import gradio as gr
from transformers import AutoTokenizer
import torch
from tiny_finbert import TinyFinBERTRegressor, preprocess_texts
import os
import nltk

nltk.download('stopwords')

MODEL_DIR = "./saved_model"
DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
tokenizer = AutoTokenizer.from_pretrained(MODEL_DIR)

model = TinyFinBERTRegressor().to(DEVICE)
model.load_state_dict(torch.load(os.path.join(MODEL_DIR, "regressor_model.pt"), map_location=DEVICE))
model.eval()

def predict_sentiment(text):
    processed = preprocess_texts([text])[0]
    inputs = tokenizer(processed, return_tensors="pt", truncation=True, padding='max_length', max_length=128)
    inputs = {k: v.to(DEVICE) for k, v in inputs.items() if k != "token_type_ids"}
    with torch.no_grad():
        score = model(**inputs)["score"].item()

    if score > 0.3:
        interpretation = "positive"
    elif score < -0.3:
        interpretation = "negative"
    else:
        interpretation = "neutral"
    return round(score, 4), interpretation

iface = gr.Interface(fn=predict_sentiment,
                     inputs=gr.Textbox(label="Enter financial sentence"),
                     outputs=[
                         gr.Number(label="Sentiment Score"),
                         gr.Textbox(label="Interpretation")
                     ],
                     title="TinyFinBERT Sentiment Analysis",
                     # allow_api=True
                     api_name="predict"
                     )

#iface.launch()
iface.launch(
    server_name="0.0.0.0",
    share=True,
    #enable_queue=False
    max_threads=40,
    show_api=True
)