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import gradio as gr |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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import torch |
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import csv |
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from datetime import datetime |
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base_model_name = "unsloth/Llama-3.2-1B-Instruct" |
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finetuned_model_name = "exo-is/esg-context-llama-1Bst-11M" |
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tokenizer = AutoTokenizer.from_pretrained(base_model_name) |
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base_model = AutoModelForCausalLM.from_pretrained(base_model_name, torch_dtype=torch.float32, low_cpu_mem_usage=True, device_map="cpu") |
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finetuned_model = AutoModelForCausalLM.from_pretrained(finetuned_model_name, torch_dtype=torch.float32, low_cpu_mem_usage=True, device_map="cpu") |
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def generate_text(model, prompt, max_new_tokens, temperature): |
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inputs = tokenizer(prompt, return_tensors="pt") |
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with torch.no_grad(): |
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outputs = model.generate( |
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**inputs, |
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max_new_tokens=int(max_new_tokens), |
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temperature=temperature, |
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num_return_sequences=1, |
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do_sample=True, |
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) |
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return tokenizer.decode(outputs[0], skip_special_tokens=True) |
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def log_interaction(model, prompt, output, validation): |
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timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S") |
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with open('interaction_log.csv', 'a', newline='') as file: |
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writer = csv.writer(file) |
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writer.writerow([timestamp, model, prompt, output, validation]) |
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def generate_and_compare(prompt, max_new_tokens, temperature): |
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base_output = generate_text(base_model, prompt, max_new_tokens, temperature) |
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finetuned_output = generate_text(finetuned_model, prompt, max_new_tokens, temperature) |
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return base_output, finetuned_output |
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def evaluate_base(output, score): |
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log_interaction("Base", gr.get_state('last_prompt'), output, score) |
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return f"Avaliação registrada para o modelo Base: {score}" |
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def evaluate_finetuned(output, score): |
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log_interaction("Fine-tuned", gr.get_state('last_prompt'), output, score) |
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return f"Avaliação registrada para o modelo Fine-tuned: {score}" |
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with gr.Blocks() as demo: |
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gr.Markdown("# Comparação de Modelos: Llama-3.2-1B-Instruct vs. Modelo Fine-tuned para Sustentabilidade") |
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with gr.Row(): |
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prompt = gr.Textbox(lines=5, label="Insira seu prompt aqui") |
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max_new_tokens = gr.Slider(50, 500, value=200, step=1, label="Máximo de Novos Tokens") |
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temperature = gr.Slider(0.1, 2.0, value=0.7, step=0.1, label="Temperatura") |
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generate_btn = gr.Button("Gerar") |
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with gr.Row(): |
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with gr.Column(): |
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base_output = gr.Textbox(label="Saída do Modelo Base", lines=10) |
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base_rating = gr.Radio(["1", "2", "3", "4", "5"], label="Avalie a resposta do Modelo Base") |
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base_submit = gr.Button("Enviar Avaliação (Base)") |
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with gr.Column(): |
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finetuned_output = gr.Textbox(label="Saída do Modelo Fine-tuned", lines=10) |
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finetuned_rating = gr.Radio(["1", "2", "3", "4", "5"], label="Avalie a resposta do Modelo Fine-tuned") |
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finetuned_submit = gr.Button("Enviar Avaliação (Fine-tuned)") |
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base_feedback = gr.Textbox(label="Feedback da Avaliação (Base)") |
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finetuned_feedback = gr.Textbox(label="Feedback da Avaliação (Fine-tuned)") |
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generate_btn.click(generate_and_compare, inputs=[prompt, max_new_tokens, temperature], outputs=[base_output, finetuned_output]) |
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base_submit.click(evaluate_base, inputs=[base_output, base_rating], outputs=base_feedback) |
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finetuned_submit.click(evaluate_finetuned, inputs=[finetuned_output, finetuned_rating], outputs=finetuned_feedback) |
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demo.load(lambda: gr.update(value=""), outputs=[prompt]) |
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prompt.change(lambda x: gr.set_state(last_prompt=x), inputs=[prompt]) |
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demo.launch() |
