interface_local.py

import gradio as gr
import requests
import json
import os
from evaluator.chrf import calculate_chrf
from evaluator.comet import calculate_comet  # Import the COMET function
from pathlib import Path

# OpenAI API URL and key
OPENAI_API_URL = "https://api.openai.com/v1/chat/completions"
# Check for required environment variables
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
if not OPENAI_API_KEY:
    raise ValueError("OPENAI_API_KEY not found. Please set this environment variable.")


CHATGPT_MODELS = {
    "GPT-4": "gpt-4"
}

def improve_translations(system_prompt, temperature, top_p):
    # Load data
    data_dir = Path(__file__).parent / "evaluator" / "mt_data"
    source_sentences = (data_dir / "source_sentences.txt").read_text(encoding="utf-8").splitlines()
    beam_search_translations = (data_dir / "beam_search_translations.txt").read_text(encoding="utf-8").splitlines()
    reference_translations = (data_dir / "reference_translations.txt").read_text(encoding="utf-8").splitlines()

    improved_translations = []
    sentence_pairs = []  # To store source, draft 1, draft 2, and reference

    for source, target, reference in zip(source_sentences, beam_search_translations, reference_translations):
        # Construct the prompt
        user_prompt = f"""
        As an expert translation post editor, your task is to improve the English translation (Target) for the below German text (Source)
        Source: {source}
        Target: {target}
        Your output should be your improved version of the target text only. Do not add any comments or explanations before or after the improved version of the target text. 
        """

        # Prepare API payload
        payload = {
            "model": CHATGPT_MODELS["GPT-4"],
            "messages": [
                {"role": "system", "content": system_prompt},
                {"role": "user", "content": user_prompt}
            ],
            "temperature": temperature,
            "top_p": top_p,
            "max_tokens": 512
        }

        headers = {
            "Authorization": f"Bearer {OPENAI_API_KEY}",
            "Content-Type": "application/json"
        }

        # Call OpenAI API
        response = requests.post(OPENAI_API_URL, headers=headers, json=payload)
        response.raise_for_status()
        data = response.json()

        # Extract improved translation
        output = data["choices"][0]["message"]["content"]
        improved_translation = output.split("Improved Translation:")[-1].strip()
        improved_translations.append(improved_translation)

        # Add sentence pair to the list
        sentence_pairs.append([source, target, improved_translation, reference])

    # Calculate ChrF scores
    beam_chrf_scores = [
        calculate_chrf(beam_translation, reference)
        for beam_translation, reference in zip(beam_search_translations, reference_translations)
    ]
    improved_chrf_scores = [
        calculate_chrf(improved_translation, reference)
        for improved_translation, reference in zip(improved_translations, reference_translations)
    ]

    # Calculate COMET scores
    beam_comet_scores = calculate_comet(source_sentences, beam_search_translations, reference_translations)
    improved_comet_scores = calculate_comet(source_sentences, improved_translations, reference_translations)

    # Calculate average scores
    average_beam_chrf = sum(beam_chrf_scores) / len(beam_chrf_scores)
    average_improved_chrf = sum(improved_chrf_scores) / len(improved_chrf_scores)
    average_beam_comet = sum(beam_comet_scores) / len(beam_comet_scores)
    average_improved_comet = sum(improved_comet_scores) / len(improved_comet_scores)

    # Calculate score changes
    chrf_change = average_improved_chrf - average_beam_chrf
    comet_change = average_improved_comet - average_beam_comet

    # Prepare dataframes
    sentence_pairs_df = sentence_pairs  # Dataframe for sentence pairs
    scores_df = [
        ["ChrF", round(average_beam_chrf, 2), round(average_improved_chrf, 2), round(chrf_change, 2)],
        ["COMET", round(average_beam_comet, 2), round(average_improved_comet, 2), round(comet_change, 2)]
    ]

    # Return dataframes and evaluation message
    evaluation_message = f"ChrF Change: {(average_improved_chrf/chrf_change):.2f}%, COMET Change: {(average_improved_comet/comet_change):.2f}%"
    return sentence_pairs_df, scores_df, evaluation_message

# Gradio interface
app = gr.Interface(
    fn=improve_translations, 
    inputs=[
        gr.Textbox(label="System Prompt", placeholder="Define the assistant's behavior here..."),
        gr.Slider(value=1, minimum=0, maximum=1.9, step=0.1, label="Temperature"),
        gr.Slider(value=1, minimum=0, maximum=1, step=0.01, label="Top P")
    ],
    outputs=[
        gr.Dataframe(headers=["Source text", "Draft 1", "Draft 2", "Reference"], label="Sentence Pairs"),
        gr.Dataframe(headers=["Metric", "Draft 1", "Draft 2", "Change"], label="Scores"),
        gr.Textbox(label="Evaluation Results")
    ],
    title="Translation Post-Editing and Evaluation",
    description="Improve translations using GPT-4 and evaluate the results with ChrF and COMET."
)



if __name__ == "__main__":
    # Try different ports in case 7860 is occupied
    for port in range(7860, 7870):
        try:
            app.launch(
                server_name="127.0.0.1",  # localhost
                server_port=port,
                share=False,  # Don't create public URL
                debug=True    # Show detailed errors
            )
            break
        except OSError:
            if port == 7869:  # Last attempt
                print("Could not find an available port between 7860-7869")
                raise
            continue