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import re
from dotenv import load_dotenv
import requests
import json
import os
import logging
import io
import sys
from api_client import predict_deepseek,predict_gpt
load_dotenv()
if os.environ.get("method") =="local":
from model import generate_response
from prompt_templates import prompt_template_textual,prompt_template_visual,description_template,suggestion_template,libraries
def describe_file(df,filename):
first_row = df.iloc[0].to_dict()
prompt = description_template.format(filename=filename,example_row=first_row)
response = predict_gpt(prompt)
return response
def suggest_questions(df,filename):
example_row = dict(df.iloc[0])
prompt = suggestion_template.format(filename=filename,example_row=example_row)
response = predict_gpt(prompt)
return response
def extract_code(text):
try:
matches = []
pattern = r"```python(.*?)```"
if text:
matches = re.findall(pattern, text, re.DOTALL)
if matches:
return matches[0]
else:
raise Exception("Error extracting code: No match")
except Exception as e:
raise Exception("Error extracting code: ",e) from e
def execute(code,namespace):
try:
buffer = io.StringIO()
sys.stdout = buffer
exec(libraries+code,namespace)
sys.stdout = sys.__stdout__
return buffer.getvalue()
except Exception as e:
raise Exception("Error executing: ",e) from e
def run(namespace,description,columns,question,method):
try:
if question.lower().startswith('plot'):
prompt = prompt_template_visual.format(description=description,columns=columns,question=question)
else:
prompt = prompt_template_textual.format(description=description,columns=columns,question=question)
full_response= None
extracted_code= None
execution= None
error = None
try:
if method == 'server':
request = {
'url' : os.environ.get("MODEL_URL"),
'payload' : json.dumps({"prompt": prompt}),
'headers' : {
'Content-Type': 'application/json'
}}
full_response = requests.request("POST", request['url'], headers=request['headers'], data=request['payload']).json()["response"]
elif method == 'local':
full_response = generate_response(prompt)
elif method == 'api':
full_response = predict_deepseek(prompt)
else:
return {'execution': 'Wrong model method'}
extracted_code = extract_code(full_response)
execution = execute(extracted_code,namespace)
except Exception as e:
error = e
data = {
'question': question,
'prompt':prompt,
'full_response': full_response,
'extracted_code': extracted_code,
'execution': execution,
'error': error
}
logging.info(data)
with open("log.json", 'w') as file:
json.dump(data, file, indent=4)
return data
except Exception as e:
print(e)
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