app.py

import torch
import torch.nn as nn
from transformers import PegasusForConditionalGeneration, PegasusTokenizer, AutoTokenizer,AutoModelForSequenceClassification
from scipy.special import softmax
from tqdm.notebook import tqdm
from sklearn.metrics import accuracy_score, f1_score, confusion_matrix
from rouge_score import rouge_scorer
from rouge import Rouge
import streamlit as st
from ydata_profiling import ProfileReport
from streamlit_pandas_profiling import st_profile_report



import time
import io
import os
import pprint
from IPython.display import HTML
import traceback
import logging
import random
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import tensorflow as tf

st.set_page_config(page_title="Review Summary App", page_icon=None, layout="centered", initial_sidebar_state="auto", menu_items=None)
# st.set_page_config(layout="wide")
st.title("Review Summarizer App")
st.write("This app summarises all the reviews of a product")


@st.cache_resource#(allow_output_mutation=True)
def load_roberta_model_and_tokenizer(model_name):
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    model = AutoModelForSequenceClassification.from_pretrained(model_name)
    return tokenizer, model

@st.cache_resource#(allow_output_mutation=True)
def load_pegasus_model_and_tokenizer(model_name):
    tokenizer = PegasusTokenizer.from_pretrained(model_name)
    model = PegasusForConditionalGeneration.from_pretrained(model_name)
    return tokenizer, model

# =========================================================
# Define a function to assign labels based on star rating
# =========================================================
def assign_star_label(row):
    return 'positive' if row['star_rating'] > 3 else 'negative'

def showEda(df):
    pr = ProfileReport(df, explorative=True)
    st.header('**Pandas Profiling Report**')
    st_profile_report(pr) 
    

def dataset_load():

    with st.spinner("Importing modules................"):
        time.sleep(2)
    st.success("Imported Modules")    



    # ===================================================================================================================
    # ================================================= UTILITY FUNCTIONS ===============================================
    # ===================================================================================================================
    with st.spinner("Initialising methods ............"):
        # ==================
        # Load & Clean Data
        # ================== 
        @st.cache_data
        def data_load_clean_df():
            df = pd.read_csv('./amazon_reviews_us_Mobile_Electronics_v1_00.csv', on_bad_lines='skip')
        #     df = df.loc[df['product_id'].isin(['B00J46XO9U'])]
            df = df[['customer_id','product_title','star_rating','review_body','product_id']]
            df[~df.duplicated(subset='review_body')] #Remove duplicates
            df = df.apply(lambda row: row[df['star_rating'].isin(['1','2','3','4','5'])]) # Remove date fields inside star_rating
            df['star_rating']=df['star_rating'].astype('int64') # Convert data type for star_rating
            df['star_rating_label'] = df.apply(assign_star_label, axis=1) # Apply the function to create the 'label' column
            df['review_body'] = df['review_body'].apply(lambda x : str(x)) # Convert text inputs to STRING
            df['review_body'] = df['review_body'].apply(lambda x : x[:512]) # Limit length of string
            return df.reset_index(drop=True)


        # ======================
        # Assign Polarity Score
        # ======================    
        def polarity_scores_roberta(review):
            encoded_text = roberta_tokenizer(review, return_tensors='pt').to(device)
            with torch.no_grad():
                output = roberta_model(**encoded_text)
        #     scores = output[0][0].detach().numpy() # FOR CPU
            scores = softmax(output.logits.detach().cpu().numpy()) # CONVERT from GPU to CPU
            scores = softmax(scores[0])
            scores_dict = {
                'roberta_negative' : scores[0],
                'roberta_positive' : scores[1]
            }
            return scores_dict

        # ==================
        # Summarising Text
        # ================== 
        def text_summarizer(review):
            batch = pegasus_tokenizer(review, truncation=True, padding="longest", max_length=1024, return_tensors="pt").to(device)
            with torch.no_grad():
                translated = pegasus_model.generate(**batch)
                #translated = pegasus_model.module.generate(**batch) #When using Data Parallel
            tgt_text = pegasus_tokenizer.batch_decode(translated, skip_special_tokens=True)
            summary_dict = {"summary":tgt_text[0]}
            return summary_dict

        # =================
        # Rouge Score Check
        # =================   
        def rouge_score_viewer(original_text,generated_summary):
            # Create a Rouge object
            rouge = Rouge()

            # Calculate ROUGE scores
            scores = rouge.get_scores(generated_summary, original_text)

            # Print ROUGE scores
            return {"Rouge-1":scores[0]['rouge-1'],"Rouge-2":scores[0]['rouge-2'],"Rouge-L":scores[0]['rouge-l']}

        # =======================================================    
        # Define a function to assign labels based on star rating
        # =======================================================
        def assign_label(row):
            if row['roberta_positive'] > row['roberta_negative']:
                return 'positive'
            else:
                return 'negative'

