Add application file

Dockerfile

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+FROM python:3.9
+
+RUN useradd -m -u 1000 user
+USER user
+ENV PATH="/home/user/.local/bin:$PATH"
+
+WORKDIR /app
+
+COPY --chown=user ./requirements.txt requirements.txt
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+
+COPY --chown=user . /app
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]
+

README.md

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----
-title: TestAPI
-emoji: ⚡
-colorFrom: yellow
-colorTo: blue
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

config/firebase_cfg.py

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+import firebase_admin
+from firebase_admin import credentials, storage
+
+def init_firebase():
+    if not firebase_admin._apps:  
+        cred = credentials.Certificate("./cred.json")
+        firebase_admin.initialize_app(cred, {
+            'storageBucket': 'paper-trans-3e6b8.appspot.com'
+        })

main.py

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+from fastapi import FastAPI
+from routes.upload_route import upload_router 
+from config.firebase_cfg import init_firebase
+
+app = FastAPI()
+
+app.include_router(upload_router)

models/api_model.py

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+import os
+import google.generativeai as genai
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain_google_genai import GoogleGenerativeAIEmbeddings
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain_google_genai import GoogleGenerativeAIEmbeddings
+
+
+#lấy API gemini
+os.environ["GOOGLE_API_KEY"] = ''
+genai.configure(api_key=os.environ["GOOGLE_API_KEY"])
+
+#lấy model chatbot
+model = ChatGoogleGenerativeAI(model="gemini-pro",
+                             temperature=0.4)
+
+#lấy model embedding
+embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")

models/rag_chain/trans_chain.py

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+from langchain_community.vectorstores import FAISS
+from langchain.chains import RetrievalQA
+from langchain.prompts import PromptTemplate
+from models import api_model
+
+def chain():
+    #Tạo prompt cho LangChains
+    prompt = PromptTemplate(
+        template = """<|im_start|>system\nYou are a chatbot that takes care of and helps customers find information. If the customer wants to ask for information, use the following information to answer the question (If you don't know the answer, say you don't know, don't try to make up the answer). If not, respond to customers like a regular chatbot.\n
+        {context}<|im_end|>\n<|im_start|>user\n{question}<|im_end|>\n<|im_start|>assistant""",
+        input_variables = ["context", "question"],
+    )
+
+    #Embedding bằng FAISS
+    db = FAISS.load_local('datasource/db_faiss', api_model.embeddings, allow_dangerous_deserialization=True)
+
+    llm_chain = RetrievalQA.from_chain_type(
+        llm = api_model.model,
+        chain_type= "stuff",
+        retriever = db.as_retriever(search_kwargs = {"k":10}, max_tokens_limit=1024),
+        return_source_documents = False,
+        chain_type_kwargs= {'prompt': prompt}
+    )
+    return llm_chain
+
+def chat(request):
+    llm_chain = chain()
+    response = llm_chain.invoke(request)
+    return str(response['result'])
+
+
+
+
+
+

requirement.txt

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+fastapi
+uvicorn[standard]

routes/upload_route.py

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+from fastapi import FastAPI, File, UploadFile, HTTPException
+from firebase_admin import credentials, storage
+import firebase_admin
+from uuid import uuid4
+from fastapi import APIRouter
+from config.firebase_cfg import init_firebase
+
+init_firebase()
+
+upload_router = APIRouter()
+
+@upload_router.post("/upload_pdf")
+async def upload_pdf(file: UploadFile = File(...)):
+    if file.content_type != "application/pdf":
+        raise HTTPException(status_code=400, detail="must be PDF file")
+    
+    file_name = f"{uuid4()}.pdf"
+
+    bucket = storage.bucket()
+    blob = bucket.blob(file_name)
+    
+    blob.upload_from_file(file.file, content_type="application/pdf")
+    blob.make_public()  
+
+    file_url = blob.public_url
+    return {"file_url": file_url}
+
+@upload_router.post("/upload_docs")
+async def upload_docs(file: UploadFile = File(...)):
+    if file.content_type != "application/docs":
+        raise HTTPException(status_code=400, detail="must be DOCS file")
+    
+    file_name = f"{uuid4()}.docs"
+
+    bucket = storage.bucket()
+    blob = bucket.blob(file_name)
+    
+    blob.upload_from_file(file.file, content_type="application/docs")
+    blob.make_public()  
+
+    file_url = blob.public_url
+    return {"file_url": file_url}
+
+@upload_router.post("/delete_file")
+async def delete_file(file_name: str):
+    try:
+        bucket = storage.bucket()
+        blob = bucket.blob(file_name)
+
+        if not blob.exists():
+            raise HTTPException(status_code=404, detail="File not found")
+
+        blob.delete()
+
+        return {"message": f"File {file_name} deleted successfully"}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"An error occurred: {str(e)}")
+
+# url = "http://127.0.0.1:8000/delete_file"
+# data = {"file_name": "example.pdf"}
+
+# response = requests.post(url, json=data)

server.py

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+#abc