Create app.py

app.py

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+import os
+import time
+import openai
+import gradio as gr
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.vectorstores import Chroma
+from langchain.chat_models import ChatOpenAI
+from langchain.schema import AIMessage, HumanMessage, SystemMessage
+
+# Sets up OpenAI embeddings model
+embeddings = OpenAIEmbeddings()
+
+# Loads database from persisted directory
+db_directory = "chroma_db"
+db = Chroma(persist_directory=db_directory, embedding_function=embeddings)
+
+# Retrieves relevant documents based on a similarity search 
+retriever = db.as_retriever(search_type='similarity', search_kwargs={"k":3})
+
+with open('system_prompt.txt', 'r') as file:
+    ORIG_SYSTEM_MESSAGE_PROMPT = file.read()
+
+openai.api_key = os.getenv("OPENAI_API_KEY")
+
+chat = ChatOpenAI(model_name="gpt-4",temperature=0)
+
+# Here is the langchain
+def predict(history, input):
+    context = retriever.get_relevant_documents(input)
+    print(context) #For debugging
+    history_langchain_format = []
+    history_langchain_format.append(SystemMessage(content=f"{ORIG_SYSTEM_MESSAGE_PROMPT}"))
+    for human, ai in history:
+        history_langchain_format.append(HumanMessage(content=human))
+        history_langchain_format.append(AIMessage(content=ai))
+    history_langchain_format.append(HumanMessage(content=input))
+    history_langchain_format.append(SystemMessage(content=f"Here are some stories the user may like: {context}"))
+    
+    gpt_response = chat(history_langchain_format)
+    
+    # Extract pairs of HumanMessage and AIMessage
+    pairs = []
+    for i in range(len(history_langchain_format)):
+        if isinstance(history_langchain_format[i], HumanMessage) and (i+1 < len(history_langchain_format)) and isinstance(history_langchain_format[i+1], AIMessage):
+            pairs.append((history_langchain_format[i].content, history_langchain_format[i+1].content))
+    
+    # Add new AI response to the pairs for subsequent interactions
+    pairs.append((input, gpt_response.content))
+    
+    return pairs
+
+# Function to handle user message
+def user(user_message, chatbot_history):
+    return "", chatbot_history + [[user_message, ""]]
+
+# Function to handle AI's response
+def bot(chatbot_history):
+    user_message = chatbot_history[-1][0] #This line is because we cleared the user_message previously in the user function above
+    # Call the predict function to get the AI's response
+    pairs = predict(chatbot_history, user_message)
+    _, ai_response = pairs[-1]  # Get the latest response
+
+    response_in_progress = ""
+    for character in ai_response:
+        response_in_progress += character
+        chatbot_history[-1][1] = response_in_progress
+        time.sleep(0.05)
+        yield chatbot_history
+
+# This is a function to do something with the voted information
+def vote(data: gr.LikeData):
+    if data.liked:
+        print("You upvoted this response: " + data.value)
+    else:
+        print("You downvoted this response: " + data.value)
+        with open("logs.txt", "a") as text_file:
+          print(f"Disliked content: {data.value}", file=text_file)
+
+# The Gradio App interface
+with gr.Blocks() as demo:
+    gr.Markdown("""<h1><center>Technocomplex Bot</center></h1>""")
+    chatbot = gr.Chatbot(label="Technocomplex Bot")
+    textbox = gr.Textbox(label="Start chatting here!")
+    clear = gr.Button("Clear")
+    
+    # Chain user and bot functions with `.then()`
+    textbox.submit(user, [textbox, chatbot], [textbox, chatbot], queue=False).then(
+        bot, chatbot, chatbot,
+    )
+    clear.click(lambda: None, None, chatbot, queue=False)
+    chatbot.like(vote, None, None)
+
+# Enable queuing
+demo.queue()
+demo.launch(debug=True, share=True)