        # =======================================================    
        # Summarise bunch of summaries together
        # =======================================================
        @st.cache_data
        def data_summarizer(df, marker, summary_count):
            summaries = []
            marker   = 'positive' if marker==1 else 'negative'
            df_new   = df[(df['star_rating_label']==marker) & (df['roberta_rating_label']==marker)]
            df_new = df_new[~df_new.duplicated(subset=["review_body","summary"])]
            sentence = df_new.sort_values(['roberta_positive','Rouge_1','Rouge_2','Rouge_L'],ascending=[False, False,False,False])['summary'].reset_index(drop=True) if marker==1 else df_new.sort_values(['roberta_negative','Rouge_1','Rouge_2','Rouge_L'],ascending=[False, False,False,False])['summary'].reset_index(drop=True)
            print(sentence)
            print(f"Sentence len :{len(sentence)}")
            count=0
            for i in range(0,len(sentence),10):
                if(count==summary_count):
                    break
                else:
                    chunk = sentence[i:i + 10]
                    joined_sentence = ' '.join(chunk)
                    print(f"JOINED SENTENCE :{joined_sentence}\n\n\n")
                    summaries.append(text_summarizer(joined_sentence[:512])["summary"])
                    count+=1
            print(f"SUMMARY IS:{summaries}\n")
            return summaries
        
        
        # ==========================================================   
        # Convert the array to a markdown string with bullet points
        # ==========================================================   
        def bullet_markdown(array):
            return "\n".join(f"- {item}" for item in array)
        
        # ==========================================================   
        # Get rows with same rating labels
        # ==========================================================        
        
        def getMatchCols(df,value):
            marker = "positive" if value == 1 else "negative"
            df_new = df[(df['star_rating_label']==marker) & (df['roberta_rating_label']==marker)]
            if df_new.shape[0]>0:
                return df_new.sort_values(['roberta_positive','Rouge_1','Rouge_2','Rouge_L'],ascending=[False,False,False,False])['review_body'].values
            else:
                return [f"No {marker} reviews available"]

    # =========================================================================================================================
    # ================================================= LOADING OF THE DATA ===================================================
    # =========================================================================================================================

    ## Load & Clean Data
    with st.spinner("Loading the data ............"):
#         st.header("Loaded Dataframe")
        df = data_load_clean_df()
    
    loaded_df = df.copy()
    # Controlling the sidebar for loaded DF and new DF with selected product
    ProductDataframeCheck = False    

    # TODO : Limit for demonstration only. Less rows to be analysed later
#     df = df.groupby('product_id').filter(lambda x: (len(x) <= 5)).reset_index(drop=True)
    st.header("The Dataframe loaded is shown below :")
    with st.spinner("Loading the data ............"):
        st.dataframe(df)
    # =========================================================================================================================
    # ================================================= LIST OF ALL PRODUCTS ==================================================
    # =========================================================================================================================
    with st.spinner("Loading list of products ............"):
        time.sleep(2)
        prod_ids = df['product_id'].unique()

    # =========================================================================================================================
    # ================================================= CHOOSE A PRODUCT ======================================================
    # =========================================================================================================================

    # Create a dual slider to select the range of product ids to display
    st.markdown("---")
    st.subheader("Step 0 : Choose a product")    
    
    
    # Group the dataframe by product_id and count the number of rows for each product_id
    grouped_df = df.groupby("product_id").size().reset_index(name="count")
    
#     st.dataframe(grouped_df)

    # Find the product_id with the maximum number of rows and store it in max_rows
    max_rows = grouped_df["count"].max()

    # Create a slider in streamlit with min value as 0, and max value as max_rows
#     slider_value = st.slider("Select the number of rows", min_value=1, max_value=max_rows)
    slider_value = st.select_slider("Select the number of rows", options=sorted(grouped_df['count'].unique()),value=max(grouped_df['count']))

    # Filter the grouped dataframe by the slider value and get the product_id column as a list
    filtered_df = grouped_df[grouped_df["count"] == slider_value]["product_id"].tolist()

    # Create a select box in streamlit with the filtered list of product_id
    st.write(f"There are {len(filtered_df)} products with {slider_value} rows")
    selected_product_id = st.selectbox("Select the product_id", filtered_df)
    
    

    preview_df = df.loc[df['product_id']==selected_product_id].reset_index(drop=True)
    
    if(not preview_df.empty):
        prod_name = preview_df['product_title'][0]

        # Display the selected product id
        st.markdown("---")
        st.subheader("Step 1 : Product Details :")
        st.write(f'Product Name : {prod_name}')
        st.write(f'Product ID   : {selected_product_id} ')
        st.write(f'Total Rows   : {preview_df.shape[0]}')
    
