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from huggingface_hub import InferenceClient |
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import gradio as gr |
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import random |
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import pandas as pd |
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from io import BytesIO |
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import csv |
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import os |
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import io |
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import tempfile |
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import re |
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from transformers import MarianMTModel, MarianTokenizer |
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client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1") |
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def extract_text_from_excel(file): |
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df = pd.read_excel(file) |
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text = ' '.join(df['Unnamed: 1'].astype(str)) |
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return text |
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def save_to_csv(sentence, output, filename="synthetic_data.csv"): |
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with open(filename, mode='a', newline='', encoding='utf-8') as file: |
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writer = csv.writer(file) |
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writer.writerow([sentence, output]) |
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def translate_english_to_azerbaijani(text): |
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model_name = 'Helsinki-NLP/opus-mt-en-az' |
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tokenizer = MarianTokenizer.from_pretrained(model_name) |
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model = MarianMTModel.from_pretrained(model_name) |
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translated = model.generate(**tokenizer.prepare_translation_batch([text])) |
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translated_text = tokenizer.decode(translated[0], skip_special_tokens=True) |
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return translated_text |
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def generate(file, temperature, max_new_tokens, top_p, repetition_penalty): |
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text = extract_text_from_excel(file) |
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sentences = text.split('.') |
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random.shuffle(sentences) |
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with tempfile.NamedTemporaryFile(mode='w', newline='', delete=False, suffix='.csv') as tmp: |
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fieldnames = ['Original Sentence', 'Generated Sentence'] |
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writer = csv.DictWriter(tmp, fieldnames=fieldnames) |
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writer.writeheader() |
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for sentence in sentences: |
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sentence = sentence.strip() |
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if not sentence: |
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continue |
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generate_kwargs = { |
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"temperature": temperature, |
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"max_new_tokens": max_new_tokens, |
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"top_p": top_p, |
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"repetition_penalty": repetition_penalty, |
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"do_sample": True, |
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"seed": 42, |
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} |
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try: |
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stream = client.text_generation(sentence, **generate_kwargs, stream=True, details=True, return_full_text=False) |
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output = "" |
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for response in stream: |
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output += response.token.text |
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generated_sentences = re.split(r'(?<=[\.\!\?:])[\s\n]+', output) |
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generated_sentences = [s.strip() for s in generated_sentences if s.strip() and s != '.'] |
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for generated_sentence in generated_sentences: |
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translated_original = translate_english_to_azerbaijani(sentence) |
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translated_generated = translate_english_to_azerbaijani(generated_sentence) |
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writer.writerow({'Original Sentence': translated_original, 'Generated Sentence': translated_generated}) |
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except Exception as e: |
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print(f"Error generating data for sentence '{sentence}': {e}") |
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tmp_path = tmp.name |
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return tmp_path |
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gr.Interface( |
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fn=generate, |
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inputs=[ |
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gr.File(label="Upload Excel File", file_count="single", file_types=[".xlsx"]), |
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gr.Slider(label="Temperature", value=0.9, minimum=0.0, maximum=1.0, step=0.05, interactive=True, info="Higher values produce more diverse outputs"), |
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gr.Slider(label="Max new tokens", value=256, minimum=0, maximum=5120, step=64, interactive=True, info="The maximum numbers of new tokens"), |
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gr.Slider(label="Top-p (nucleus sampling)", value=0.95, minimum=0.0, maximum=1, step=0.05, interactive=True, info="Higher values sample more low-probability tokens"), |
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gr.Slider(label="Repetition penalty", value=1.0, minimum=1.0, maximum=2.0, step=0.1, interactive=True, info="Penalize repeated tokens"), |
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], |
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outputs=gr.File(label="Synthetic Data "), |
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title="SDG", |
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description="AYE QABIL.", |
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allow_flagging="never", |
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).launch() |
