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Dockerfile.txt

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+# read the doc: https://huggingface.co/docs/hub/spaces-sdks-docker
+# you will also find guides on how best to write your Dockerfile
+
+FROM python:3.9
+
+WORKDIR /code
+
+COPY ./requirements.txt /code/requirements.txt
+
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+#Added from here
+RUN useradd -m -u 1000 user
+USER user
+ENV HOME=/home/user \
+	PATH=/home/user/.local/bin:$PATH
+
+WORKDIR $HOME/app
+
+COPY --chown=user . $HOME/app
+#COPY . .
+
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]

main.py

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+from fastapi import FastAPI
+from app import predict
+import os
+from huggingface_hub import login
+from pydantic import BaseModel
+import sys
+from langchain.chat_models import ChatOpenAI
+from langchain.prompts import PromptTemplate
+from langchain.memory import ConversationBufferMemory
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.runnables import RunnablePassthrough
+import os
+import PyPDF2 as pdf
+import gradio as gr
+from langchain_community.document_loaders import PyPDFLoader
+import os
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain_community.embeddings.sentence_transformer import (
+    SentenceTransformerEmbeddings,
+)
+from langchain_chroma import Chroma
+from sentence_transformers import SentenceTransformer
+from langchain_core.messages import AIMessage, HumanMessage
+from fastapi import FastAPI, Request, UploadFile, File
+
+os.environ['HF_HOME'] = '/hug/cache/'
+os.environ['TRANSFORMERS_CACHE'] = '/blabla/cache/'
+
+app = FastAPI()
+app.recursion_limit = 10**4
+
+def predict(message, db):
+
+    llm = ChatOpenAI(model_name="gpt-4o-mini", temperature=0)
+    template = """You are a general purpose chatbot. Be friendly and kind. Help people answer their questions. Use the context below to answer the questions
+    {context}
+    Question: {question}
+    Helpful Answer:"""
+    QA_CHAIN_PROMPT = PromptTemplate(input_variables=["context", "question"],template=template,)
+    memory = ConversationBufferMemory(
+        memory_key="chat_history",
+        return_messages=True
+    )
+        
+    retriever = db.as_retriever(k=3)
+
+    contextualize_q_system_prompt = """Given a chat history and the latest user question \
+    which might reference context in the chat history, formulate a standalone question \
+    which can be understood without the chat history. Do NOT answer the question, \
+    just reformulate it if needed and otherwise return it as is."""
+    contextualize_q_prompt = ChatPromptTemplate.from_messages(
+        [
+            ("system", contextualize_q_system_prompt),
+            MessagesPlaceholder(variable_name="chat_history"),
+            ("human", "{question}"),
+        ]
+    )
+    contextualize_q_chain = contextualize_q_prompt | llm | StrOutputParser()
+    def contextualized_question(input: dict):
+      if input.get("chat_history"):
+          return contextualize_q_chain
+      else:
+          return input["question"]
+
+    rag_chain = (
+        RunnablePassthrough.assign(
+            context=contextualized_question | retriever
+        )
+        | QA_CHAIN_PROMPT
+        | llm
+      )
+    history = []
+    ai_msg = rag_chain.invoke({"question": message, "chat_history": history})
+    print(ai_msg)
+    bot_response = ai_msg.content.strip()
+
+    # Ensure history is correctly formatted as a list of tuples (user_message, bot_response)
+    history.append((HumanMessage(content=message), AIMessage(content=bot_response)))
+
+    docs = db.similarity_search(message,k=3)
+    extra = "\n" + "*"*100 + "\n"
+    additional_info = []
+    for d in docs:
+        citations = d.metadata["source"] + " pg." + str(d.metadata["page"])
+        additional_info = d.page_content
+        extra += citations + "\n" + additional_info + "\n" + "*"*100 + "\n"
+    # Return the bot's response and the updated history
+    return bot_response + extra
+
+def upload_file(file_path):
+
+    loaders = []
+    print(file_path)
+    loaders.append(PyPDFLoader(file_path))
+
+    documents = []
+    for loader in loaders:
+        documents.extend(loader.load())
+
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=512, chunk_overlap=16)
+    docs = text_splitter.split_documents(documents)
+
+    model = "thenlper/gte-large"
+    embedding_function = SentenceTransformerEmbeddings(model_name=model)
+    print(f"Model's maximum sequence length: {SentenceTransformer(model).max_seq_length}")
+    collection_name = "Autism"
+    persist_directory = "./chroma"
+    print(len(docs))
+    db = Chroma.from_documents(docs, embedding_function)
+    print("Done Processing, you can query")
+
+    return db
+
+
+class Item(BaseModel):
+    code: str
+
+@app.get("/")
+async def root():
+ return {"Code Review Automation":"Version 1.0 'First Draft'"}
+
+@app.post("/UploadFile/")
+def predict(question: str, file: UploadFile = File(...)):
+    contents =  file.file.read()
+    with open(file.filename, 'wb') as f:
+        f.write(contents)
+    
+    db = upload_file(file.filename)
+    result = predict(question, db)
+    return {"answer":result}
+

requirements.txt

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+PyPDF2
+langchain 
+langchain_community 
+langchain_chroma
+chromadb
+openai
+pypdf
+requests
+sentence-transformers
+fastapi
+pydantic
+uvicorn
+openpyxl