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import sys
import os
import random
from time import sleep
from gradio_client import Client
current_dir = os.path.dirname(os.path.abspath(__file__))
sys.path.append(os.path.dirname(current_dir))
from datasets import load_dataset
def send_message(client, message):
_apps = ["fintechgpt", "healthgpt", "mydoc", "knowledge-centre", "assistantgpt"]
# Compute exponential weights for service names
factor = 2 # Control the steepness of the exponential decrease
app_weights = [factor ** -i for i in range(len(_apps))]
# Select a service_name randomly based on the exponential weights
service_name = random.choices(_apps, weights=app_weights, k=1)[0]
# Define providers and corresponding models with weights
providers_models = {
"Meta AI": (["LLaMA 65B", "LLaMA 33B"], 10),
"Mistral": (["Mistral 7B"], 8),
"Anthropic": (["Claude 3.5 Sonnet"], 15),
"OpenAI": (["GPT-3", "GPT-3.5", "GPT-4", "GPT-4o"], 25),
"Google": (["Gemini Ultra", "Gemini Pro", "Gemini Nano", "Lamda", "Palm"], 20),
"Databricks": (["Dolly"], 5),
"IBM AI": (["Watson NLP"], 5),
"Azure AI": (["Azure OpenAI", "Custom GPT-3.5"], 7),
"Snowflake": (["Snowflake GPT"], 3),
"Krutrim": (["Krutrim LLM"], 2),
"Baidu": (["Ernie 4.0"], 10),
"Stability AI": (["StableLM 3B", "StableLM 7B"], 4),
"Microsoft": (["Orca", "Phi-1"], 8)
}
# Extract providers and their weights
providers = list(providers_models.keys())
provider_weights = [providers_models[provider][1] for provider in providers]
# Randomly select a provider based on weights
selected_provider = random.choices(providers, weights=provider_weights, k=1)[0]
# Randomly select a model from the selected provider
selected_model = random.choice(providers_models[selected_provider][0])
result = client.predict(
message, # str in 'Message' Textbox component
api_name="/chat"
)
print(f"Sending message: {message} \n\t Result {result}")
def main():
prompt_injection_dataset = load_dataset("deepset/prompt-injections")
toxicity_prompts = load_dataset("allenai/real-toxicity-prompts")
nats_url = os.environ.get('CHAT_APP_URL', "http://localhost:7860/")
client = Client(nats_url)
while True:
# Collect messages
prompt_injection_sample = prompt_injection_dataset["train"].shuffle(seed=42).select(range(10))
toxicity_sample = toxicity_prompts["train"].shuffle(seed=42).select(range(10))
plain_messages = ["this is test conversation" for _ in range(10)]
# Combine all messages into a single list
all_messages = [msg["text"] for msg in prompt_injection_sample] + \
[msg["prompt"]["text"] for msg in toxicity_sample] + \
plain_messages
# Shuffle the combined list to mix message types
random.shuffle(all_messages)
# Send each message
for message in all_messages:
send_message(client, message)
sleep(random.uniform(0.5, 2)) # Random sleep between 0.5 to 2 seconds
if __name__ == "__main__":
main()
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