app.py

import streamlit as st
import pandas as pd
from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
from textblob import TextBlob
from transformers import pipeline
import matplotlib.pyplot as plt
import base64
import os
from wordcloud import WordCloud
import time

# Function to perform sentiment analysis using Hugging Face model
hf_sentiment_analyzer = pipeline(
    "sentiment-analysis", "Dmyadav2001/Sentimental-Analysis"
)

def analyze_hf_sentiment(text):
    if len(text) > 512:
        temp = text[:511]
        text = temp
    result = hf_sentiment_analyzer(text)
    label = result[0]["label"]
    if label == "LABEL_1":
        return "Positive"
    elif label == "LABEL_0":
        return "Negative"
    elif label == "LABEL_2":
        return "Neutral"

# Function to perform sentiment analysis using VADER
def analyze_vader_sentiment(text):
    analyzer = SentimentIntensityAnalyzer()
    vader_score = analyzer.polarity_scores(text)["compound"]
    if vader_score > 0:
        return "Positive"
    elif vader_score == 0:
        return "Neutral"
    else:
        return "Negative"

# Function to perform sentiment analysis using TextBlob
def analyze_textblob_sentiment(text):
    analysis = TextBlob(text)
    sentiment_score = analysis.sentiment.polarity
    if sentiment_score > 0:
        return "Positive"
    elif sentiment_score == 0:
        return "Neutral"
    else:
        return "Negative"

# Function to display DataFrame with updated sentiment column
def display_dataframe(df):
    st.write(df)

# Function to display pie chart for sentiment distribution
def display_pie_chart(df, column):
    sentiment_counts = df[column].value_counts()
    fig, ax = plt.subplots()
    ax.pie(
        sentiment_counts,
        labels=sentiment_counts.index,
        autopct="%1.1f%%",
        startangle=140,
    )
    ax.axis("equal")
    st.pyplot(fig)

    # Add a download button
    if st.button('Download Pie Chart'):
        # Save the pie chart as an image file
        plt.savefig('pie_chart.png')
        
        # Offer the image file for download
        st.download_button(label='Download Pie Chart Image', data=open('pie_chart.png', 'rb').read(), file_name='pie_chart.png', mime='image/png')

# Function to display word cloud
def display_wordcloud(text_data):
    wordcloud = WordCloud(width=800, height=400, background_color="white").generate(
        text_data
    )
    fig, ax = plt.subplots(figsize=(10, 5))
    ax.imshow(wordcloud, interpolation="bilinear")
    ax.axis("off")
    st.pyplot(fig)

    # Add a download button
    if st.button('Download Word Cloud'):
        # Save the word cloud as an image file
        plt.savefig('word_cloud.png')
        
        # Offer the image file for download
        st.download_button(label='Download Word Cloud Image', data=open('word_cloud.png', 'rb').read(), file_name='word_cloud.png', mime='image/png')

# Function to download CSV file
def download_csv(df):
    csv = df.to_csv(index=False)
    b64 = base64.b64encode(csv.encode()).decode()  # B64 encoding
    href = f'<a href="data:file/csv;base64,{b64}" download="sentiment_analysis_results.csv">Download CSV File</a>'
    st.markdown(href, unsafe_allow_html=True)

# Function to count occurrences of keywords and sentiment distribution
def count_reviews_with_keywords(df,keywords):
    # keywords=['logistics', 'supply chain', 'cargo', 'shipment', 'freight', 'package', 'tracking']
    keyword_counts = {keyword: {"Positive": 0, "Negative": 0, "Total": 0} for keyword in keywords}

    for _, row in df.iterrows():
        review_text = row["review_text"]
        sentiment = row["Sentiment"]

        for keyword in keywords:
            if keyword.lower() in review_text.lower():
                keyword_counts[keyword]["Total"] += 1
                if sentiment == "Positive":
                    keyword_counts[keyword]["Positive"] += 1
                elif sentiment == "Negative":
                    keyword_counts[keyword]["Negative"] += 1

    return keyword_counts


# Streamlit UI
st.set_page_config(page_title="SentimentAnalysis App", page_icon=":smiley:")
# st.title("SentimentAnalysis App")
text = "SentimentAnalysis App"
t = st.empty()
for i in range(len(text) + 1):
    t.markdown("## %s" % text[0:i])
    time.sleep(0.1)

# Sidebar
st.sidebar.title("Options")
input_option = st.sidebar.radio("Select Input Option", ("Free Text", "CSV Files"))
selected_model = st.sidebar.radio(
    "Select Sentiment Analysis Model", ("VADER", "TextBlob", "Hugging Face")
)
result_option = st.sidebar.radio(
    "Select Result Display Option",
    ("DataFrame", "Pie Chart", "Bar Chart", "Keyword Frequency", "WordCloud", "Comparative Sentiment Analysis"),
)

