Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+import PyPDF2
+
+# Carregar o modelo de linguagem gratuito da Hugging Face
+nlp = pipeline("question-answering", model="distilbert-base-cased-distilled-squad")
+
+def extract_text_from_pdf(pdf_file):
+    with open(pdf_file.name, "rb") as file:
+        reader = PyPDF2.PdfFileReader(file)
+        text = ""
+        for page_num in range(reader.numPages):
+            page = reader.getPage(page_num)
+            text += page.extract_text()
+    return text
+
+def answer_question(pdf_file, question):
+    # Extrair texto do PDF
+    context = extract_text_from_pdf(pdf_file)
+
+    # Usar o modelo para responder a pergunta
+    result = nlp(question=question, context=context)
+    return result['answer']
+
+# Interface Gradio
+iface = gr.Interface(
+    fn=answer_question,
+    inputs=[
+        gr.inputs.File(label="Carregar PDF"),
+        gr.inputs.Textbox(label="Pergunta")
+    ],
+    outputs=gr.outputs.Textbox(label="Resposta"),
+    title="QA sobre PDF",
+    description="Carregue um PDF e faça perguntas sobre o conteúdo."
+)
+
+# Iniciar a interface
+iface.launch()