Delete reviews_app.py

reviews_app.py

@@ -1,151 +0,0 @@
-#!/usr/bin/env python
-# coding: utf-8
-
-from sklearn.feature_extraction.text import TfidfVectorizer
-from nltk.stem import WordNetLemmatizer
-import streamlit as st
-import pickle
-import pandas as pd
-import numpy as np
-import nltk
-import regex as re
-from nltk.corpus import stopwords
-from nltk.tokenize import word_tokenize
-from sklearn.ensemble import RandomForestClassifier
-import transformers
-from transformers import AutoTokenizer, AutoModelForSequenceClassification, AutoConfig
-from scipy.special import softmax
-import matplotlib.pyplot as plt
-import seaborn as sns
-import ast
-
-
-# Load the model
-def load_model():
-    with open('random_forest_model.pkl', 'rb') as file:
-        loaded_model = pickle.load(file)
-    return loaded_model
-
-def load_vectorizer():
-    with open('tfidf_vectorizer.pkl', 'rb') as file:
-        loaded_vectorizer = pickle.load(file)
-    return loaded_vectorizer
-
-
-def ratings(list_of_reviews):
-    xidf = []
-    stopwords = nltk.corpus.stopwords.words('english')
-    lemmatizer = WordNetLemmatizer()
-    review = re.sub('[^a-zA-Z]', ' ', list_of_reviews)
-    review = review.lower()
-    review = review.split()
-    review = [lemmatizer.lemmatize(word) for word in review if not word in set(stopwords)]
-    review = ' '.join(review)
-    xidf.append(review)
-    tf_idf_vectorizer = load_vectorizer()
-    # Transform the new review using the loaded vectorizer
-    tf_review = tf_idf_vectorizer.transform(xidf)
-    model = load_model()
-    prediction = model.predict(tf_review)
-    
-    return prediction
-
-def sentiment_analysis(texts):
-    MODEL = "cardiffnlp/twitter-roberta-base-sentiment"
-    task = 'sentiment'
-    tokenizer = AutoTokenizer.from_pretrained(MODEL)
-    config = AutoConfig.from_pretrained(MODEL)
-
-    # PT
-    model = AutoModelForSequenceClassification.from_pretrained(MODEL)
-
-    results = []
-    for text in texts:
-        encoded_input = tokenizer(text, return_tensors='pt', max_length=512, truncation=True)
-        output = model(**encoded_input)
-        scores = output[0][0].detach().numpy()
-        scores = softmax(scores)
-        results.append(scores.tolist())
-
-    return results
-
-def get_sentiment_label(row):
-    if row['positive_score'] > row['neutral_score'] and row['positive_score'] > row['negative_score']:
-        return 'positive'
-    elif row['negative_score'] > row['neutral_score'] and row['negative_score'] > row['positive_score']:
-        return 'negative'
-    else:
-        return 'neutral'
-
-
-
-st.set_option('deprecation.showPyplotGlobalUse', False)
-
-
-# Create two columns
-col1, col2 = st.columns([0.5, 1.2])  # Adjust the ratio as needed
-
-# Column 1: Image
-with col1:
-    st.image("img2.png", width=200)  # Adjust the path and width as needed
-
-# Column 2: Text
-with col2:
-    st.write("""
-    # Ratings Prediction & Reviews Sentiment Analysis App 
-    """)
-st.write(" This app predicts **the average rating of a product, given a list of reviews and also displays the sentiment of these reviews**!")
-st.write('---')
-
-
-
-sidebar_selection = st.sidebar.radio("Select an option:", ("Ratings Prediction", "Sentiment Analysis"))
-
-list_reviews = st.text_input("Enter the list of reviews: ")
-sentiment_review = list_reviews
-ratings_review = list_reviews
-submit_button = st.button("Submit")
-
-if sidebar_selection == "Ratings Prediction":
-    # Check if the submit button is clicked and the input is not empty
-    if submit_button and ratings_review:
-        rating_pred = ratings(ratings_review)
-        st.write(f"The predicted average rating for a product with the list of reviews above is: {rating_pred}")
-    elif submit_button:
-        # Display a message if the submit button is clicked but no review is provided
-        st.write("Please enter a review to get a prediction.")
-
-    
-elif sidebar_selection == "Sentiment Analysis":
-    if submit_button and sentiment_review:
-        # Create a DataFrame
-        # Split the string into a list of reviews
-        review_list = sentiment_review.split(',')
-        df = pd.DataFrame(review_list,  columns=['Review'])
-        scores = sentiment_analysis(df['Review'])
-
-        df['negative_score'] = [score[0] for score in scores]
-        df['neutral_score'] = [score[1] for score in scores]
-        df['positive_score'] = [score[2] for score in scores]
-
-        df['sentiment'] = df.apply(get_sentiment_label, axis=1)
-        
-        # Display the sentiment distribution chart using Streamlit
-        st.write("**Sentiment Distribution:**")
-        plt.figure(figsize=(8, 6))
-        sns.countplot(data=df, x='sentiment', color='blue')
-
-        # Display values on top of the bars
-        for p in plt.gca().patches:
-            plt.gca().annotate(f'{p.get_height()}', (p.get_x() + p.get_width() / 2, p.get_height()), ha='center',
-                           va='bottom')
-
-        # Set plot labels and title
-        plt.xlabel('Sentiment')
-        plt.ylabel('Count')
-        plt.title('Sentiment Distribution')
-
-        st.pyplot(plt)
-
-
-