import gradio as gr
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load models and tokenizers
sarcasm_model = AutoModelForSequenceClassification.from_pretrained("dnzblgn/Sarcasm-Detection-Customer-Reviews")
sentiment_model = AutoModelForSequenceClassification.from_pretrained("dnzblgn/Sentiment-Analysis-Customer-Reviews")
sarcasm_tokenizer = AutoTokenizer.from_pretrained("dnzblgn/Sarcasm-Detection-Customer-Reviews", use_fast=False)
sentiment_tokenizer = AutoTokenizer.from_pretrained("dnzblgn/Sentiment-Analysis-Customer-Reviews", use_fast=False)

# Function to analyze sentiment
def analyze_sentiment(sentence):
    inputs = sentiment_tokenizer(sentence, return_tensors="pt", truncation=True, padding=True, max_length=512)
    with torch.no_grad():
        outputs = sentiment_model(**inputs)
    logits = outputs.logits
    predicted_class = torch.argmax(logits, dim=-1).item()
    sentiment_mapping = {1: "Negative", 0: "Positive"}
    return sentiment_mapping[predicted_class]

# Function to detect sarcasm
def detect_sarcasm(sentence):
    inputs = sarcasm_tokenizer(sentence, return_tensors="pt", truncation=True, padding=True, max_length=512)
    with torch.no_grad():
        outputs = sarcasm_model(**inputs)
    logits = outputs.logits
    predicted_class = torch.argmax(logits, dim=-1).item()
    return "Sarcasm" if predicted_class == 1 else "Not Sarcasm"

# Combined function for processing sentences
def process_text_pipeline(text):
    sentences = text.split("\n")  # Split text into multiple sentences
    processed_sentences = []

    for sentence in sentences:
        sentence = sentence.strip()
        if not sentence:
            continue  # Skip empty lines

        sentiment = analyze_sentiment(sentence)
        if sentiment == "Negative":
            processed_sentences.append(f"❌ '{sentence}' -> Sentiment: Negative")
        else:
            sarcasm_result = detect_sarcasm(sentence)
            if sarcasm_result == "Sarcasm":
                processed_sentences.append(f"⚠️ '{sentence}' -> Sentiment: Negative (Sarcastic Positive)")
            else:
                processed_sentences.append(f"✅ '{sentence}' -> Sentiment: Positive")

    return "\n".join(processed_sentences)

# CSS for transparent boxes and background image
background_css = """
.gradio-container {
    background-image: url('https://huggingface.co/spaces/dnzblgn/Sarcasm_Detection/resolve/main/image.png');
    background-size: cover;
    background-position: center;
    color: white;
}

.gr-input, .gr-textbox {
    background-color: rgba(255, 255, 255, 0.3) !important;  /* Make the boxes transparent */
    border-radius: 10px;
    padding: 10px;
    color: black !important;
}

h1, h2, p {
    text-shadow: 1px 1px 2px rgba(0, 0, 0, 0.8);  /* Add subtle shadow to text for better readability */
}
"""

# Gradio UI with updated header and transparent design
with gr.Blocks(css=background_css) as interface:
    gr.Markdown(
        """
        <h1 style='text-align: center; font-size: 36px;'>🌟 Sentiment Analysis Powered by Sarcasm Detection 🌟</h1>
        <p style='text-align: center; font-size: 18px;'>Analyze the sentiment of customer reviews and detect sarcasm in positive reviews.</p>
        """
    )

    with gr.Tab("Text Input"):
        with gr.Row():
            text_input = gr.Textbox(
                lines=10,
                label="Enter Sentences",
                placeholder="Enter one or more sentences, each on a new line."
            )
            result_output = gr.Textbox(label="Results", lines=10, interactive=False)
        analyze_button = gr.Button("🔍 Analyze")

        analyze_button.click(process_text_pipeline, inputs=text_input, outputs=result_output)

    with gr.Tab("Upload Text File"):
        file_input = gr.File(label="Upload Text File")
        file_output = gr.Textbox(label="Results", lines=10, interactive=False)

        def process_file(file):
            text = file.read().decode("utf-8")
            return process_text_pipeline(text)

        file_input.change(process_file, inputs=file_input, outputs=file_output)

# Run the interface
if __name__ == "__main__":
    interface.launch()