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Update app.py
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import os
import json
import gradio as gr
from utils.response_manager import ResponseManager # Import the ResponseManager class
"""
This script sets up a Gradio interface to host an AI chatbot using RAG (Retrieval-Augmented Generation)
to provide responses to user queries. Response API from OpenAI is used for both retrieval and generation of responses.
"""
# Vector store ID for the retrieval of knowledge base documents
# Load the vector store ID from the environment variable
vector_store_id = os.getenv('VECTOR_STORE_ID')
# Check if the VECTOR_STORE_ID environment variable is set
if not vector_store_id:
raise ValueError("VECTOR_STORE_ID environment variable is not set.")
# Initialize the ResponseManager with the vector store ID
response_manager = ResponseManager(vector_store_id)
# Define the chatbot function to handle user queries and generate responses
def chat_interaction(query: str) -> str:
"""
Function to handle the chatbot interaction.
:param query: The user query to respond to.
:return: The response text from the chatbot.
"""
try:
if query.strip():
response = response_manager.create_response(query, model, temperature, max_output_tokens, max_num_results)
if not response:
return "Sorry, I couldn't generate a response at this time. Please try again later."
# Return the response from the AI model
return response
else:
return "Please enter a valid query."
except Exception as e:
return str(e)
# Set parameters for the response generation
model = "gpt-4o-mini" # Set the model to be used for response generation
temperature=0 # Set the temperature for response generation
max_output_tokens=800 # Set the maximum number of output tokens
max_num_results=7 # Set the maximum number of knowledge base documents to return for retrieval
# Load the configuration for Gradio GUI interface from the JSON file
with open('config/gradio_config.json', 'r') as config_file:
config = json.load(config_file)
# Check if the configuration file is loaded successfully
if not config:
raise ValueError("Failed to load the configuration file.")
# Extract the configuration parameters
chatbot_title = config["chatbot_title"]
chatbot_description = config["chatbot_description"]
chatbot_input_label = config["chatbot_input_label"]
chatbot_input_placeholder = config["chatbot_input_placeholder"]
chatbot_output_label = config["chatbot_output_label"]
chatbot_output_placeholder = config["chatbot_output_placeholder"]
chatbot_submit_button = config["chatbot_submit_button"]
chatbot_reset_button = config["chatbot_reset_button"]
# Check if the configuration parameters are set correctly
if not all([chatbot_title, chatbot_description,
chatbot_input_label, chatbot_input_placeholder,
chatbot_output_label, chatbot_output_placeholder,
chatbot_submit_button, chatbot_reset_button]):
raise ValueError("One or more configuration parameters are missing or empty.")
# Define the reset function
def reset_output():
return chatbot_output_placeholder
# Create a Gradio Blocks interface
with gr.Blocks() as demo:
gr.Markdown(f"## {chatbot_title}\n{chatbot_description}")
with gr.Row():
user_input = gr.Textbox(
lines=7,
label=chatbot_input_label,
placeholder=chatbot_input_placeholder
)
with gr.Row():
# Output box always visible with initial placeholder
output = gr.Textbox(
lines=7,
label=chatbot_output_label,
placeholder=chatbot_output_placeholder,
interactive=False
)
# Place Reset on the Left and Submit on the Right
with gr.Row():
# Left Column for Reset
with gr.Column(scale=1):
reset = gr.Button(chatbot_reset_button, variant="secondary")
# Right Column for Submit
with gr.Column(scale=1):
submit = gr.Button(chatbot_submit_button, variant="primary")
# Define button click actions
submit.click(fn=chat_interaction, inputs=user_input, outputs=output)
reset.click(fn=reset_output, inputs=None, outputs=output)
# Enable "Enter" key to submit
user_input.submit(fn=chat_interaction, inputs=user_input, outputs=output)
if __name__ == "__main__":
demo.launch()