frontend/app.py

# frontend/app.py

import streamlit as st
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import sys
import os

# Add the parent directory of 'backend' to the Python path
# This allows importing 'backend' as a package
# os.path.abspath(os.path.join(os.path.dirname(__file__), '..')) points to 'sentilyze/'
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))

# Import functions directly from the 'backend' package
# The __init__.py in backend handles the individual imports
from backend import analyze_sentiment, process_csv_for_dashboard, detect_sarcasm_and_highlight

# --- Streamlit App Configuration ---
st.set_page_config(
    page_title="Sentilyze - Sentiment & Sarcasm Analyzer",
    page_icon="✨",
    layout="wide",
    initial_sidebar_state="expanded"
)

# --- Custom CSS for better aesthetics ---
st.markdown("""
    <style>
    .main-header {
        font-size: 3em;
        font-weight: bold;
        color: #4CAF50;
        text-align: center;
        margin-bottom: 30px;
        text-shadow: 2px 2px 4px #aaaaaa;
    }
    .stButton>button {
        background-color: #4CAF50;
        color: white;
        border-radius: 12px;
        padding: 10px 24px;
        font-size: 18px;
        border: none;
        cursor: pointer;
        transition: all 0.3s ease;
        box-shadow: 0 4px 8px 0 rgba(0,0,0,0.2);
    }
    .stButton>button:hover {
        background-color: #45a049;
        box-shadow: 0 6px 12px 0 rgba(0,0,0,0.3);
        transform: translateY(-2px);
    }
    .stTextInput>div>div>input {
        border-radius: 12px;
        border: 1px solid #ccc;
        padding: 10px;
        box-shadow: inset 0 1px 3px rgba(0,0,0,0.1);
    }
    .stFileUploader>div>div>button {
        background-color: #2196F3;
        color: white;
        border-radius: 12px;
        padding: 10px 24px;
        font-size: 18px;
        border: none;
        cursor: pointer;
        transition: all 0.3s ease;
        box-shadow: 0 4px 8px 0 rgba(0,0,0,0.2);
    }
    .stFileUploader>div>div>button:hover {
        background-color: #0b7dda;
        box-shadow: 0 6px 12px 0 rgba(0,0,0,0.3);
        transform: translateY(-2px);
    }
    .stAlert {
        border-radius: 12px;
    }
    mark {
        background-color: #FFEB3B; /* Yellow highlight */
        padding: 2px 5px;
        border-radius: 3px;
    }
    </style>
    """, unsafe_allow_html=True)


# --- Header ---
st.markdown("<h1 class='main-header'>Sentilyze ✨</h1>", unsafe_allow_html=True)
st.write("Analyze sentiment, detect sarcasm, and visualize insights from your text data.")

# --- Navigation (using Streamlit's sidebar for sections) ---
st.sidebar.title("Navigation")
page = st.sidebar.radio("Go to", ["Single Text Analysis", "CSV File Analysis", "About"])

# --- Single Text Analysis Section ---
if page == "Single Text Analysis":
    st.header("Analyze Single Text")
    user_input = st.text_area("Enter text here:", "This product is absolutely fantastic!", height=150)

    col1, col2 = st.columns(2)

    with col1:
        if st.button("Analyze Sentiment"):
            if user_input:
                sentiment_result = analyze_sentiment(user_input)
                st.success(f"**Sentiment:** {sentiment_result['class'].capitalize()}")
                st.info(f"**Polarity Score:** {sentiment_result['polarity']:.2f} (closer to 1 is positive, -1 is negative)")
            else:
                st.warning("Please enter some text to analyze sentiment.")

    with col2:
        if st.button("Detect Sarcasm"):
            if user_input:
                sarcasm_result = detect_sarcasm_and_highlight(user_input)
                st.success(f"**Sarcasm Probability:** {sarcasm_result['sarcasm_percent']:.2f}%")
                # FIX: Changed 'predicted_sentiment_label' to 'predicted_sarcasm_label'
                st.info(f"**Predicted Sarcasm (Model's view):** {sarcasm_result['predicted_sarcasm_label'].capitalize()}")
                st.markdown(f"**Highlighted Text:** {sarcasm_result['highlighted_sentence']}", unsafe_allow_html=True)
                if "note" in sarcasm_result:
                    st.caption(f"Note: {sarcasm_result['note']}")
            else:
                st.warning("Please enter some text to detect sarcasm.")

