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import gradio as gr
import pandas as pd
from app import run, describe_file,suggest_questions
import os
from PIL import Image
import logging
import sys
from dotenv import load_dotenv
load_dotenv()
if not os.environ.get('method'):
if len(sys.argv) > 1 and sys.argv[1] in ["server","api","local"] :
os.environ['method'] = sys.argv[1]
else:
print("Please type a valid model method")
sys.exit()
logging.basicConfig(level=logging.INFO)
logging.info(os.environ.get('method'))
def read_image():
directory='./'
image_files = [f for f in os.listdir(directory) if os.path.isfile(os.path.join(directory, f)) and (f.endswith('.png') or f.endswith('.jpg'))]
if image_files:
image_path = os.path.join(directory, image_files[0])
try:
image = Image.open(image_path)
return image
except Exception as e:
print(f"Error {e}")
return None
return None
def delete_image():
directory='./'
image_files = [f for f in os.listdir(directory) if os.path.isfile(os.path.join(directory, f)) and (f.endswith('.png') or f.endswith('.jpg'))]
if image_files:
for image_file in image_files:
image_path = os.path.join(directory, image_file)
try:
os.remove(image_path)
except:
return
def generate_description(uploaded_file):
delete_image()
if uploaded_file is None:
return "", "Please upload a CSV file"
df = pd.read_csv(uploaded_file)
automatic_description = describe_file(df, uploaded_file.name)
suggestions = suggest_questions(df, uploaded_file.name)
return automatic_description, "",suggestions
def process_csv_question_and_description(uploaded_file, description, question):
delete_image()
if uploaded_file is None:
return "Please upload a CSV file."
df = pd.read_csv(uploaded_file)
df_columns = str(list(df.columns))
namespace = {'df': df}
logging.info("Generating started")
response = run(namespace, description, df_columns, question,os.environ.get("method"))
logging.info("Generating finished")
logging.info(response)
image = read_image()
# logging.basicConfig(level=logging.INFO)
# logging.info("This is an info message")
# logging.info(response)
try:
execution = response['execution']
except:
try:
execution = response['error']
except:
execution = 'An error occured while generating a response'
return execution,image
with gr.Blocks(css=".file_container {max-height:150px} .file_container > button > div {display:flex;flex-direction:row}") as app:
gr.Markdown("## CSV Question Answering App")
gr.Markdown("Upload a CSV file, and an automatic description will be generated. You can edit this description before asking your question.")
with gr.Row():
with gr.Column():
file_input = gr.File(label="Upload CSV File",elem_classes=['file_container'])
description_input = gr.Textbox(label="Dataset Description", placeholder="The description will be generated here...")
question_input = gr.Textbox(label="For better visualization results start your input with \"Plot\"")
submit_button = gr.Button("Submit")
suggestions=gr.Text(label="Suggestions")
with gr.Column():
output = gr.Text(label="Answer")
image = gr.Image()
message = gr.Textbox(label="Message", visible=False)
file_input.change(fn=generate_description, inputs=[file_input], outputs=[description_input, message,suggestions])
submit_button.click(fn=process_csv_question_and_description, inputs=[file_input, description_input, question_input], outputs=[output,image])
app.launch(debug=True)
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