app.py

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

# Load the model and tokenizer (same as your original code)
model_name = "frameai/PersianSentiment"
loaded_tokenizer = AutoTokenizer.from_pretrained(model_name)
loaded_model = AutoModelForSequenceClassification.from_pretrained(model_name)


def predict_sentiment(text):
    """Predicts the sentiment of a given text."""
    inputs = loaded_tokenizer(text, return_tensors="pt", padding=True, truncation=True)  # Add padding and truncation
    outputs = loaded_model(**inputs)
    # Use softmax to get probabilities and argmax to get the predicted class
    probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)
    predictions = torch.argmax(probabilities, dim=-1).item()

    if predictions == 0:
        sentiment = "Negative"
    elif predictions == 1:
        sentiment = "Positive"
    else:
        sentiment = "Neutral"

    #  Return probabilities as well for a more informative output
    return {
        "Negative": float(probabilities[0][0]),
        "Positive": float(probabilities[0][1]),
        "Neutral": float(probabilities[0][2]),
    }, sentiment

# Create example sentences
examples = [
    ["این فیلم عالی بود!"],  # Positive example
    ["من این غذا را دوست نداشتم."],  # Negative example
    ["هوا خوب است."],  # Neutral (could be slightly positive, depends on context)
    ["کتاب جالبی بود اما کمی خسته کننده هم بود."] , # Mixed/Neutral
      ["اصلا راضی نبودم."] #negative
]


# Create the Gradio interface
iface = gr.Interface(
    fn=predict_sentiment,
    inputs=gr.Textbox(label="Enter Persian Text", lines=5, placeholder="Type your text here..."),
    outputs=[
        gr.Label(label="Sentiment Probabilities"),
        gr.Textbox(label="Predicted Sentiment") # Add output component for the sentiment string

    ],
    title="Persian Sentiment Analysis",
    description="Enter a Persian sentence and get its sentiment (Positive, Negative, or Neutral).",
    examples=examples,
    live=False  # set to True for automatic updates as you type
)


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