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| import tempfile, os, requests | |
| from rest_framework import viewsets, filters | |
| from django_filters.rest_framework import DjangoFilterBackend | |
| from endpoint_teste.models import EndpointTesteModel | |
| from endpoint_teste.serializer import EndpointTesteSerializer, PDFUploadSerializer | |
| from ..environment import default_model | |
| from rest_framework.decorators import api_view | |
| from rest_framework.response import Response | |
| from langchain_backend.main import get_llm_answer | |
| from .serializer import TesteSerializer | |
| from langchain_huggingface import HuggingFaceEndpoint | |
| class EndpointTesteViewSet(viewsets.ModelViewSet): | |
| """Mostrará todas as tarefas""" | |
| queryset = EndpointTesteModel.objects.order_by("id").all() | |
| serializer_class = EndpointTesteSerializer | |
| filter_backends = [DjangoFilterBackend, filters.SearchFilter] | |
| search_fields = ["id"] | |
| def getTeste(request): | |
| if request.method == "POST": | |
| serializer = TesteSerializer(data=request.data) | |
| if serializer.is_valid(raise_exception=True): | |
| data = request.data | |
| pdf_url = None | |
| if data["pdf_url"]: | |
| pdf_url = data["pdf_url"] | |
| resposta_llm = get_llm_answer(data["system_prompt"], data["user_message"], pdf_url) | |
| return Response({ | |
| "Resposta": resposta_llm | |
| }) | |
| if request.method == "GET": | |
| # hugging_face_token = os.environ.get("hugging_face_token") | |
| # API_URL = "https://api-inference.huggingface.co/models/meta-llama/Meta-Llama-3-8B" | |
| # headers = {"Authorization": "Bearer " + hugging_face_token} | |
| # def query(payload): | |
| # response = requests.post(API_URL, headers=headers, json=payload) | |
| # return response.json() | |
| # output = query({ | |
| # "inputs": "Can you please let us know more details about your something I don't know", | |
| # }) | |
| # print('output: ', output) | |
| # print('output: ', dir(output)) | |
| llm = HuggingFaceEndpoint( | |
| repo_id="meta-llama/Meta-Llama-3-8B-Instruct", | |
| task="text-generation", | |
| max_new_tokens=100, | |
| do_sample=False, | |
| huggingfacehub_api_token = os.environ.get("HUGGINGFACEHUB_API_TOKEN") | |
| ) | |
| result = llm.invoke("Hugging Face is") | |
| print('result: ', result) | |
| return Response(result) | |
| def getPDF(request): | |
| if request.method == "POST": | |
| serializer = PDFUploadSerializer(data=request.data) | |
| if serializer.is_valid(raise_exception=True): | |
| # Access the uploaded file | |
| data = request.data | |
| print('data: ', data) | |
| pdf_file = serializer.validated_data['file'] | |
| pdf_file.seek(0) | |
| # print(dir(pdf_file)) | |
| # print('pdf_file: ', pdf_file.read()) | |
| # pdf_content = pdf_file.read() | |
| # Save the file or process it as needed | |
| # For example, you can save it to a specific location | |
| # with open(f'endpoint_teste/media/uploads/{pdf_file.name}', 'wb+') as destination: | |
| # for chunk in pdf_file.chunks(): | |
| # destination.write(chunk) | |
| # return Response({"message": "File uploaded successfully."}) | |
| # 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 pdf_file.chunks(): | |
| temp_file.write(chunk) | |
| temp_file_path = temp_file.name # Get the path of the temporary file | |
| print('temp_file_path: ', temp_file_path) | |
| resposta_llm = None | |
| try: | |
| resposta_llm = get_llm_answer(data["system_prompt"], data["user_message"], temp_file_path, model=serializer.validated_data['model']) | |
| except: | |
| resposta_llm = get_llm_answer(data["system_prompt"], data["user_message"], temp_file_path, model=default_model) | |
| os.remove(temp_file_path) | |
| return Response({ | |
| "Resposta": resposta_llm | |
| }) |