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Runtime error
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import gradio as gr
import requests
import time
import requests
import base64
import time
token = '5UAYO8UWHNQKT3UUS9H8V360L76MD72DRIUY9QC2'
##############################################################
#################################################
def SD_call(image_prompt, age, color, hair_color,NSFW):
positive = "clothes"
negative = "naked, nsfw, porn"
serverless_api_id = '3g77weiulabzuk'
# Define the URL you want to send the request to
url = f"https://api.runpod.ai/v2/{serverless_api_id}/runsync"
# Define your custom headers
headers = {
"Authorization": f"Bearer {token}",
"Accept": "application/json",
"Content-Type": "application/json"
}
# Define your data (this could also be a JSON payload)
print("SD_processing")
if NSFW == True:
positive = "naked, nsfw"
negative = "clothes"
data = {
"input": {
"api": {
"method": "POST",
"endpoint": "/sdapi/v1/txt2img"
},
"payload": {
"override_settings": {
"sd_model_checkpoint": "CyberRealistic",
"sd_vae": ""
},
"override_settings_restore_afterwards": True,
"refiner_checkpoint": "",
"refiner_switch_at": 0.8,
"prompt": f"masterpiece, best quality, 8k, (looking at viewer:1.1), gorgeous, hot, seductive, {age} years old american {color} woman, (eye contact:1.1), beautiful face, hyper detailed, best quality, ultra high res, {hair_color} hair,blue eyes, photorealistic, high resolution, detailed, raw photo, 1girl,{image_prompt}, {positive} ",
"negative_prompt": f"EasyNegative, fat, paintings, sketches, (worst quality:2), (low quality:2), (normal quality:2), lowres, ((monochrome)), ((grayscale)), bad anatomy, text, error, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, bad feet, poorly drawn face, bad proportions, gross proportions, ng_deepnegative_v1_75t, badhandsv5-neg, {negative}",
"seed": -1,
"batch_size": 1,
"steps": 30,
"cfg_scale": 7,
"width": 520,
"height": 520,
"sampler_name": "DPM++ SDE Karras",
"sampler_index": "DPM++ SDE Karras",
"restore_faces": False
}
}
}
# Send the POST request with headers and data
response = requests.post(url, headers=headers, json=data)
# Check the response
if response.status_code == 200:
response_data = response.json()
msg_id = response_data['id']
print("Message ID:", msg_id)
# Poll the status until it's not 'IN_QUEUE'
while response_data['status'] == 'IN_QUEUE':
time.sleep(5) # Wait for 5 seconds before checking again
response = requests.get(f"{url}/{msg_id}", headers=headers)
try:
response_data = response.json()
except Exception as e:
print("Error decoding JSON:", e)
print("Response content:", response.text)
break # Exit the loop on JSON decoding error
# Check if the response contains images
if 'images' in response_data.get('output', {}):
base64_image = response_data['output']['images'][0]
image_bytes = base64.b64decode(base64_image)
# Save the image to a file
image_path = f"output_image_{msg_id}.png"
with open(image_path, "wb") as img_file:
img_file.write(image_bytes)
print(f"Image downloaded successfully: {image_path}")
return image_path
else:
return "No images found in the response."
else:
# Print error message
return f"Error: {response.status_code} - {response.text}"
##############################################################
#################################################
def LLM_call(message_log, temperature):
serverless_api_id = '4whzcbwuriohqh'
# Define the URL you want to send the request to
url = f"https://api.runpod.ai/v2/{serverless_api_id}/run"
# Define your custom headers
headers = {
"Authorization": f"Bearer {token}",
"Accept": "application/json",
"Content-Type": "application/json"
}
# Define your data (this could also be a JSON payload)
data = {
"input": {
"prompt": message_log,
"max_new_tokens": 4500,
"temperature": 0.7,
"top_k": 50,
"top_p": 0.9,
"repetition_penalty": 1.2,
"batch_size": 8,
"stop": ["</s>"]
}
}
# Send the POST request with headers and data
call = requests.post(url, headers=headers, json=data)
response_data = call.json()
msg_id = response_data['id']
print("Message ID:", msg_id)
output = "Output not available"
# Poll the API until the response is ready
while True:
# Get the status using the message ID
response = requests.get(f"https://api.runpod.ai/v2/{serverless_api_id}/status/{msg_id}", headers=headers)
if response.status_code == 200:
response_data = response.json()
status = response_data.get('status')
if status == 'COMPLETED':
# Access the 'output' directly from the response
output = response_data.get('output', 'Output not available')
print("Response content:", output)
break # Exit the loop once the response is ready
elif status == 'FAILED':
error_message = response_data.get('error', 'Unknown error')
print("Request failed. Reason:", error_message)
break # Exit the loop if the request failed
else:
print("Failed to get status. HTTP status code:", response.status_code)
# Wait for a short time before polling again (e.g., 2 seconds)
time.sleep(2)
return output
def Chat_call(chat,temperature):
message_log = [{"role": "assistant", "content": f"I want you to act as a woman named Julie. Use colloquial language. Julie is 25 years old, she is very hot physically, she loves watching football, she loves to talk about sex. Julie loves sex and being fucked. She never refuses anything. You'll answer to a man, call him baby. Write very short answers, 15 words."},]
new_message = {"role": "user", "content": chat}
message_log.append(new_message)
response = LLM_call(message_log, temperature)
print("From Runpod:" + response)
return response
########################################################
#######################################################
def greet(chat, image_prompt, temperature, age, color, hair_color,NSFW):
if image_prompt.strip():
image_path = SD_call(image_prompt,age, color, hair_color,NSFW)
return "Image generated successfully", image_path
if temperature > 3:
return "You are too warm please try again", None
else:
text_answer = Chat_call(chat,temperature)
return text_answer, None
demo = gr.Interface(
fn=greet,
inputs=[
"text",
gr.Textbox(label="Image", lines=3),
gr.Slider(label="Text temperature", value=1, minimum=0, maximum=2),
gr.Slider(label="Age", value=22, minimum=18, maximum=75),
gr.Dropdown(["asian", "white", "black", "latina"], label="Color", info="Will add more later!"),
gr.Dropdown(["blond", "brune", "red", "white", "pink", "black", "blue", "green"], label="Hair color", info="Blond is cool"),
gr.Checkbox(label="NSFW", info="👀👀👀")
],
flagging_options=["blurry", "incorrect", "other"],
outputs=[gr.Textbox(label="Answer", lines=3), gr.Image(label="Generated Image", type="filepath")],
)
demo.launch(share=True)
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