Update app.py

app.py

@@ -1,5 +1,5 @@
 import gradio as gr
-from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqGeneration
+from transformers import pipeline, AutoTokenizer, AutoModel
 import torch
 import warnings
 warnings.filterwarnings('ignore')
@@ -11,6 +11,9 @@ try:
         'transcription': pipeline("automatic-speech-recognition", 
                                 model="openai/whisper-small", 
                                 device=device),
+        'translation': pipeline("translation", 
+                              model="facebook/mbart-large-50-many-to-many-mmt",
+                              device=device),
         'summarization': pipeline("summarization", 
                                 model="facebook/bart-large-cnn", 
                                 device=device),
@@ -25,10 +28,6 @@ try:
                         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)}")
 
@@ -40,20 +39,41 @@ 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."
-        
-    input_text = text
+    
     if direction == "pt_en":
-        input_text = f"translate Portuguese to English: {text}"
+        result = models['translation'](text, src_lang="pt", tgt_lang="en")[0]
     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)
+        result = models['translation'](text, src_lang="en", tgt_lang="pt")[0]
+    
+    return result['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']
 
 @safe_process
 def chat_response(message, history):
@@ -63,8 +83,6 @@ def chat_response(message, history):
     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"):
         gr.HTML(open("index.html").read())
@@ -91,6 +109,29 @@ 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")