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import time
import re
import os
import requests
import json
from api_keys import meshy_api_key
from api_keys import gpt_api_key
import openai
def shouldStop(input_str, model="gpt-3.5-turbo"):
openai.api_key = gpt_api_key
prompt = f"determine whether the text delimited by triple backticks contains number 1 at the end. {input_str} \n Format: 1/0"
messages = [{"role": "user", "content": prompt}]
response = openai.ChatCompletion.create(
model=model,
messages=messages,
temperature=0, # this is the degree of randomness of the model's output
)
return response.choices[0].message["content"]
def extract_text_surrounded_by_backticks(input_string):
# Define a regular expression pattern to match text within triple backticks
pattern = r'```(.*?)```'
# Use re.DOTALL to match across multiple lines, including newline characters
extracted_text = re.findall(pattern, input_string, re.DOTALL)
# If extracted_text is not empty, return the first match; otherwise, return None
return extracted_text[0] if extracted_text else None
def text_to_3d_gen(extracted_str):
payload = {"object_prompt":"", "style_prompt":"", "enable_pbr": True, "art_style": "", "negative_prompt":"low quality, low resolution, blurry, ugly, "}
response_dict = json.loads(extracted_str)
for i in payload.keys():
if i == "enable_pbr":
continue
if i != "negative_prompt":
payload[i] = response_dict[i]
else:
payload[i] += response_dict[i]
return payload
def create_meshy_object(payload, target="3d"):
headers = {
"Authorization": f"Bearer {meshy_api_key}"
}
response = requests.post(
f"https://api.meshy.ai/v1/text-to-{target}",
headers=headers,
json=payload,
)
response.raise_for_status()
meshy_response = response.json()
text_file = open("id.txt", "w")
text_file.write(meshy_response["result"])
text_file.close()
return meshy_response["result"]
def download_model(task_id, target="3d"):
headers = {
"Authorization": f"Bearer {meshy_api_key}"
}
# print(retrieve_response["progress"])
print_once = True;
# Extract the "model_url"
while True:
response = requests.get(
f"https://api.meshy.ai/v1/text-to-{target}/{task_id}",
headers=headers,
)
response.raise_for_status()
# Convert the JSON response to a Python dictionary
retrieve_response = json.loads(response.text)
if retrieve_response["progress"] == 100:
model_url = retrieve_response["model_url"]
# Get the directory of the script
script_dir = os.path.dirname(os.path.abspath(__file__))
# Define the file name
file_name = f"{task_id}.glb"
# Define the complete file path
file_path = os.path.join(script_dir, file_name)
# Check if the file already exists
if not os.path.isfile(file_path):
# Download the file
response = requests.get(model_url)
if response.status_code == 200:
with open(file_path, "wb") as file:
file.write(response.content)
print(f"File downloaded successfully to {file_path}.")
else:
print(f"File download failed with status code {response.status_code}.")
if "error" in response.text:
error_message = json.loads(response.text)["error"]
print(f"Error message: {error_message}")
else:
print(f"File '{file_path}' already exists. Skipping download.")
break
else:
if print_once:
print("the model is still in progress...")
print_once = False
time.sleep(10)
# |