# --- CSV File Analysis Section ---
elif page == "CSV File Analysis":
    st.header("Analyze CSV File")
    st.write("Upload a CSV file containing text data for sentiment analysis and dashboard visualization.")

    uploaded_file = st.file_uploader("Choose a CSV file", type="csv")

    if uploaded_file is not None:
        # Save the uploaded file temporarily to process it with pandas
        # In a real app, consider more robust temporary file handling or direct BytesIO
        temp_filepath = os.path.join("data", uploaded_file.name)
        with open(temp_filepath, "wb") as f:
            f.write(uploaded_file.getbuffer())

        df = process_csv_for_dashboard(temp_filepath)

        if not df.empty:
            st.success("CSV file uploaded and processed successfully!")
            st.subheader("Raw Data Preview:")
            st.dataframe(df.head())

            # Allow user to select the text column
            text_columns = [col for col in df.columns if df[col].dtype == 'object'] # Assuming text is object/string type
            if not text_columns:
                st.error("No text columns found in the CSV. Please ensure your CSV has columns with review text.")
            else:
                selected_text_column = st.selectbox(
                    "Select the column containing text/reviews for analysis:",
                    text_columns
                )

                if st.button(f"Perform Sentiment Analysis on '{selected_text_column}'"):
                    with st.spinner("Analyzing sentiment... This might take a while for large files."):
                        # Apply sentiment analysis to the selected column
                        df['Sentiment'] = df[selected_text_column].astype(str).apply(lambda x: analyze_sentiment(x)['class'])
                        df['Polarity'] = df[selected_text_column].astype(str).apply(lambda x: analyze_sentiment(x)['polarity'])

                    st.subheader("Sentiment Analysis Results:")
                    st.dataframe(df[[selected_text_column, 'Sentiment', 'Polarity']].head())

                    st.subheader("Sentiment Distribution:")
                    sentiment_counts = df['Sentiment'].value_counts()
                    st.bar_chart(sentiment_counts)

                    # Interactive Dashboard Elements
                    st.subheader("Interactive Dashboard")

                    # Pie chart for sentiment distribution
                    fig1, ax1 = plt.subplots()
                    sentiment_counts.plot.pie(autopct='%1.1f%%', startangle=90, ax=ax1,
                                            colors=['#4CAF50', '#FFC107', '#F44336']) # Positive, Neutral, Negative
                    ax1.set_ylabel('') # Hide the default 'Sentiment' label
                    ax1.set_title('Overall Sentiment Distribution')
                    st.pyplot(fig1)

                    # Histogram of Polarity Scores
                    fig2, ax2 = plt.subplots()
                    sns.histplot(df['Polarity'], bins=20, kde=True, ax=ax2, color='#2196F3')
                    ax2.set_title('Distribution of Polarity Scores')
                    ax2.set_xlabel('Polarity Score')
                    ax2.set_ylabel('Frequency')
                    st.pyplot(fig2)

                    # Display data by sentiment type
                    st.subheader("View Data by Sentiment Type")
                    sentiment_filter = st.selectbox(
                        "Filter by Sentiment:",
                        ["All", "positive", "neutral", "negative"]
                    )
                    if sentiment_filter == "All":
                        st.dataframe(df[[selected_text_column, 'Sentiment', 'Polarity']])
                    else:
                        filtered_df = df[df['Sentiment'] == sentiment_filter]
                        st.dataframe(filtered_df[[selected_text_column, 'Sentiment', 'Polarity']])

        else:
            st.error("Could not process the CSV file. Please check its format.")
        
        # Clean up the temporary file
        if os.path.exists(temp_filepath):
            os.remove(temp_filepath)


# --- About Section ---
elif page == "About":
    st.header("About Sentilyze")
    st.write("""
        Sentilyze is a web application designed to help you understand the sentiment and nuances
        of text data. It offers:

        - **Single Text Analysis:** Quickly determine the sentiment (positive, neutral, negative)
          and potential sarcasm of individual pieces of text.
        - **CSV File Analysis:** Upload your own datasets (e.g., customer reviews, social media comments)
          and get an interactive dashboard showing sentiment distribution and polarity.
        - **Sarcasm Detection:** A feature to estimate the sarcasm percentage in a sentence,
          with basic highlighting (note: advanced sarcasm highlighting is a complex NLP task).

        **Technologies Used:**
        - **Backend:** Python, `pandas`, `TextBlob`, `transformers` (Hugging Face)
        - **Frontend:** Streamlit
        - **Deployment:** Docker, GitHub, (potential platforms like Streamlit Community Cloud, Heroku, Render)

        **Developed by:** [Your Name/Team Name Here]
        """)
    st.markdown("[GitHub Repository (Coming Soon!)](#)", unsafe_allow_html=True)

# --- Footer ---
st.markdown("""
    <hr>
    <p style='text-align: center; color: grey;'>Sentilyze © 2023</p>
    """, unsafe_allow_html=True)