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import gradio as gr |
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import torch |
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from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification |
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classifier_1 = pipeline( |
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"text-classification", |
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model=AutoModelForSequenceClassification.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment-latest"), |
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tokenizer=AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment-latest"), |
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device=0 if torch.cuda.is_available() else -1 |
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) |
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classifier_2 = pipeline( |
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"text-classification", |
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model=AutoModelForSequenceClassification.from_pretrained("finiteautomata/bertweet-base-sentiment-analysis"), |
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tokenizer=AutoTokenizer.from_pretrained("finiteautomata/bertweet-base-sentiment-analysis"), |
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device=0 if torch.cuda.is_available() else -1 |
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) |
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arbitro = pipeline( |
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"text2text-generation", |
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model="google/flan-t5-large", |
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device=0 if torch.cuda.is_available() else -1 |
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) |
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def classifier_1_predict(text): |
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result = classifier_1(text)[0]['label'] |
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return result |
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def classifier_2_predict(text): |
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result = classifier_2(text)[0]['label'] |
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return result |
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def judge_sentiment(text, result_1, result_2): |
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prompt = ( |
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f"Sentence: \"{text}\"\n" |
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f"Model A prediction: {result_1} (uses labels: negative, neutral, positive)\n" |
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f"Model B prediction: {result_2} (uses labels: neg, neu, pos)\n\n" |
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"Interpret the real sentiment expressed in the sentence.\n" |
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"Judge whether each prediction is good or bad, and explain the parameters (weights) that influenced your decision.\n\n" |
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"Your judgment criteria (weights from 0 to 1):\n" |
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"- Semantic match (meaning alignment with the sentence)\n" |
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"- Tone match (emotional consistency)\n" |
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"- Label accuracy (correct label among known sentiment labels)\n\n" |
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"Respond ONLY in this format:\n" |
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"Model A: good | Model B: bad\n" |
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"Weights used:\n" |
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"- Semantic match: 0.4\n" |
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"- Tone match: 0.4\n" |
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"- Label accuracy: 0.2\n" |
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"Explanation: [your reasoning here]" |
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) |
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output = arbitro(prompt, max_new_tokens=150)[0]['generated_text'].strip() |
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return f"Model A: {result_1} | Model B: {result_2}\n🤖 Árbitro:\n{output}" |
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def process_input(text): |
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result_1 = classifier_1_predict(text) |
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result_2 = classifier_2_predict(text) |
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decision = judge_sentiment(text, result_1, result_2) |
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return decision |
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iface = gr.Interface( |
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fn=process_input, |
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inputs="text", |
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outputs="text", |
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title="AI Sentiment Duel: Classificador de Sentimentos", |
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description=( |
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"Compare duas IAs na tarefa de identificar o sentimento de uma frase. " |
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"Uma terceira IA, chamada **árbitro**, decide qual resposta está mais correta e agora **explica os critérios (pesos) usados para julgar**.\n\n" |
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"**Critérios de julgamento do árbitro:**\n" |
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"- **Semantic match**: o quanto a resposta combina com o significado geral da frase.\n" |
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"- **Tone match**: o quanto a resposta combina com o tom emocional.\n" |
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"- **Label accuracy**: se o rótulo está entre os mais apropriados.\n\n" |
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"⚠️ Melhores resultados com frases em inglês.\n\n" |
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"💡 Exemplos:\n" |
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"- 'I absolutely loved the movie!'\n" |
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"- 'Not bad, but could be better.'" |
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) |
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) |
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iface.launch(share=True) |
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