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import gradio as gr
import os
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from langchain_community.chat_models import ChatAnyscale
from Hypothesis_Test_Agent.Hypothesis_Agent import infer_hypothesis
from EDA_Agent.EDA import infer_EDA
from Basic_Inf_Agent.Basic_Inference_Agent import infer_basic
print(os.getcwd())
def run_agent(data_path:str = '', provider:str = 'Mistral', agent_type:str = 'EDA', query:str = '', layers:str ='', temp:float = 0.1):
df = pd.read_csv(data_path)
df.to_csv('./df.csv', index=False)
if provider.lower() == 'mistral':
llm = ChatAnyscale(model_name='mistralai/Mixtral-8x7B-Instruct-v0.1', temperature=temp)
if agent_type == 'Data Explorer':
EDA_response, EDA_image = infer_EDA(user_input=query, llm=llm, df=df)
return EDA_response, EDA_image
if agent_type == 'Hypothesis Tester':
hypothesis_response = infer_hypothesis(user_input=query, llm=llm, df=df)
return hypothesis_response, None
if agent_type == 'Basic Inference':
basic_response = infer_basic(user_input=query, df=df, llm=llm)
return basic_response, None
return None
demo = gr.Interface (
run_agent,
[
gr.UploadButton(label="Upload your CSV!"),
gr.Radio(["Mistral","GPT"],label="Select Your LLM"),
gr.Radio(["Data Explorer", "Hypothesis Tester", "Basic Inference", "Super Inference"], label="Choose Your Agent"),
gr.Text(label="Query", info="Your input to the Agent. Be descriptive!"),
gr.Text(label="Architecture", info="Specify the layer by layer architecture only for Super Inference Agent"),
gr.Slider(label="Model Temperature", info="Slide right to make your model more creative!")
],
[
gr.Text(label="Agent Output", info="This might take a while to generate since agent debugs its errors too ..."),
gr.Image(label="Graph")
]
)
demo.launch(share=True)
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