import gradio as gr
from transformers import MT5ForConditionalGeneration, AutoTokenizer

# Load pre-trained model and tokenizer
model_name = "persiannlp/mt5-small-parsinlu-sentiment-analysis"
model = MT5ForConditionalGeneration.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

def analyze_sentiment(text):
    # Prepare input for the model
    inputs = tokenizer.encode("sentiment: " + text, return_tensors="pt", max_length=512, truncation=True)
    
    # Generate output
    outputs = model.generate(inputs, max_length=2)
    
    # Decode the sentiment label
    sentiment = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return sentiment

with gr.Blocks() as demo:
    gr.Markdown("Enter Persian text to get its sentiment analyzed")
    input_text = gr.Textbox(label="Status")
    output_text = gr.Textbox(label="Status")
    submit_button = gr.Button("Upload and Process")
    

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

# Launch the app
demo.launch()

# # Define Gradio interface
# iface = gr.Interface(
#     fn=analyze_sentiment,
#     inputs="text",
#     outputs="text",
#     title="Persian Sentiment Analysis",
#     description="Enter Persian text to get its sentiment analyzed."
# )

# # Launch the app
# if __name__ == "__main__":
#     iface.launch(server_port=7865)