import gradio as gr
from transformers import pipeline
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier
from sklearn.pipeline import make_pipeline
from sklearn.model_selection import train_test_split
from sklearn import metrics
import pandas as pd
from transformers.utils import logging

logging.set_verbosity("ERROR")

# Load the provided dataset
file_path = 'data.csv'
df = pd.read_csv(file_path)

# Split data into training and test sets
X_train, X_test, y_train, y_test = train_test_split(df['Sentence'], df['Sentiment'], test_size=0.2, random_state=42)

# Define models
nb_model = make_pipeline(TfidfVectorizer(), MultinomialNB())
svm_model = make_pipeline(TfidfVectorizer(), SVC(probability=True))
rf_model = make_pipeline(TfidfVectorizer(), RandomForestClassifier())

# Train models
nb_model.fit(X_train, y_train)
svm_model.fit(X_train, y_train)
rf_model.fit(X_train, y_train)

# Define sentences to choose from
sentences = [
    "The announced restructuring will erase the company's indebtedness.",
    "UPM-Kymmene upgraded to `in-line' from `underperform' by Goldman Sachs.",
    "Profitability (in EBIT %) was not impressive due to expenses rising by 14.3%.",
    "The Finnish bank has issued a profit warning.",
    "TeliaSonera's underlying results however included 457 mln SKr in positive one-offs, hence the adjusted underlying EBITDA actually amounts to 7.309 bln SKr, clearly below expectations, analysts said."
]

# Function to map BERT labels
def map_bert_label(label):
    if label in ["1 star", "2 stars"]:
        return "negative"
    elif label == "3 stars":
        return "neutral"
    elif label in ["4 stars", "5 stars"]:
        return "positive"

# Function to analyze sentiment
def analyze_sentiment(sentence):
    # Define model paths
    model_paths = {
        "BERT": "nlptown/bert-base-multilingual-uncased-sentiment",
    }
    
    # Analyze sentiment using transformers models
    results = {}
    for model_name, model_path in model_paths.items():
        sentiment_analyzer = pipeline("sentiment-analysis", model=model_path)
        result = sentiment_analyzer(sentence[:512])[0]  # Analyze first 512 characters for brevity
        if model_name == "BERT":
            result['label'] = map_bert_label(result['label'])
        results[model_name] = result
    
    # Analyze sentiment using sklearn models
    results["Naive Bayes"] = {"label": nb_model.predict([sentence])[0],
                              "score": nb_model.predict_proba([sentence]).max()}
    results["SVM"] = {"label": svm_model.predict([sentence])[0],
                      "score": svm_model.predict_proba([sentence]).max()}
    results["Random Forest"] = {"label": rf_model.predict([sentence])[0],
                                "score": rf_model.predict_proba([sentence]).max()}
    
    return sentence, results


# Define custom CSS with slightly larger font size
custom_css = """
.gradio-container, .gradio-container * {
    font-size: 0.65rem !important;
}
.gradio-container h1 {
    font-size: 1.1rem !important;
}
.gradio-container h2, .gradio-container h3 {
    font-size: 0.9rem !important;
}
.gradio-container .label {
    font-size: 0.75rem !important;
}
.gradio-container .output-markdown pre {
    font-size: 0.6rem !important;
}
"""

# Create Gradio interface with custom CSS
with gr.Blocks(css=custom_css) as demo:
    gr.Markdown("# Compare Sentiment Analysis Across Models")
    gr.Markdown("Select a sentence to see sentiment analysis results from multiple models.")
    
    dropdown = gr.Dropdown(choices=sentences, label="Select Sentence")
    text_output = gr.Textbox(label="Selected Sentence", lines=2)
    sentiment_output = gr.JSON(label="Sentiment Scores")
    
    dropdown.change(analyze_sentiment, inputs=[dropdown], outputs=[text_output, sentiment_output])

demo.launch()