import gradio as gr
from transformers import pipeline, PipelineException

# Load a summarization model from Hugging Face
summarizer = pipeline("summarization")

def evaluate_text_against_rubric(rubric, text):
    # Split rubric into criteria
    criteria = [criterion.strip() for criterion in rubric.split('\n') if criterion.strip()]
    
    if not criteria:
        return "No valid criteria found in the rubric."
    
    if not text:
        return "No text provided for evaluation."
    
    evaluations = {}
    for i, criterion in enumerate(criteria):
        try:
            summary = summarizer(text, max_length=50, min_length=25, do_sample=False)[0]['summary_text']
            evaluations[f'Criteria {i+1}'] = {
                "Criterion": criterion,
                "Score": 3,  # Dummy score for now
                "Comment": f"Evaluation based on criterion: {criterion}",
                "Example": summary
            }
        except PipelineException as e:
            evaluations[f'Criteria {i+1}'] = {
                "Criterion": criterion,
                "Score": 0,
                "Comment": f"Error during evaluation: {str(e)}",
                "Example": ""
            }
        except Exception as e:
            evaluations[f'Criteria {i+1}'] = {
                "Criterion": criterion,
                "Score": 0,
                "Comment": f"An unexpected error occurred: {str(e)}",
                "Example": ""
            }
    
    return evaluations

def evaluate(rubric, text):
    evaluation = evaluate_text_against_rubric(rubric, text)
    
    if isinstance(evaluation, str):  # If it's an error message
        return evaluation
    
    evaluation_text = ""
    for criterion, details in evaluation.items():
        evaluation_text += f"{criterion}:\n"
        evaluation_text += f"  Criterion: {details['Criterion']}\n"
        evaluation_text += f"  Score: {details['Score']}\n"
        evaluation_text += f"  Comment: {details['Comment']}\n"
        evaluation_text += f"  Example: {details['Example']}\n\n"
    
    return evaluation_text

# Create Gradio interface
interface = gr.Interface(
    fn=evaluate,
    inputs=[
        gr.Textbox(lines=10, placeholder="Enter your rubric here..."),
        gr.Textbox(lines=10, placeholder="Paste the text to be evaluated here...")
    ],
    outputs="text",
    title="Text Evaluator",
    description="Upload or enter a rubric and paste text for evaluation. The system will grade the text against the rubric and provide feedback."
)

# Launch the interface
interface.launch()