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import gradio as gr
import openai
import os
import json
# OpenAI API setup
openai.api_key = os.getenv("GROQ_API_KEY")
openai.api_base = "https://api.groq.com/openai/v1"
# File to store conversation history
CONVERSATION_FILE = "conversation_history.json"
# Function to load conversation history
def load_history():
if not os.path.exists(CONVERSATION_FILE):
# Create the file with an empty list as default content
with open(CONVERSATION_FILE, "w") as file:
json.dump([], file)
try:
with open(CONVERSATION_FILE, "r") as file:
return json.load(file)
except json.JSONDecodeError:
return []
# Function to save conversation history
def save_history(history):
try:
with open(CONVERSATION_FILE, "w") as file:
json.dump(history, file, indent=4)
except Exception as e:
print(f"Error saving history: {e}")
# Function to clear conversation history
def clear_conversation_history():
try:
with open(CONVERSATION_FILE, "w") as file:
json.dump([], file)
return "Conversation history cleared successfully.", ""
except Exception as e:
return f"Error clearing history: {e}", ""
# Function to format bot response
def format_bot_response(response):
"""
Converts markdown-like symbols to HTML and structures response.
"""
response = response.replace("**", "<b>").replace("**", "</b>") # Bold formatting
response = response.replace("1.", "<br> 1.")
response = response.replace("2.", "<br> 2.")
response = response.replace("3.", "<br> 3.")
response = response.replace("4.", "<br> 4.")
response = response.replace("5.", "<br> 5.")
return f"<div>{response}</div>"
# Function to get response from the LLM
def get_groq_response(message, history=[]):
try:
messages = [{"role": "system", "content": "Precise answer"}] + history + [{"role": "user", "content": message}]
response = openai.ChatCompletion.create(
model="llama-3.1-70b-versatile",
messages=messages
)
return format_bot_response(response.choices[0].message["content"])
except Exception as e:
return f"Error: {str(e)}"
# Chatbot function
def chatbot(user_input, history):
# Load conversation history
conversation_history = history or load_history()
# Format history for the LLM
formatted_history = [{"role": "user" if i % 2 == 0 else "assistant", "content": msg} for i, (msg, _) in enumerate(conversation_history)] + \
[{"role": "assistant", "content": response} for _, response in conversation_history]
# Get bot response
bot_response = get_groq_response(user_input, formatted_history)
# Update history with the new conversation
conversation_history.append((user_input, bot_response))
# Save the updated history
save_history(conversation_history)
# Format for HTML display
display_html = "".join(
f"<div class='user-message'><b>User:</b> {user}</div>"
f"<div class='bot-message'><b>Bot:</b> {bot}</div>"
for user, bot in conversation_history
)
return conversation_history, display_html, "" # Clear the user input field
# Gradio Interface
with gr.Blocks(css="""
.user-message {
background-color: #9ACBD0;
padding: 10px;
margin: 10px;
border-radius: 8px;
max-width: 60%;
float: right;
clear: both;
}
.bot-message {
background-color: #F2EFE7;
padding: 10px;
margin: 10px;
border-radius: 8px;
max-width: 60%;
float: left;
clear: both;
}
.user-message:hover, .bot-message:hover {
transform: scale(1.02);
box-shadow: 0px 4px 10px rgba(0, 0, 0, 0.1);
}
""") as demo:
gr.Markdown("""# Mom: We have ChatGPT at Home, \n ChatGPT at Home: """)
chat_display = gr.HTML(label="Conversation")
user_input = gr.Textbox(label="Type your message here: Feel free to ask questions. After you're done, remember to clear the history for privacy. ")
clear_button = gr.Button("Clear History")
system_message = gr.Textbox(label="System Message", interactive=False)
history_state = gr.State(load_history())
# Chat interaction
user_input.submit(chatbot, inputs=[user_input, history_state], outputs=[history_state, chat_display, user_input])
# Clear history button action
clear_button.click(clear_conversation_history, inputs=None, outputs=[system_message, chat_display])
clear_button.click(lambda: [], outputs=history_state) # Reset the history state
# Launch the app
demo.launch()
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