app.py

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

# Load the pre-trained model and tokenizer
model_name = "distilbert-base-uncased"  # Replace this with the desired model
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Define a function for sentiment analysis
def predict_sentiment(text):
    # Tokenize the input text and prepare it to be used by the model
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)

    # Forward pass through the model
    with torch.no_grad():
        outputs = model(**inputs)

    # Get the predicted probabilities and convert them to percentages
    probabilities = torch.softmax(outputs.logits, dim=1).squeeze().tolist()
    positive_percent = probabilities[2] * 100
    negative_percent = probabilities[0] * 100
    neutral_percent = probabilities[1] * 100

    # Construct the result dictionary
    result = {
        "Positive": round(positive_percent, 2),
        "Negative": round(negative_percent, 2),
        "Neutral": round(neutral_percent, 2)
    }

    return result

iface = gr.Interface(
    fn=predict_sentiment,
    inputs=gr.inputs.Textbox(lines=10, label="Enter financial statement"),
    outputs=gr.outputs.Label(num_top_classes=3, label="Sentiment Percentages"),
    title="Financial Statement Sentiment Analysis",
    description="Predict the sentiment percentages of a financial statement."
)

if __name__ == "__main__":
    iface.launch()