# Import the necessary libraries
import gradio as gr
from transformers import pipeline
import traceback # Import the traceback module to get detailed errors

# --- Configuration ---
# Define the models we want to offer in our application.
MODELS = {
    "English": "distilbert-base-uncased-finetuned-sst-2-english",
    "Persian (Farsi)": "m3hrdadfi/albert-fa-base-v2-sentiment-binary",
}

# --- State and Caching ---
pipeline_cache = {}

def get_pipeline(model_name):
    """
    Loads a sentiment-analysis pipeline for the given model name.
    """
    if model_name not in pipeline_cache:
        print(f"Loading pipeline for model: {model_name}...")
        pipeline_cache[model_name] = pipeline("sentiment-analysis", model=model_name)
        print("Pipeline loaded successfully.")
    return pipeline_cache[model_name]

# --- Core Logic with Error Handling ---
def analyze_sentiment(text, model_choice):
    """
    Analyzes the sentiment of a given text using the selected model.
    Includes a try-except block to catch and display any errors.
    """
    try:
        if not text:
            return "Please enter some text to analyze."
        
        model_name = MODELS[model_choice]
        sentiment_pipeline = get_pipeline(model_name)
        
        result = sentiment_pipeline(text)[0]
        label = result['label']
        score = result['score']
        
        return f"Sentiment: {label} (Score: {score:.4f})"
    except Exception as e:
        # If any error occurs, format it and return it to the UI.
        # This is our debugging tool!
        error_details = traceback.format_exc()
        print(error_details) # Also print to server logs if we can see them
        return f"An error occurred:\n\n{error_details}"

# --- Gradio Interface ---
with gr.Blocks() as iface:
    gr.Markdown("# Multi-Lingual Sentiment Analyzer")
    gr.Markdown("Select a language, enter some text, and see the sentiment analysis. The first time you select a language, the model will take a moment to load.")
    
    with gr.Row():
        model_selector = gr.Dropdown(
            choices=list(MODELS.keys()), 
            value="English", 
            label="Select Language Model"
        )
        output_text = gr.Textbox(label="Result", interactive=False, lines=10) # Made the box bigger for errors

    input_text = gr.Textbox(lines=5, placeholder="Enter text here...")
    submit_button = gr.Button("Analyze Sentiment")

    submit_button.click(
        fn=analyze_sentiment,
        inputs=[input_text, model_selector],
        outputs=output_text
    )

# Launch the web application
if __name__ == "__main__":
    print("Launching Gradio interface...")
    iface.launch()