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import tempfile, os
from pdfs.serializer import PDFUploadSerializer
from setup.environment import default_model
from drf_spectacular.utils import extend_schema
from rest_framework.decorators import api_view, parser_classes
from rest_framework.parsers import MultiPartParser
from rest_framework.response import Response
from _utils.main import get_llm_answer
@extend_schema(
request=PDFUploadSerializer,
)
@api_view(["POST"])
@parser_classes([MultiPartParser])
def getPDF(request):
if request.method == "POST":
serializer = PDFUploadSerializer(data=request.data)
if serializer.is_valid(raise_exception=True):
listaPDFs = []
print('\n\n')
data = request.data
print('data: ', data)
embedding = serializer.validated_data.get("embedding", "gpt")
model = serializer.validated_data.get("model", default_model)
# pdf_file = serializer.validated_data['file']
for file in serializer.validated_data['files']:
print("file: ", file)
file.seek(0)
# Create a temporary file to save the uploaded PDF
with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as temp_file:
# Write the uploaded file content to the temporary file
for chunk in file.chunks():
temp_file.write(chunk)
temp_file_path = temp_file.name # Get the path of the temporary file
listaPDFs.append(temp_file_path)
# print('temp_file_path: ', temp_file_path)
print('listaPDFs: ', listaPDFs)
resposta_llm = None
# resposta_llm = get_llm_answer(data["system_prompt"], data["user_message"], temp_file_path, model=model, embedding=embedding)
resposta_llm = get_llm_answer(data["system_prompt"], data["user_message"], listaPDFs, model=model, embedding=embedding)
for file in listaPDFs:
os.remove(file)
# os.remove(temp_file_path)
return Response({
"Resposta": resposta_llm
}) |