app1.py

# app.py

import gradio as gr
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline

# Only the official Google FLAN-T5 models
MODEL_OPTIONS = {
    "FLAN-T5-small (Google)": "google/flan-t5-small",
    "FLAN-T5-base (Google)": "google/flan-t5-base"
}

# Cache loaded pipelines
loaded_pipelines = {}

def get_pipeline(model_id: str):
    if model_id not in loaded_pipelines:
        tokenizer = AutoTokenizer.from_pretrained(model_id)
        model = AutoModelForSeq2SeqLM.from_pretrained(
            model_id,
            low_cpu_mem_usage=True,   # CPU optimization
            torch_dtype="auto"
        )
        pipe = pipeline("text2text-generation",
                        model=model,
                        tokenizer=tokenizer,
                        device=-1)
        # Warm-up to avoid first-call lag
        _ = pipe("Correct the grammar: test", max_new_tokens=8, do_sample=False)
        loaded_pipelines[model_id] = pipe
    return loaded_pipelines[model_id]

def oxford_polish(sentence: str, model_choice: str) -> str:
    model_id = MODEL_OPTIONS[model_choice]
    polisher = get_pipeline(model_id)

    # Minimal prompt for FLAN-T5
    prompt = f"You are an English grammar corrector and teacher. Return the corrected version: {sentence}"
    out = polisher(prompt,
                   max_new_tokens=60,
                   do_sample=False,
                   num_beams=2)
    text = out[0]["generated_text"].strip()

    # Strip accidental echo
    if text.startswith(prompt):
        text = text[len(prompt):].strip()
    return text

# Gradio interface
demo = gr.Interface(
    fn=oxford_polish,
    inputs=[
        gr.Textbox(lines=2, placeholder="Enter a sentence to correct..."),
        gr.Dropdown(choices=list(MODEL_OPTIONS.keys()),
                    value="FLAN-T5-base (Google)",
                    label="Choose Model")
    ],
    outputs=gr.Textbox(label="Oxford-grammar Correction"),
    title="Oxford Grammar Polisher",
    description="Compare Google’s official FLAN-T5 small and base models for grammar correction."
)

demo.launch()