Create app.py

app.py

@@ -0,0 +1,129 @@
+# app.py
+import gradio as gr
+from transformers import pipeline
+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)
+    }
+except Exception as e:
+    print(f"Erro ao carregar modelos: {str(e)}")
+
+def safe_process(func):
+    def wrapper(*args, **kwargs):
+        try:
+            return func(*args, **kwargs)
+        except Exception as e:
+            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']
+
+@safe_process
+def chat_response(message, history):
+    if not message:
+        return "Por favor, insira uma mensagem."
+    response = models['chat'](message, max_length=100, do_sample=True, temperature=0.7)
+    return response[0]['generated_text']
+
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    with gr.Tab("Início"):
+        gr.HTML(open("index.html").read())
+    
+    with gr.Tab("Transcrição de Áudio"):
+        audio_input = gr.Audio(type="filepath", label="Upload de Áudio")
+        transcribe_button = gr.Button("Transcrever")
+        transcription_output = gr.Textbox(label="Transcrição", lines=3)
+        transcribe_button.click(transcribe, inputs=audio_input, outputs=transcription_output)
+    
+    with gr.Tab("Tradução"):
+        with gr.Row():
+            translation_direction = gr.Radio(
+                ["en_pt", "pt_en"], 
+                label="Direção da Tradução",
+                value="en_pt"
+            )
+        text_to_translate = gr.Textbox(label="Texto para Traduzir", lines=3)
+        translate_button = gr.Button("Traduzir")
+        translation_output = gr.Textbox(label="Tradução", lines=3)
+        translate_button.click(
+            translate, 
+            inputs=[text_to_translate, translation_direction], 
+            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])
+        clear.click(lambda: None, None, chatbot, queue=False)
+
+if __name__ == "__main__":
+    demo.launch(share=True)