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app.py

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+# Imports
+import gradio as gr
+from helper_functions import *
+
+with gr.Blocks() as app:
+    gr.Markdown('# Trading Q&A Bot')
+    session_data = gr.State([
+        [],[]
+    ])
+    def user(user_message, history):
+        return "", history + [[user_message, None]]
+
+    def bot(history, session_data_fn):
+        messages_archived = session_data_fn[0]
+        messages_current = session_data_fn[1]
+        bot_message, messages_archived, messages_current = get_reply(history[-1][0], messages_archived, messages_current)
+        history[-1][1] = bot_message
+        session_data_fn[0] = messages_archived
+        session_data_fn[1] = messages_current
+        return history, session_data_fn
+    
+    def reset_memory(session_data_fn):
+        messages_archived = session_data_fn[0]
+        # print("Message Archived Len=", len(messages_archived))
+        if(len(messages_archived)>=21):
+            messages_archived = messages_archived[0:1] + messages_archived[3:]     
+            session_data_fn[0] = messages_archived
+        return session_data_fn
+
+    def clear_data(session_data_fn):
+        messages_archived = [
+        {"role": "system", "content": pre_text}
+        ]   
+        messages_current = []   
+        session_data_fn[0] = messages_archived
+        session_data_fn[1] = messages_current
+        return None, session_data_fn
+
+    def get_context_gr(session_data_fn):
+        messages_current = session_data_fn[1]
+        return str(messages_current)
+
+    with gr.Tab("Chat"):
+        with gr.Row():
+            with gr.Column():
+                msg = gr.Textbox()
+                with gr.Row():
+                    submit = gr.Button("Submit")
+                    clear = gr.Button("Clear")
+            with gr.Column():
+                chatbot = gr.Chatbot()
+            
+    with gr.Tab("Prompt"):
+        context = gr.Textbox()
+        submit_p = gr.Button("Check Prompt")
+    # Tab Chat
+    msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
+        bot, [chatbot, session_data], [chatbot, session_data]
+    ).then(
+        fn = reset_memory, inputs = session_data, outputs = session_data
+    )
+    submit.click(user, [msg, chatbot], [msg, chatbot], queue=False).then(
+        bot, [chatbot, session_data], [chatbot, session_data]
+    ).then(
+        fn = reset_memory, inputs = session_data, outputs = session_data
+    )
+    clear.click(
+        fn = clear_data, 
+        inputs = session_data, 
+        outputs = [chatbot, session_data],
+        queue = False
+    )
+    # Tab Prompt
+    submit_p.click(get_context_gr, session_data, context, queue=False)
+app.launch(auth=(os.getenv("id"), os.getenv("password")), show_api=False)

helper_functions.py

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+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.vectorstores import Chroma
+import os
+import openai
+import pandas as pd
+import numpy as np
+
+# Set up OpenAI API key
+openai.api_key = os.getenv("OPENAI_API_KEY")
+
+# Load data
+persist_directory = 'trading_psychology_db'
+embedding = OpenAIEmbeddings()
+vectordb = Chroma(persist_directory=persist_directory, embedding_function=embedding)
+
+pre_text_trading_psychology = """
+You are a helpful AI assistant that helps people with their trading psychology.
+You can use the provided context as knowledge source.
+Context is generated from a trading psychology podcast called 'Chat with Traders' Hosted by 'Aaron Fifield'.
+You answer the question by using the context and your own knowledge. Try to give definitive answers.
+Do not mention that the answer is based on the context.
+"""
+
+def search(query):
+    similar_docs = vectordb.similarity_search(query=query, k=10)
+    similar_texts = [doc.page_content for doc in similar_docs]
+    context = "\n\n".join(similar_texts)
+    return context
+
+def get_context(prompt):
+    # Get the context based on the prompt
+    context = search(prompt) 
+
+    # Concatenate the prompt and context
+    formatted_prompt = f"""
+    {pre_text_trading_psychology}
+
+    User Question: {prompt}
+
+    Context: ```{context}```
+
+    Your answer:
+    """
+    return formatted_prompt
+
+def get_reply(message, messages_archived, messages_current):
+    
+    if message:
+        messages_current = messages_archived.copy()
+        context = get_context(message)
+        messages_current.append(
+            {"role": "user", "content": context}
+        )
+        chat = openai.ChatCompletion.create(
+            model="gpt-3.5-turbo", messages=messages_current, temperature=0
+        )
+    
+        reply = chat.choices[0].message.content
+        messages_archived.append({"role": "user", "content": message})
+        messages_archived.append({"role": "assistant", "content": reply})
+    # If no message is provided, return a string that says "No Message Received"
+    else:
+        reply = "No Message Received"
+
+    return reply, messages_archived, messages_current
+

requirements.txt

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+openai
+pandas
+numpy
+matplotlib
+scikit-learn
+plotly
+chroma
+langchain

trading_psychology_db/chroma-collections.parquet

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trading_psychology_db/chroma-embeddings.parquet

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