Create app.py

app.py

@@ -0,0 +1,122 @@
+import streamlit as st
+from dotenv import load_dotenv
+from PyPDF2 import PdfReader
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.embeddings import OpenAIEmbeddings, HuggingFaceInstructEmbeddings
+from langchain.embeddings import HuggingFaceEmbeddings, SentenceTransformerEmbeddings
+from langchain import HuggingFaceHub
+from langchain.vectorstores import FAISS
+from langchain.memory import ConversationBufferMemory
+from langchain.chains import ConversationalRetrievalChain
+from langchain.chat_models import ChatOpenAI
+from htmlTemplates import bot_template, user_template, css
+from transformers import pipeline
+import sys
+import os
+from dotenv import load_dotenv
+
+HUGGINGFACEHUB_API_TOKEN = os.getenv("HUGGINGFACEHUB_API_TOKEN")
+repo_id=os.getenv("repo_id")
+
+OPENAI_API_KEY = os.environ.get('OPENAI_API_KEY')
+openai_api_key = os.environ.get('openai_api_key')
+embeddings = OpenAIEmbeddings(openai_api_key=OPENAI_API_KEY)
+
+#*******************************************#Pinecone Account: b***liu@gmail.com
+#pinecone_index_name=os.environ.get('pinecone_index_name')
+#pinecone_namespace=os.environ.get('pinecone_namespace')
+#pinecone_api_key=os.environ.get('pinecone_api_key')
+#pinecone_environment=os.environ.get('pinecone_environment')
+#pinecone.init(      
+#	api_key=pinecone_api_key,      
+#	environment=pinecone_environment      
+#)      
+#index = pinecone.Index(pinecone_index_name)
+#loaded_v_db_500_wt_metadata = Pinecone.from_existing_index(index_name=pinecone_index_name, embedding=embeddings, namespace=pinecone_namespace)
+#*******************************************#
+
+#*******************************************#Pinecone Account: ij***.l**@hotmail.com
+pinecone_index_name_1=os.environ.get('pinecone_index_name_1')
+#pinecone_namespace_1=os.environ.get('pinecone_namespace_1') #no namespace under this Pinecone account
+pinecone_api_key_1=os.environ.get('pinecone_api_key_1')
+pinecone_environment_1=os.environ.get('pinecone_environment_1')
+pinecone.init(      
+	api_key=pinecone_api_key_1,      
+	environment=pinecone_environment_1      
+)      
+index = pinecone.Index(pinecone_index_name_1)
+#vectorstore = Pinecone.from_existing_index(index_name=pinecone_index_name_1, embedding=embeddings)
+#*******************************************#
+
+hf_token = os.environ.get('HUGGINGFACEHUB_API_TOKEN')
+HUGGINGFACEHUB_API_TOKEN = os.environ.get('HUGGINGFACEHUB_API_TOKEN')
+huggingfacehub_api_token= os.environ.get('huggingfacehub_api_token')
+repo_id = os.environ.get('repo_id')
+
+def get_vector_store():   
+    #vectorstore = FAISS.from_texts(texts = text_chunks, embedding = embeddings)
+    vectorstore = Pinecone.from_existing_index(index_name=pinecone_index_name_1, embedding=embeddings)
+    return vectorstore
+
+def get_conversation_chain(vector_store):   
+    # OpenAI Model
+    #llm = ChatOpenAI()
+    #HuggingFace Model
+    #llm = HuggingFaceHub(repo_id="google/flan-t5-xxl")
+    #llm = HuggingFaceHub(repo_id="tiiuae/falcon-40b-instruct", model_kwargs={"temperature":0.5, "max_length":512}) #出现超时timed out错误
+    #llm = HuggingFaceHub(repo_id="meta-llama/Llama-2-70b-hf", model_kwargs={"min_length":100, "max_length":1024,"temperature":0.1})
+    #repo_id="HuggingFaceH4/starchat-beta"
+    llm = HuggingFaceHub(repo_id=repo_id,
+                         model_kwargs={"min_length":1024,
+                                       "max_new_tokens":5632, "do_sample":True,
+                                       "temperature":0.1,
+                                       "top_k":50,
+                                       "top_p":0.95, "eos_token_id":49155}) 
+    memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)
+    conversation_chain = ConversationalRetrievalChain.from_llm(
+        llm = llm,
+        retriever = vector_store.as_retriever(),
+        memory = memory
+    )
+    print("***Start of printing Conversation_Chain***")
+    print(conversation_chain)
+    print("***End of printing Conversation_Chain***")
+    st.write("***Start of printing Conversation_Chain***")
+    st.write(conversation_chain)
+    st.write("***End of printing Conversation_Chain***")    
+    return conversation_chain
+
+def handle_user_input(question):
+    response = st.session_state.conversation({'question':question})    
+    st.session_state.chat_history = response['chat_history']
+    for i, message in enumerate(st.session_state.chat_history):
+        if i % 2 == 0:
+            st.write(user_template.replace("{{MSG}}", message.content), unsafe_allow_html=True)
+        else:
+            st.write(bot_template.replace("{{MSG}}", message.content), unsafe_allow_html=True)
+            
+def main():
+    load_dotenv()
+    st.set_page_config(page_title='Chat with Your own PDFs', page_icon=':books:')
+    st.write(css, unsafe_allow_html=True)    
+    if "conversation" not in st.session_state:
+        st.session_state.conversation = None
+    if "chat_history" not in st.session_state:
+        st.session_state.chat_history = None
+    st.header('Chat with Your own PDFs :books:')
+    question = st.text_input("Ask anything to your PDF: ")
+    if question:
+        handle_user_input(question)  
+    with st.sidebar:
+        st.subheader("Upload your Documents Here: ")
+        pdf_files = st.file_uploader("Choose your PDF Files and Press OK", type=['pdf'], accept_multiple_files=True)
+        if st.button("OK"):
+            with st.spinner("Preparation under process..."):          
+                # Create Vector Store                
+                vector_store = get_vector_store()
+                st.write("DONE")
+                # Create conversation chain
+                st.session_state.conversation =  get_conversation_chain(vector_store)
+
+if __name__ == '__main__':
+    main()