# Main content
progress_label = st.empty()  # Define progress label
progress_bar = st.progress(0)
progress = 0

# Directory path to store processed files
processed_directory = "processed_files"

# Ensure the directory exists, if not create it
os.makedirs(processed_directory, exist_ok=True)

# List to store processed filenames
processed_files = []

# Function to get filenames from the processed directory
def get_processed_filenames():
    return [
        f
        for f in os.listdir(processed_directory)
        if os.path.isfile(os.path.join(processed_directory, f))
    ]

if input_option == "Free Text":
    st.subheader("Enter review for sentiment analysis:")
    user_input = st.text_area("", "")
    if not user_input:
        st.info("Enter some text above for sentiment analysis.")
    else:
        with st.spinner("Analyzing..."):
            if selected_model == "Hugging Face":
                result = analyze_hf_sentiment(user_input)
            elif selected_model == "VADER":
                result = analyze_vader_sentiment(user_input)
            elif selected_model == "TextBlob":
                result = analyze_textblob_sentiment(user_input)
        st.write("Sentiment:", result)

if input_option == "CSV Files":
    st.subheader("Select CSV files for sentiment analysis:")

    # Uploading new file
    files = st.file_uploader(
        "Upload New File", type=["csv"], accept_multiple_files=True
    )
    if files:
        # Process uploaded new files
        for file in files:
            if file.type != "text/csv":
                st.warning(
                    "Uploaded file is not a CSV file. Please upload a CSV file only."
                )
            else:
                df = pd.read_csv(file)
                if "review_text" not in df.columns:
                    st.warning(
                        "Uploaded CSV file doesn't contain 'review_text' column. Please check the CSV file format."
                    )
                else:
                    total_rows = len(df)

                    sentiments_v = []
                    sentiments_tb = []
                    sentiments_hf = []

                    for review_text in df["review_text"]:
                        sentiments_v.append(analyze_vader_sentiment(review_text))
                        sentiments_tb.append(analyze_textblob_sentiment(review_text))
                        sentiments_hf.append(analyze_hf_sentiment(review_text))
                        progress += 1
                        progress_label.text(f"{progress}/{total_rows}")
                        progress_bar.progress(min(progress / total_rows, 1.0))

                    df["VADER Sentiment"] = sentiments_v
                    df["TextBlob Sentiment"] = sentiments_tb
                    df["HuggingFace Sentiment"] = sentiments_hf

                    # Save processed file with modified filename
                    new_filename = os.path.splitext(file.name)[0] + "1.csv"
                    df.to_csv(
                        os.path.join(processed_directory, new_filename), index=False
                    )
                    st.success(f"New file processed and saved as {new_filename}")

    # List of already processed files
    processed_files = get_processed_filenames()
    selected_files = st.multiselect("Select from Processed Files", processed_files)

    if not files and not selected_files:
        st.info(
            "Upload a new CSV file or select from processed files above for sentiment analysis."
        )

    all_dfs = []

    # Process already selected files
    for file_name in selected_files:
        df = pd.read_csv(os.path.join(processed_directory, file_name))
        all_dfs.append(df)

    # Results
    if all_dfs:
        combined_df = pd.concat(all_dfs, ignore_index=True)
        if selected_model == "TextBlob":
            result = "TextBlob Sentiment"
            combined_df.drop(
                columns=["VADER Sentiment", "HuggingFace Sentiment"],
                inplace=True,
            )
        elif selected_model == "VADER":
            result = "VADER Sentiment"
            combined_df.drop(
                columns=["TextBlob Sentiment", "HuggingFace Sentiment"],
                inplace=True,
            )
        elif selected_model == "Hugging Face":
            result = "HuggingFace Sentiment"
            combined_df.drop(
                columns=["TextBlob Sentiment", "VADER Sentiment"],
                inplace=True,
            )
        combined_df.rename(columns={result: "Sentiment"}, inplace=True)

        if result_option == "DataFrame":
            st.subheader("Sentiment Analysis Results")
            display_dataframe(combined_df)
            download_csv(combined_df)
        elif result_option == "Pie Chart":
            st.subheader("Sentiment Distribution")
            display_pie_chart(combined_df, "Sentiment")
        elif result_option == "Bar Chart":
            # Calculate value counts
            sentiment_counts = combined_df["Sentiment"].value_counts()
            # Display bar chart
            st.bar_chart(sentiment_counts)

            # Add a download button
            if st.button('Download Sentiment Counts Chart'):
                # Plot the bar chart
                fig, ax = plt.subplots()
                sentiment_counts.plot(kind='bar', ax=ax)
                plt.xlabel('Sentiment')
                plt.ylabel('Count')
                plt.title('Sentiment Counts')
                plt.xticks(rotation=45, ha='right')
                plt.tight_layout()
                
                # Save the bar chart as an image file
                plt.savefig('sentiment_counts_chart.png')
                
                # Offer the image file for download
                st.download_button(label='Download Sentiment Counts Chart Image', data=open('sentiment_counts_chart.png', 'rb').read(), file_name='sentiment_counts_chart.png', mime='image/png')

        elif result_option == "Keyword Frequency":
            st.subheader("Keyword Frequency")