    #================================================================
    # Use the condition to control the display of the radio buttons
    #================================================================
    if(not preview_df.empty):
        ProductDataframeCheck = True
    
    if (not ProductDataframeCheck):
        option = st.sidebar.radio("Select an option", ["None","Show EDA"])
    else:
        option = st.sidebar.radio("Select an option", ["None","Show EDA", "Product EDA"])
        
    if(option=="Show EDA"):
        showEda(loaded_df)
    elif option=="Product EDA":
        showEda(preview_df)


    
    
    if st.button('Confirm Product'):
        df = df.loc[df['product_id']==selected_product_id].reset_index(drop=True)
        st.markdown("---")
        st.subheader("Step 2 : Dataframe with chosen product :")
        st.dataframe(df)
#         st.success(f"Dataframe loaded with product_id:{selected_product_id}")
    #     st.write(f"Selected product is {selected_product_id}, named as \"{df['product_title']}\" with dataframe having {df.shape[0]} rows")
    
        df_rows = df.shape[0]

        # =========================================================================================================================
        # ================================================ PRE-TRAINED MODEL ======================================================
        # =========================================================================================================================
        st.markdown("---")
        st.subheader("Step 3 : Initialising the models & running operation")
        with st.spinner("Initializing RoBERTa Model ............"):
            device = "cuda" if torch.cuda.is_available() else "cpu"
            st.write(f"Selected device for processing is (CPU/GPU) : {device.upper()}")

        # ROBERTA Model
        with st.spinner("Initializing RoBERTa Model ............"):
#             roberta_model_name = f"siebert/sentiment-roberta-large-english" 
#             roberta_tokenizer = AutoTokenizer.from_pretrained(roberta_model_name)
#             roberta_model = AutoModelForSequenceClassification.from_pretrained(roberta_model_name).to(device)

            roberta_model_name = "siebert/sentiment-roberta-large-english"
            roberta_tokenizer, roberta_model = load_roberta_model_and_tokenizer(roberta_model_name)
            roberta_model.to(device)

        
        # PEGASUS Model
        with st.spinner("Initializing Pegasus Model ............"):
#             pegasus_model_name = "google/pegasus-large"
#             pegasus_tokenizer = PegasusTokenizer.from_pretrained(pegasus_model_name)
#             pegasus_model = PegasusForConditionalGeneration.from_pretrained(pegasus_model_name).to(device)
            pegasus_model_name = "google/pegasus-large"
            pegasus_tokenizer, pegasus_model = load_pegasus_model_and_tokenizer(pegasus_model_name)
            pegasus_model.to(device)

        st.success("Models successfully loaded")
        # =========================================================================================================================
        # ================================================ RUN MODEL ON DATA ======================================================
        # =========================================================================================================================

        # Sentimental Analysis & Text Summarisation

        res = {}
        summaries = {}
        rouge_1 = {}
        rouge_2 = {}
        rouge_L = {}
        broken_ids = []

        with st.spinner("Operation in progress ............"):

            progress_bar_analysis = st.progress((0/len(df))*100, text="Please wait......... 0%")

            progress_percent = 0
            progress_text = f"Please wait......... {float(progress_percent):.2f}%"


            for i, row in tqdm(df.iterrows(), total=len(df)):

                progress_percent = (i/len(df))*100
                progress_text = f"Please wait......... {progress_percent:.2f}%"
                progress_bar_analysis.progress(int(progress_percent+1), text=progress_text)


                # Process Sentimental Analysis
                text = row['review_body']
                myid = row['customer_id']


                roberta_result = polarity_scores_roberta(text)
                both = {**roberta_result}
                res[myid] = both

                # Process Summaries
                summary_result = text_summarizer(text)
                summaries[myid] = {**summary_result}

                #Rouge SCore
                original_text      = row['review_body']
                generated_summary  = summary_result['summary']
                rouge_scores       = rouge_score_viewer(original_text,generated_summary)
                rouge_1[myid]={"rouge-1":rouge_scores['Rouge-1']['f']}
                rouge_2[myid]={"rouge-2":rouge_scores['Rouge-2']['f']}
                rouge_L[myid]={"rouge-L":rouge_scores['Rouge-L']['f']}
            progress_bar_analysis.progress(int(100), text="Completed......... 100%")
        st.success("Operation Completed")

        with st.spinner("Merging in progress ............"):

