import streamlit as st
from textblob import TextBlob
import json
import os
import matplotlib.pyplot as plt

# === UI/UX Modifications ===
def set_background_and_styles():
    st.markdown(
        """
        <style>
        @import url('https://fonts.googleapis.com/css2?family=Montserrat:wght@300;400;700&display=swap');
        @import url('https://fonts.googleapis.com/css2?family=Merriweather:wght@300;400;700&display=swap');

        .stApp {
            background-image: url("https://i.pinimg.com/1200x/f0/ca/71/f0ca7163efb2ecc676426ae5ba1c1723.jpg");
            background-size: cover;
            background-position: center;
            background-repeat: no-repeat;
            background-attachment: fixed;
            font-family: 'Montserrat', sans-serif;
            color: white; /* Make all general text white */
        }
        
        /* Changed all header colors to white, also targeting labels directly */
        h1, h2, h3, h4, h5, h6, .stMarkdown, label { /* Added .stMarkdown and label for white text */
            font-family: 'Merriweather', serif;
            color: white; 
        }
        
        /* Specific target for the text area label, just in case */
        .stTextArea > label {
            color: white !important;
        }

        .stButton>button {
            background-color: #4682B4; /* Steel Blue */
            color: white;
            border-radius: 8px;
            border: none;
            padding: 10px 20px;
            font-size: 16px;
            font-weight: bold;
            transition: all 0.2s ease-in-out;
        }
        .stButton>button:hover {
            background-color: #5F9EA0; /* Cadet Blue */
            transform: translateY(-2px);
            box-shadow: 0 4px 8px rgba(0, 0, 0, 0.2);
        }
        
        .stTextInput>div>div>input, .stTextArea>div>div>textarea {
            border-radius: 8px;
            border: 1px solid #B0C4DE; /* LightSteelBlue */
            padding: 10px;
            background-color: rgba(255, 255, 255, 0.85);
            color: #333; /* Input text color should remain dark for readability */
        }

        .stSelectbox>div>div>div {
            border-radius: 8px;
            border: 1px solid #B0C4DE;
            background-color: rgba(255, 255, 255, 0.85);
            color: #333; /* Selectbox text color should remain dark for readability */
        }
        
        /* Modified stSuccess and stInfo for white text on darker transparent black background */
        .stSuccess { 
            border-left: 5px solid #28a745; 
            background-color: rgba(0, 0, 0, 0.6); /* Transparent black */
            color: white; /* Make text white */
            border-radius: 8px;
            padding: 10px;
            margin-bottom: 10px;
        }
        .stInfo { 
            border-left: 5px solid #17a2b8; 
            background-color: rgba(0, 0, 0, 0.6); /* Transparent black */
            color: white; /* Make text white */
            border-radius: 8px;
            padding: 10px;
            margin-bottom: 10px;
        }
        .stWarning { 
            border-left: 5px solid #ffc107; 
            background-color: rgba(255, 255, 255, 0.9); /* Keep existing light for warning */
            color: #333; /* Keep dark for warning */
            border-radius: 8px;
            padding: 10px;
            margin-bottom: 10px;
        }
        .stError { 
            border-left: 5px solid #dc3545; 
            background-color: rgba(255, 255, 255, 0.9); /* Keep existing light for error */
            color: #333; /* Keep dark for error */
            border-radius: 8px;
            padding: 10px;
            margin-bottom: 10px;
        }

        /* Custom container for content with blur background */
        .main-content-container {
            background-color: rgba(255, 255, 255, 0.7); /* Slightly transparent white */
            backdrop-filter: blur(5px); /* Blur effect */
            border-radius: 15px;
            padding: 30px;
            margin: 20px auto; /* Centering the container */
            max-width: 550px; /* Significantly less wide */
            box-shadow: 0 8px 16px rgba(0, 0, 0, 0.2);
        }

        /* Wider layout (adjusted for much less wide) */
        .st-emotion-cache-fg4lbf { 
            max-width: 600px !important; /* Adjusted for less wide */
            padding-left: 0 !important; 
            padding-right: 0 !important;
        }
        .st-emotion-cache-1d391kg { 
            padding-top: 2rem;
            padding-bottom: 2rem;
        }
        .block-container { /* Ensure content inside main-content-container respects its max-width */
            padding-left: 1rem;
            padding-right: 1rem;
            color: white; /* Ensure text within block-container is white */
        }


        /* Center plots */
        .st-emotion-cache-1pxazr6 { /* Specific Streamlit container for pyplot */
            display: flex;
            justify-content: center;
        }

        /* Hide the top grey bar */
        header.st-emotion-cache-1gh8zsi {
            display: none !important;
        }
        div.st-emotion-cache-fis6y8 { 
            padding-top: 0 !important;
        }
        div.st-emotion-cache-z5inrg { 
            display: none !important;
        }
        </style>
        """,
        unsafe_allow_html=True
    )

