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from data_sources import process_data_upload
import gradio as gr
import json
from haystack.dataclasses import ChatMessage
from haystack.components.generators.chat import OpenAIChatGenerator
import os
from getpass import getpass
from dotenv import load_dotenv
load_dotenv()
if "OPENAI_API_KEY" not in os.environ:
os.environ["OPENAI_API_KEY"] = getpass("Enter OpenAI API key:")
chat_generator = OpenAIChatGenerator(model="gpt-4o")
response = None
messages = [
ChatMessage.from_system(
"You are a helpful and knowledgeable agent who has access to an SQL database which has a table called 'data_source'"
)
]
def chatbot_with_fc(message, history):
from functions import sqlite_query_func
from pipelines import rag_pipeline_func
import tools
import importlib
importlib.reload(tools)
available_functions = {"sql_query_func": sqlite_query_func, "rag_pipeline_func": rag_pipeline_func}
messages.append(ChatMessage.from_user(message))
response = chat_generator.run(messages=messages, generation_kwargs={"tools": tools.tools})
while True:
# if OpenAI response is a tool call
if response and response["replies"][0].meta["finish_reason"] == "tool_calls":
function_calls = response["replies"][0].tool_calls
for function_call in function_calls:
messages.append(ChatMessage.from_assistant(tool_calls=[function_call]))
## Parse function calling information
function_name = function_call.tool_name
function_args = function_call.arguments
## Find the correspoding function and call it with the given arguments
function_to_call = available_functions[function_name]
function_response = function_to_call(**function_args)
## Append function response to the messages list using `ChatMessage.from_tool`
messages.append(ChatMessage.from_tool(tool_result=function_response['reply'], origin=function_call))
response = chat_generator.run(messages=messages, generation_kwargs={"tools": tools.tools})
# Regular Conversation
else:
messages.append(response["replies"][0])
break
return response["replies"][0].text
css= ".file_marker .large{min-height:50px !important;}"
with gr.Blocks(css=css) as demo:
title = gr.HTML("<h1 style='text-align:center;'>Virtual Data Analyst</h1>")
description = gr.HTML("<p style='text-align:center;'>Upload a CSV file and chat with our virtual data analyst to get insights on your data set</p>")
file_output = gr.File(label="CSV File", show_label=True, elem_classes="file_marker", file_types=['.csv'])
@gr.render(inputs=file_output)
def data_options(filename):
print(filename)
if filename:
bot = gr.Chatbot(type='messages', label="CSV Chat Window", show_label=True, render=False, visible=True, elem_classes="chatbot")
chat = gr.ChatInterface(
fn=chatbot_with_fc,
type='messages',
chatbot=bot,
title="Chat with your data file",
examples=[
["Describe the dataset"],
["List the columns in the dataset"],
["What could this data be used for?"],
],
)
process_upload(filename)
def process_upload(upload_value):
if upload_value:
process_data_upload(upload_value)
return [], []
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