app.py

import gradio as gr
from huggingface_hub import InferenceClient
from transformers import AutoTokenizer, AutoModelForCausalLM

# Initialize the Hugging Face model and tokenizer
model_name = "HooshvareLab/gpt2-fa"  # Example Persian GPT-2 model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)


def suggest_next_word(prompt, num_suggestions=3,temper=0.7):
    """
    Suggests the next word based on the given prompt.
    :param prompt: The text input so far.
    :param num_suggestions: Number of next-word suggestions to generate.
    :return: List of suggested next words.
    """
    # Tokenize input and generate predictions
    input_ids = tokenizer.encode(prompt, return_tensors="pt")
    outputs = model.generate(
        input_ids,
        max_length=len(input_ids[0]) + 1,
        num_return_sequences=num_suggestions,
        do_sample=True,
        top_k=50,  # Adjust for randomness in suggestions
        temperature=temper,  # Adjust for creativity in suggestions
        pad_token_id=tokenizer.eos_token_id,  # Set pad_token_id explicitly
    )
    # Decode and extract next words
    suggestions = []
    for output in outputs:
        decoded_text = tokenizer.decode(output, skip_special_tokens=True)
        next_word = decoded_text[len(prompt):].strip().split()[0]  # Get the next word
        suggestions.append(next_word)
    
    return suggestions


# Gradio interface
def chat_interface(prompt, num_suggestions,temperature):
    suggestions = suggest_next_word(prompt, num_suggestions,temperature)
    return " | ".join(suggestions)


# Define Gradio app
with gr.Blocks() as demo:
    gr.Markdown("# Persian Language Next Word Predictor")
    
    prompt_input = gr.Textbox(label="Enter your prompt:", lines=2)
    num_suggestions_input = gr.Slider(
        minimum=1, maximum=5, value=3, step=1, label="Number of suggestions"
    )
    temperature_input = gr.Slider(
        minimum=0.1, maximum=2, value=1, step=0.1, label="Temperature"
    )
    output = gr.Textbox(label="Next Word Suggestions:")

    # Link input and output
    suggest_btn = gr.Button("Suggest Next Word")
    suggest_btn.click(
        chat_interface,
        inputs=[prompt_input, num_suggestions_input,temperature_input],
        outputs=output
    )


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