            # List of keywords
            keywords = [
                "delivery",
                "shipping",
                "parcel",
                "package",
                "tracking",
                "shipment",
                "cargo",
                "freight",
                "automation",
                "automated",
                "robotic",
                "robots",
                "AI",
                "artificial intelligence",
                "machine learning",
                "chatbot",
                "virtual assistant",
                "customer support",
                "real-time",
                "instant",
                "live update",
                "status",
                "IoT",
                "internet of things",
                "connected devices",
                "smart technology",
                "blockchain",
                "ledger",
                "transparency",
                "security",
                "sustainability",
                "eco-friendly",
                "green logistics",
                "carbon footprint",
                "customer service",
                "support",
                "experience",
                "satisfaction",
                "data analytics",
                "big data",
                "analysis",
                "insights",
                "cloud computing",
                "cloud-based",
                "digital infrastructure",
                "storage",
                "5G",
                "connectivity",
                "network speed",
                "wireless",
                "drone",
                "aerial delivery",
                "UAV",
                "drone shipping",
                "augmented reality",
                "AR",
                "virtual reality",
                "VR",
                "3D printing",
                "additive manufacturing",
                "custom parts",
                "prototyping",
                "inventory management",
                "stock levels",
                "warehouse management",
                "storage solutions",
                "supply chain",
                "logistics",
                "supply network",
                "distribution",
                "eco-packaging",
                "sustainable materials",
                "recycling",
                "waste reduction",
                "digital platform",
                "e-commerce",
                "online shopping",
                "online order",
                "cybersecurity",
                "data protection",
                "privacy",
                "encryption",
                "predictive modeling",
                "forecasting",
                "demand planning",
                "trend analysis",
                "robotics",
                "automated vehicles",
                "self-driving cars",
                "logistics automation",
                "visibility",
                "supply chain visibility",
                "track and trace",
                "monitoring",
                "integration",
                "ERP",
                "supply chain integration",
                "software",
                "optimization",
                "efficiency",
                "process improvement",
                "lean logistics",
                "personalization",
                "customization",
                "tailored services",
                "personal touch",
                "ethical sourcing",
                "fair trade",
                "labor rights",
                "ethical business",
                "user experience",
                "UX",
                "customer journey",
                "service design",
                "visibility",
            ]
            text_data = " ".join(combined_df["review_text"])
            keyword_frequency = (
                pd.Series(text_data.split()).value_counts().reset_index()
            )
            keyword_frequency.columns = ["Keyword", "Frequency"]

            # Filter keyword frequency for specific keywords
            filtered_keyword_frequency = keyword_frequency[
                keyword_frequency["Keyword"].isin(keywords)
            ]

            # Display bar chart for filtered keyword frequency
            st.bar_chart(filtered_keyword_frequency.set_index("Keyword"))

             # Add a download button
            if st.button('Download Keyword Frequency Chart'):
                # Plot the bar chart
                fig, ax = plt.subplots()
                filtered_keyword_frequency.plot(kind='bar', x='Keyword', y='Frequency', ax=ax)
                plt.xticks(rotation=45, ha='right')
                plt.tight_layout()
                
                # Save the bar chart as an image file
                plt.savefig('keyword_frequency_chart.png')
                
                # Offer the image file for download
                st.download_button(label='Download Keyword Frequency Chart Image', data=open('keyword_frequency_chart.png', 'rb').read(), file_name='keyword_frequency_chart.png', mime='image/png')
        elif result_option == "Word Cloud":
            st.subheader("Word Cloud")
            text_data = " ".join(combined_df["review_text"])
            display_wordcloud(text_data)
        else:
            st.subheader("Comparative Sentiment Analysis")            
            supply_chain_areas = {
                'logistics': ['logistics', 'supply chain', 'cargo', 'shipment', 'freight', 'package', 'tracking'],
                'delivery': ['delivery', 'shipping', 'courier', 'postal', 'parcel'],
                'inventory': ['inventory', 'stock', 'storage', 'warehouse', 'security’'],
                'customer service': ['customer service', 'support', 'helpdesk', 'service center', 'experience', 'refund'],
                'procurement': ['procurement', 'sourcing', 'purchasing', 'buying', 'order'],
                'distribution': ['distribution', 'supply network', 'distribution center'],
                'manufacturing': ['manufacturing', 'production', 'assembly', 'quality', 'defect']
            }

            supply_chain_area = st.sidebar.radio(
                "Select Supply Chain Area",
                ("logistics", "delivery", "inventory", "customer service", "procurement", "distribution","manufacturing"),
            )
            # Call the function to count occurrences of keywords and sentiment distribution
            keyword_counts = count_reviews_with_keywords(combined_df,supply_chain_areas[supply_chain_area])

            # Convert keyword_counts to DataFrame
            df_counts = pd.DataFrame(keyword_counts).transpose()

            # Plot dual bar chart horizontally
            st.bar_chart(df_counts[["Positive", "Negative"]], use_container_width=True, height=500)