# === Load Quotes ===
QUOTES_PATH = os.path.join(os.path.dirname(__file__), "quotes.json")
# Create a dummy quotes.json if it doesn't exist for the script to run locally without error
if not os.path.exists(QUOTES_PATH):
    dummy_quotes = {
        "positive": ["You are doing great!", "Keep shining!", "Believe in yourself!"],
        "negative": ["It's okay to feel down, brighter days are ahead.", "Take a deep breath.", "This too shall pass."],
        "neutral": ["Observe your thoughts.", "Find your center.", "Acknowledge your feelings."]
    }
    with open(QUOTES_PATH, "w") as f:
        json.dump(dummy_quotes, f, indent=4)

with open(QUOTES_PATH, "r") as f:
    quotes_data = json.load(f)

# === Emotion Detection via TextBlob ===
def detect_emotion(text):
    blob = TextBlob(text)
    polarity = blob.sentiment.polarity

    if polarity > 0.2:
        return "positive", polarity
    elif polarity < -0.2:
        return "negative", polarity
    else:
        return "neutral", polarity

# === Quotes Retrieval ===
def get_quote(emotion):
    if isinstance(quotes_data, dict):
        # Return a random quote from the list for the given emotion
        import random
        return [random.choice(quotes_data.get(emotion, ["Stay strong, you're doing great!"]))]
    else:
        return ["Stay strong, you're doing great!"]

# === Page Layout ===
st.set_page_config(page_title="DilBot - Emotionally Intelligent AI Companion", layout="centered") 
set_background_and_styles()

# This div will act as the centered container
st.markdown('<div class="main-content-container">', unsafe_allow_html=True) 

# Motivational quote above title
st.markdown("<p style='text-align: center; font-style: italic; color: white;'>\"The only way to do great work is to love what you do.\"</p>", unsafe_allow_html=True)
st.title("DilBot: Your Emotional Companion") 
st.markdown("---")

# === Input Section ===
# Label color is now white via CSS
user_input = st.text_area("How are you feeling today?", height=150, placeholder="Type your thoughts here...") 

if st.button("Analyze"):
    if user_input.strip():
        # Detect Emotion
        emotion, polarity = detect_emotion(user_input)
        confidence = abs(polarity) * 100
        
        # Display Emotion - now white text on transparent black background
        st.subheader("Detected Emotion:") 
        st.success(f"**{emotion.capitalize()}** with a confidence of **{confidence:.2f}%**")

        # Show a personal quote - now white text on transparent black background
        st.markdown("### DilBot's Message:") 
        quote = get_quote(emotion)[0]
        st.info(f"_{quote}_")

        # === Personal Dashboard ===
        st.markdown("### Personal Dashboard") 

        # Bar & Doughnut Chart
        fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(6, 3), dpi=100) 

        labels = ['Positive', 'Neutral', 'Negative']
        
        display_values = [0, 0, 0]
        if emotion == 'positive':
            display_values[0] = confidence
            remaining = 100 - confidence
            display_values[1] = remaining / 2
            display_values[2] = remaining / 2
        elif emotion == 'negative':
            display_values[2] = confidence
            remaining = 100 - confidence
            display_values[0] = remaining / 2
            display_values[1] = remaining / 2
        else: # neutral
            display_values[1] = confidence
            remaining = 100 - confidence
            display_values[0] = remaining / 2
            display_values[2] = remaining / 2
        
        # Ensure values sum to 100 for the pie chart
        total_sum = sum(display_values)
        if total_sum != 0: 
            display_values = [v * 100 / total_sum for v in display_values]

        colors = ['#00cc99', '#b0bec5', '#ff6f61']

        # Set chart backgrounds to white
        ax1.set_facecolor('white') 
        ax2.set_facecolor('white') 
        fig.patch.set_facecolor('white') # Entire figure background white

        # Bar Chart
        ax1.bar(labels, display_values, color=colors)
        ax1.set_title("Confidence by Emotion", color='black') 
        ax1.set_ylabel("Percentage", color='black') 
        ax1.tick_params(axis='x', colors='black') 
        ax1.tick_params(axis='y', colors='black') 
        ax1.spines['bottom'].set_color('black') 
        ax1.spines['left'].set_color('black') 
        ax1.spines['top'].set_visible(False) 
        ax1.spines['right'].set_visible(False) 

        # Doughnut Chart
        if sum(display_values) > 0:
            wedges, texts, autotexts = ax2.pie(
                display_values, labels=labels, autopct='%1.1f%%',
                colors=colors, startangle=90, wedgeprops=dict(width=0.4)
            )
            ax2.set_title("Emotion Distribution", color='black') 
            for text_obj in texts: 
                text_obj.set_color('black') 
            for autotext_obj in autotexts: 
                autotext_obj.set_color('black') 

        else:
            ax2.text(0.5, 0.5, 'No data', horizontalalignment='center', verticalalignment='center', transform=ax2.transAxes, color='black')

        # Use st.columns to center the plot
        col_left, col_center, col_right = st.columns([0.5, 6, 0.5]) 
        with col_center:
            st.pyplot(fig) 
        plt.close(fig) 

    else:
        st.warning("Please write something to analyze.")

st.markdown('</div>', unsafe_allow_html=True) # Close main-content-container