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import gradio as gr
from gradio_client import Client, handle_file
import requests
from PIL import Image
import io
import fitz # PyMuPDF
import tempfile
import os
# Função para extrair texto e imagens de um PDF
def extract_from_pdf(pdf_path):
try:
# Abre o PDF
doc = fitz.open(pdf_path)
extracted_text = ""
extracted_images = []
# Itera sobre as páginas do PDF
for page_num in range(len(doc)):
page = doc.load_page(page_num)
# Extrai texto
extracted_text += page.get_text()
# Extrai imagens
image_list = page.get_images(full=True)
for img_index, img in enumerate(image_list):
xref = img[0]
base_image = doc.extract_image(xref)
image_bytes = base_image["image"]
image = Image.open(io.BytesIO(image_bytes))
extracted_images.append(image)
return extracted_text, extracted_images
except Exception as e:
return f"Erro ao processar PDF: {str(e)}", []
# Função principal para fazer a predição
def predict(file, question, seed, top_p, temperature):
try:
# Verifica se o arquivo é um PDF
if file.endswith(".pdf"):
# Extrai texto e imagens do PDF
extracted_text, extracted_images = extract_from_pdf(file)
# Se houver imagens, processa a primeira imagem
if extracted_images:
image = extracted_images[0]
with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmp_file:
image.save(tmp_file.name, format="PNG")
img_path = tmp_file.name
else:
return "Nenhuma imagem encontrada no PDF."
# Se houver texto, adiciona ao prompt
if extracted_text:
question = f"Texto extraído do PDF:\n{extracted_text}\n\nPergunta: {question}"
else:
# Se não for PDF, trata como imagem
if file.startswith('http'):
response = requests.get(file)
img_path = handle_file(io.BytesIO(response.content))
else:
img_path = handle_file(file)
# Inicializa o cliente do Gradio
client = Client("deepseek-ai/Janus-Pro-7B")
# Faz a predição
result = client.predict(
image=img_path,
question=question,
seed=seed,
top_p=top_p,
temperature=temperature,
api_name="/multimodal_understanding"
)
return result
except Exception as e:
return f"Erro durante a predição: {str(e)}"
# Componentes da interface
file_input = gr.File(label="Upload PDF or Image", file_types=[".pdf", ".png", ".jpg", ".jpeg"])
question_input = gr.Textbox(label="Question", placeholder="Ask something about the file...")
seed_slider = gr.Slider(0, 100, value=42, label="Seed")
top_p_slider = gr.Slider(0, 1, value=0.95, label="Top-p")
temp_slider = gr.Slider(0, 1, value=0.1, label="Temperature")
# Cria a interface
demo = gr.Interface(
fn=predict,
inputs=[
file_input,
question_input,
seed_slider,
top_p_slider,
temp_slider
],
outputs=gr.Textbox(label="Answer"),
title="Janus-Pro-7B Multimodal Demo",
description="Ask questions about PDFs or images using the Janus-Pro-7B model",
examples=[
["https://raw.githubusercontent.com/gradio-app/gradio/main/test/test_files/bus.png", "What's in this image?", 42, 0.95, 0.1]
]
)
if __name__ == "__main__":
demo.launch() |