Added Prompt Template to the LLM

app.py

@@ -0,0 +1,155 @@
+import shutil
+
+import streamlit as st
+import os
+from dotenv import load_dotenv
+import datetime
+from langchain.document_loaders import PyPDFLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.vectorstores import Chroma
+from langchain.chat_models import ChatOpenAI
+from langchain.chains import  ConversationalRetrievalChain
+from langchain.prompts import PromptTemplate
+from langchain.chains import LLMChain
+# from langchain.vectorstores.azuresearch import AzureSearch
+from langchain.retrievers import BM25Retriever
+load_dotenv()
+
+print("Streamlit run has started")
+# Title
+st.title("Ask PDF")
+
+def create_folder(folder_path):
+    if not os.path.exists(folder_path):
+        os.makedirs(folder_path)
+
+def delete_files_in_folder(folder_path):
+    files = os.listdir(folder_path)
+    for file in files:
+        file_path = os.path.join(folder_path, file)
+        try:
+            if os.path.isfile(file_path):
+                os.unlink(file_path)
+        except Exception as e:
+            st.error(f"Error deleting file {file_path}: {e}")
+
+def delete_folder(folder_path):
+    try:
+        os.rmdir(folder_path)
+    except Exception as e:
+        st.error(f"Error deleting folder {folder_path}: {e}")
+
+
+TARGET_DIR = "./uploaded_data"
+
+# Uploading Files
+uploaded_files = st.file_uploader("Upload your files", type=['pdf'], accept_multiple_files=True)
+
+@st.cache_resource
+def load_uploaded_files(uploaded_files: list,target_dir:str):
+    # delete_files_in_folder("./docs")
+    # delete_folder("./docs")
+    # shutil.rmtree("./docs")
+    # try :
+    #     os.remove("./docs/chroma/chroma.sqlite3")
+    # except :
+    #     print("No such directory")
+    if len(uploaded_files) > 0:
+        # creates new folder for the uploaded data
+        create_folder(target_dir)
+        print("Created the directory for the uploaded files")
+
+        for uploaded_file in uploaded_files:
+            file_path = os.path.join(target_dir, uploaded_file.name)
+            with open(file_path, "wb") as f:
+                f.write(uploaded_file.read())
+        print("Written all the files successfully")
+        st.write("Successfully Uploaded the files")
+
+        # Loading pdfs
+        docs = []
+        for file  in os.listdir(target_dir):
+            loader = PyPDFLoader(target_dir + '/' + file)
+            docs.extend(loader.load())
+        print(f"length of the docs {len(docs)}")
+
+
+        text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=100,separators=["\n\n","\n"])
+        splits = text_splitter.split_documents(docs)
+
+        print(f"length of the splits {len(splits)}")
+        embedding = OpenAIEmbeddings()
+        print("loaded the Open AI Embeddings Function")
+
+
+        # Creating Vector Database and persisting it
+        persist_directory = './docs/chroma/'
+        vectordb = Chroma.from_documents(
+            documents=splits,
+            embedding=embedding,
+            persist_directory=persist_directory
+        )
+        # Creating a retriever
+        retriever = vectordb.as_retriever()
+        print("Created Vector DB ")
+
+        # db = AzureSearch(azure_search_endpoint="https://tvsmazdscssbx01-hackathronai.search.windows.net","")
+
+        # retriever = BM25Retriever.from_documents(splits)
+
+        retriever = vectordb.as_retriever()
+        print("Created Vector DB ")
+
+        # Selecting a Model
+        current_date = datetime.datetime.now().date()
+        if current_date < datetime.date(2023, 9, 2):
+            llm_name = "gpt-3.5-turbo-0301"
+        else:
+            llm_name = "gpt-3.5-turbo"
+        print(llm_name)
+        print("chose the llm")
+
+        delete_files_in_folder(target_dir)
+        delete_folder(target_dir)
+        print("Deleted the files and directory")
+
+
+        return llm_name, retriever
+
+
+if len(uploaded_files) > 0 :
+    llm_name,retriever = load_uploaded_files(uploaded_files,target_dir=TARGET_DIR)
+
+    template = """Combine the chat history and follow up question into 
+        a standalone question. Chat History: {chat_history}
+        "Follow up question: {question} . 
+         If you dont know the answer for the given question then reply politely that you dont know the answer. 
+         """
+    prompt_template = PromptTemplate(input_variables=["chat_history",'question'],template=template)
+    llm = ChatOpenAI(model_name=llm_name, temperature=0)
+    chat_history = []
+    qa_chain = ConversationalRetrievalChain.from_llm(llm=llm, retriever=retriever,
+                                                     chain_type="stuff",condense_question_prompt=prompt_template)
+
+
+print("started Chatbot")
+st.title("QA Bot")
+if "messages" not in st.session_state.keys():
+    st.session_state.messages = [{"role": "Assistant", "content": "How can i help you"}]
+
+for msg in st.session_state.messages:
+    st.chat_message(msg["role"]).write(msg["content"])
+
+question = st.chat_input("Ask a Question")
+#
+if question is not None:
+    st.session_state.messages.append({"role": "user", "content": question})
+    st.chat_message("user").write(question)
+    result = qa_chain({"question": question, "chat_history": chat_history})['answer']
+    chat_history = [(question, result)]
+    st.session_state.messages.append({"role": 'Assistant', "content": result})
+    st.chat_message("Assistant").write(result)
+
+
+