return send_file('./data/search.gif')
+  
+@app.route('/')
+def home():
+    return render_template('index.html')
+
+@app.route('/search')
+def search():
+    query = request.args.get('query')
+    if query:
+            search_data = search_links(query)
+            if len(search_data[0]) == 0:
+                search_data[1] = "No links found for your query"
+            return render_template('search.html', links=search_data[0], Err=search_data[1])
+    else:
+        return redirect('/')
+
+@app.route('/review')
+def review():
+    url = request.args.get('url')
+    if url:
+            url = url
+            user = get_reviews(url, 'user')
+            critic = get_reviews(url, 'all')
+            user_pie_img, user_bar_img, critic_pie_img, critic_bar_img = '', '', '', ''
+
+            if len(user) > 0:
+                user_polarity = get_polarity_scores(user)
+                user_reviews = analyze_sentiment(user)
+                user_bar = plot_bar(user_polarity, "User Sentiments")
+                user_pie = plot_pie(user_reviews, "User Reviews")
+                user_bar_img = f'<img src="data:image/png;base64,{user_bar}" alt="User Reviews Bar Plot">'
+                user_pie_img = f'<img src="data:image/png;base64,{user_pie}" alt="User Reviews Pie Plot">'
+            else:
+                user_bar_img = "No User Reviews Found"
+                user_pie_img = "No User Reviews Found"
+
+            if len(critic) > 0:
+                critic_polarity = get_polarity_scores(critic)
+                critic_reviews = analyze_sentiment(critic)
+                critic_bar = plot_bar(critic_polarity, "Critic Sentiments")
+                critic_pie = plot_pie(critic_reviews, "Critic Reviews")
+                critic_bar_img = f'<img src="data:image/png;base64,{critic_bar}" alt="Critic Reviews Bar Plot">'
+                critic_pie_img = f'<img src="data:image/png;base64,{critic_pie}" alt="Critic Reviews Pie Plot">'
+            else:
+                critic_bar_img = "No Critic Reviews Found"
+                critic_pie_img = "No Critic Reviews Found"
+
+            return render_template('review.html', user_bar=user_bar_img, user_pie=user_pie_img, critic_bar=critic_bar_img, critic_pie=critic_pie_img, title=show_info[0], url=url, poster=show_info[1])
+          
+    else:
+        return redirect('/')
+
+@app.errorhandler(404)
+def not_found_error(error):
+    return render_template('404.html'), 404
+
+if __name__ == '__main__':
+    app.run(host='0.0.0.0', port=3000, debug=True)