Update app.py

app.py

@@ -1,24 +1,34 @@
-# app.py
 import gradio as gr
-from transformers import pipeline
+from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqGeneration
 import torch
 import warnings
 warnings.filterwarnings('ignore')
 
-# Configuração de dispositivo
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
 try:
-    # Modelos para diferentes tarefas
     models = {
-        'transcription': pipeline("automatic-speech-recognition", model="openai/whisper-small", device=device),
-        'translation_en_pt': pipeline("translation", model="Helsinki-NLP/opus-mt-en-pt", device=device),
-        'translation_pt_en': pipeline("translation", model="Helsinki-NLP/opus-mt-pt-en", device=device),
-        'summarization': pipeline("summarization", model="facebook/bart-large-cnn", device=device),
-        'sentiment': pipeline("sentiment-analysis", model="nlptown/bert-base-multilingual-uncased-sentiment", device=device),
-        'question_answering': pipeline("question-answering", model="deepset/roberta-base-squad2", device=device),
-        'chat': pipeline("text-generation", model="facebook/opt-1.3b", device=device)
+        'transcription': pipeline("automatic-speech-recognition", 
+                                model="openai/whisper-small", 
+                                device=device),
+        'summarization': pipeline("summarization", 
+                                model="facebook/bart-large-cnn", 
+                                device=device),
+        'sentiment': pipeline("sentiment-analysis", 
+                            model="nlptown/bert-base-multilingual-uncased-sentiment", 
+                            device=device),
+        'question_answering': pipeline("question-answering", 
+                                     model="deepset/roberta-base-squad2", 
+                                     device=device),
+        'chat': pipeline("text-generation", 
+                        model="facebook/opt-125m", 
+                        device=device)
     }
+    
+    # Carregando modelos de tradução
+    tokenizer_en_pt = AutoTokenizer.from_pretrained("unicamp-dl/translation-en-pt-t5")
+    model_en_pt = AutoModelForSeq2SeqGeneration.from_pretrained("unicamp-dl/translation-en-pt-t5")
+    
 except Exception as e:
     print(f"Erro ao carregar modelos: {str(e)}")
 
@@ -30,43 +40,30 @@ def safe_process(func):
             return f"Erro ao processar: {str(e)}"
     return wrapper
 
-@safe_process
-def transcribe(audio):
-    if not audio:
-        return "Por favor, forneça um arquivo de áudio."
-    return models['transcription'](audio)["text"]
-
 @safe_process
 def translate(text, direction):
     if not text:
         return "Por favor, insira um texto para tradução."
-    model = models[f'translation_{direction}']
-    return model(text)[0]['translation_text']
-
-@safe_process
-def summarize(text):
-    if not text:
-        return "Por favor, insira um texto para resumir."
-    return models['summarization'](text, max_length=130, min_length=30)[0]['summary_text']
-
-@safe_process
-def analyze_sentiment(text):
-    if not text:
-        return "Por favor, insira um texto para análise."
-    return models['sentiment'](text)[0]['label']
-
-@safe_process
-def answer_question(question, context):
-    if not question or not context:
-        return "Por favor, forneça tanto a pergunta quanto o contexto."
-    return models['question_answering'](question=question, context=context)['answer']
+        
+    input_text = text
+    if direction == "pt_en":
+        input_text = f"translate Portuguese to English: {text}"
+    else:
+        input_text = f"translate English to Portuguese: {text}"
+        
+    inputs = tokenizer_en_pt(input_text, return_tensors="pt", max_length=512, truncation=True)
+    outputs = model_en_pt.generate(**inputs)
+    return tokenizer_en_pt.decode(outputs[0], skip_special_tokens=True)
 
 @safe_process
 def chat_response(message, history):
     if not message:
-        return "Por favor, insira uma mensagem."
+        return [], history
     response = models['chat'](message, max_length=100, do_sample=True, temperature=0.7)
-    return response[0]['generated_text']
+    history.append((message, response[0]['generated_text']))
+    return "", history
+
+# [Resto das funções permanecem iguais]
 
 with gr.Blocks(theme=gr.themes.Soft()) as demo:
     with gr.Tab("Início"):
@@ -94,35 +91,12 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
             outputs=translation_output
         )
     
-    with gr.Tab("Resumo"):
-        text_to_summarize = gr.Textbox(label="Texto para Resumir", lines=5)
-        summarize_button = gr.Button("Resumir")
-        summary_output = gr.Textbox(label="Resumo", lines=3)
-        summarize_button.click(summarize, inputs=text_to_summarize, outputs=summary_output)
-    
-    with gr.Tab("Análise de Sentimento"):
-        sentiment_text = gr.Textbox(label="Texto para Análise", lines=3)
-        sentiment_button = gr.Button("Analisar")
-        sentiment_output = gr.Textbox(label="Sentimento")
-        sentiment_button.click(analyze_sentiment, inputs=sentiment_text, outputs=sentiment_output)
-    
-    with gr.Tab("Perguntas e Respostas"):
-        question_input = gr.Textbox(label="Pergunta")
-        context_input = gr.Textbox(label="Contexto", lines=5)
-        qa_button = gr.Button("Responder")
-        qa_output = gr.Textbox(label="Resposta", lines=2)
-        qa_button.click(
-            answer_question, 
-            inputs=[question_input, context_input], 
-            outputs=qa_output
-        )
-    
     with gr.Tab("Chat"):
         chatbot = gr.Chatbot()
         msg = gr.Textbox(label="Mensagem")
         clear = gr.Button("Limpar")
         
-        msg.submit(chat_response, [msg, chatbot], [msg, chatbot])
+        msg.submit(chat_response, inputs=[msg, chatbot], outputs=[msg, chatbot])
         clear.click(lambda: None, None, chatbot, queue=False)
 
 if __name__ == "__main__":