            # Merge dataframes
            results_df = pd.DataFrame(res).T    
            results_df['summary'] = (pd.DataFrame(summaries).T)['summary'].values #Add summary column
            results_df['Rouge_1'] = pd.DataFrame(rouge_1).T[:].values
            results_df['Rouge_2'] = pd.DataFrame(rouge_2).T[:].values
            results_df['Rouge_L'] = pd.DataFrame(rouge_L).T[:].values
            results_df = results_df.reset_index().rename(columns={'index': 'customer_id'})
            results_df = results_df.merge(df, how='left')

            results_df['roberta_rating_label'] = results_df.apply(assign_label, axis=1) # Apply the function to create the 'label' column
            st.markdown("---")
            st.subheader("Step 4 : Dataframe after operation")
#             st.dataframe(results_df)
#         st.success("Merge Completed")


        with st.spinner("Matching Columns in progress ............"):
            # prod_a = results_df.loc[results_df['product_id']=='B00J46XO9U']
            prod_a = results_df.copy()
            prod_a = prod_a[prod_a['star_rating_label'] == prod_a['roberta_rating_label']]
            prod_a.reset_index(drop=True)

#         st.success("Matching columns Completed")

#         st.header("Dataframe with matching labels")
        st.dataframe(prod_a)
        
        # =========================================================================================================================
        # ============================================= HISTOGRAM CHECK ======================================================
        # =========================================================================================================================        
        
        # # Create a histogram using matplotlib
        # plt.figure(figsize=(8, 6))
        # plt.hist(prod_a['Rouge_1'], bins=30, alpha=0.7, color='blue')  # Adjust bins and color as needed
        # plt.title('Histogram of Random Data')
        # plt.xlabel('Values')
        # plt.ylabel('Frequency')
        # plt.grid(True)
        # plt.show()
        
        # # Create a histogram using matplotlib
        # plt.figure(figsize=(8, 6))
        # plt.hist(prod_a['Rouge_2'], bins=30, alpha=0.7, color='blue')  # Adjust bins and color as needed
        # plt.title('Histogram of Random Data')
        # plt.xlabel('Values')
        # plt.ylabel('Frequency')
        # plt.grid(True)
        # plt.show()
        
        # # Create a histogram using matplotlib
        # plt.figure(figsize=(8, 6))
        # plt.hist(prod_a['Rouge_L'], bins=30, alpha=0.7, color='blue')  # Adjust bins and color as needed
        # plt.title('Histogram of Random Data')
        # plt.xlabel('Values')
        # plt.ylabel('Frequency')
        # plt.grid(True)
        # plt.show()


        # =========================================================================================================================
        # ============================================= CHECKING THE METRICS ======================================================
        # =========================================================================================================================
        
        # RUN only if NUMBER OF ROWS > 4
        if(df_rows>4):
            with st.spinner("Creating confusion matrix ............"):
                st.markdown("---")
                st.subheader("Step 5. - Confusion Matrix")
                # Sample confusion matrix (replace this with your actual data)
                conf_df = results_df.copy()
                actual_labels = conf_df['star_rating_label']
                predicted_labels = conf_df['roberta_rating_label']

                # Create the confusion matrix
                cm_a = confusion_matrix(actual_labels, predicted_labels)

                # Display the confusion matrix using seaborn
                st.set_option('deprecation.showPyplotGlobalUse', False)
                sns.heatmap(cm_a, annot=True, fmt='d')
                st.pyplot()


                # Extract true positives, false positives, false negatives, true negatives
                tn, fp, fn, tp = cm_a.ravel()

                # Calculate accuracy
                accuracy = accuracy_score(actual_labels, predicted_labels)

                # Calculate precision, recall, and F1 score
                precision = tp / (tp + fp)
                recall = tp / (tp + fn)
                f1 = 2 * (precision * recall) / (precision + recall)

                st.write(f"Accuracy :{accuracy*100:.2f} | Precision :{precision:.2f} | Recall:{recall:.2f} | F1-Score:{f1:.2f}")

        # =========================================================================================================================
        # ============================================= SUMMARRY OF PRODUCT =======================================================
        # =========================================================================================================================
        st.markdown("---")
        st.subheader("Step 6 : Summary of product")
        choice = 10#st.number_input("Choose number of summaries", 0, 10)

        # POSITIVE SUMMARIES
        st.header("Positive Reviews Summary")
        if(df_rows<=10):
            st.markdown(bullet_markdown(getMatchCols(prod_a,1)))
        else:
            with st.spinner("Generating Positive Summaries ............"):
                sum_list_pos = data_summarizer(prod_a,1,choice)
                st.markdown(bullet_markdown(sum_list_pos))

        # NEGATIVE SUMMARIES 
        st.header("Negative Reviews Summary")
        if(df_rows<=10):
            st.markdown(bullet_markdown(getMatchCols(prod_a,0)))
        else:
            with st.spinner("Generating Negative Summaries ............"):
                sum_list_neg =data_summarizer(prod_a,0,choice)
                st.markdown(bullet_markdown(sum_list_neg))
                
                
dataset_load()