initial commit

app.py

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+import gradio as gr
+import random
+import time
+import openai
+
+import os
+
+openai.api_type = os.environ['OPENAI_API_TYPE']
+openai.api_key = os.environ['OPENAI_API_KEY']
+openai.api_base =  os.environ['OPENAI_API_BASE']
+openai.api_version = os.environ['OPENAI_API_VERSION']
+
+######################## Input TASK 1A  ########################
+from langchain.document_loaders import PyPDFLoader, OnlinePDFLoader
+from langchain.embeddings.sentence_transformer import SentenceTransformerEmbeddings
+from langchain.text_splitter import CharacterTextSplitter
+
+linkToPDF = os.environ['ONLINE_PDF_URL']
+
+loader = OnlinePDFLoader(linkToPDF)
+documents = loader.load()
+
+chuck_size = 1000
+chuck_overlap = 200
+text_splitter = CharacterTextSplitter(chunk_size=chuck_size, chunk_overlap=chuck_overlap)
+docs = text_splitter.split_documents(documents)
+###########################END OF TASK 1A ##################################
+
+######################## Input TASK 1B  ######3##############################
+from langchain.vectorstores import Chroma
+
+# create the open-source embedding function
+embedding_function = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
+
+# create simple ids - Index
+ids = [str(i) for i in range(1, len(docs) + 1)]
+
+# load it into Chroma
+db = Chroma.from_documents(docs, embedding_function, ids=ids, collection_metadata={"hnsw:space": "cosine"} )
+###########################END OF TASK 1B ##################################
+
+###########################INPUT OF TASK 3B ##################################
+# We try to limit the number of characters for the context
+contextCharsLimit = 3072
+promptStart = "Answer questions truthfully based on the information in sources provided below. \n If you cannot find the answer to a question based on the sources below, respond by saying “I apologize, but I am unable to provide an answer to your question, which is out of the scope of the document uploaded. Thank you! \n Sources:\n"
+
+# We try to construct a function which can return the system prompt based on user query and fit in context into system prompt
+def construct_system_prompt_chromadb (userQuery):
+  ### find the number of relevant documents
+  docs = db.similarity_search(userQuery)
+  context = []
+
+  ### append all relevant documents pagecontent into the context array
+  for match in docs:
+    context.append(match.page_content)
+
+  ### loop for the context, check if the chars of context > limit, if not insert the pagecontent into the prompt with "-" or "\n" separator
+  for i in range(1, len(context)):
+    if len("-".join(context[:i])) >= contextCharsLimit:
+      responsePrompt = promptStart + "-".join(context[:i-1])
+    elif i == len(context)-1:
+      responsePrompt = promptStart + "-".join(context)
+
+  ## return the response rpompt
+  return responsePrompt
+########################### END OF TASK 3B ##################################
+
+
+
+systemMessageContent = "" # System prompt we talked before - e.g., "You are a teaching assistant of a programming course CS1117. Try to answer student's question on python only"
+systemMessage = {"role": "system", "content": systemMessageContent}
+
+userMessageContent = "" # place holder
+chatbotMessageContent = ""  # place holder
+
+temperature = 0.8
+top_p = 0.95
+max_tokens = 800
+numOfHistory = 5 # Add in the number history windows here
+
+
+
+with gr.Blocks() as simpleChatDemo:
+    inputMessages = gr.State([systemMessage])
+#    inputMessages.append(systemMessage)
+
+    # Chatbot interface
+    chatbot = gr.Chatbot()
+
+    # Message is a Text Box
+    msg = gr.Textbox()
+
+    # Clear Button on to clear up the msg and chatbot
+    clear = gr.ClearButton([msg, chatbot])
+
+
+    def respond(userMessageInput, inputMessagesHistory, chatbot_history):
+
+        ## Construct the system message content based on the prompt function -- i.e., the input messages [0] will change based on system message now
+        systemMessageContent = construct_system_prompt_chromadb(userMessageInput) # Change the system content to the function
+        systemMessage = {"role": "system", "content": systemMessageContent}
+        inputMessagesHistory[0] = systemMessage
+
+
+        userMessageContent = userMessageInput
+        userMessage = {"role": "user", "content": userMessageContent}
+
+        inputMessagesHistory.append(userMessage)
+
+        if len(inputMessagesHistory)  > numOfHistory + 1:
+          numOutstandingMessages = len(inputMessagesHistory) - (numOfHistory + 1)
+          inputMessagesHistory = [inputMessagesHistory[0], *inputMessagesHistory[1+numOutstandingMessages :]]
+        print(inputMessages)
+        completion = openai.ChatCompletion.create(engine="chatgpt", messages=inputMessagesHistory, temperature=temperature, top_p = top_p, max_tokens = max_tokens)
+
+        chatbotMessageContent =  completion.choices[0].message.content
+        chatbotMessage = {"role": "assistant", "content": chatbotMessageContent }
+        inputMessagesHistory.append(chatbotMessage)
+
+        # chat history is main list of [(user message string, bot message string)]
+        chatbot_history.append((userMessageContent, chatbotMessageContent))
+
+        time.sleep(2)
+        # return with clear up the message box, and put the new messages into the chat_history
+        return "", inputMessagesHistory, chatbot_history,
+
+    # when the textbox click submit, i.e., enter, the function will be called (function, [input parameters], [output response])
+    msg.submit(respond, [msg, inputMessages, chatbot], [msg, inputMessages, chatbot])
+
+simpleChatDemo.launch(share=True)