app.py

import gradio as gr
from transformers import pipeline

# Load the Flan-T5-Large model and tokenizer via a pipeline.
# If you're on a GPU Space, device=0 will place it on GPU.
# If you're on CPU only, leave device=-1.
model_name = "google/flan-t5-large"
pipe = pipeline("text2text-generation", model=model_name, device=-1)

def judge_ecolinguistics(pairs_text):
    """
    Takes a multiline string of question–answer pairs and returns a model-generated
    scoring from 1 to 10 for each pair, along with a brief explanation.
    """
    # Construct a single prompt that instructs the model to score each Q&A pair.
    prompt = f"""
You are an ecolinguistics judge. You evaluate answers based on how thoroughly
they address ecological concerns, clarity of expression, and factual correctness.

Below is a set of question–answer pairs:

{pairs_text}

Please provide, for each pair, a single numerical score from 1 to 10 and a brief explanation.
"""
    # Use the pipeline to generate a response.
    response = pipe(prompt, max_length=512, truncation=True)[0]["generated_text"]
    return response

# Build the Gradio interface.
demo = gr.Interface(
    fn=judge_ecolinguistics,
    inputs=gr.Textbox(
        lines=10,
        label="Enter Your Question–Answer Pairs",
        placeholder="Example:\nQ1: What is an ecological niche?\nA1: It is the role a species plays in its environment.\n\nQ2: How does deforestation affect the climate?\nA2: It can reduce carbon sequestration and disrupt rainfall patterns.\n"
    ),
    outputs="text",
    title="Ecolinguistics Q&A Scorer (Flan-T5-Large)",
    description=(
        "Paste multiple question–answer pairs. The model will assign a score from 1–10 "
        "to each answer, considering ecological relevance and clarity. "
        "It will also provide a brief rationale for its scoring."
    )
)

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