File size: 10,891 Bytes
e34a93e a095bcc e34a93e 9ef7776 7c0f8d5 9ef7776 a095bcc e34a93e 9ef7776 e34a93e 9ef7776 e34a93e 9ef7776 e34a93e e62fecd e34a93e a095bcc |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 |
import openai
import gradio as gr
from os import getenv
from typing import Any, Dict, Generator, List
from huggingface_hub import InferenceClient
from transformers import AutoTokenizer
import google.generativeai as genai
import os
import PIL.Image
import gradio as gr
#from gradio_multimodalchatbot import MultimodalChatbot
from gradio.data_classes import FileData
#tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1")
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mixtral-8x7B-Instruct-v0.1")
# temperature = 0.2
# #top_p = 0.6
# repetition_penalty = 1.0
temperature = 0.5
top_p = 0.7
repetition_penalty = 1.2
# Fetch an environment variable.
GOOGLE_API_KEY = os.environ.get('GOOGLE_API_KEY')
genai.configure(api_key=GOOGLE_API_KEY)
OPENAI_KEY = getenv("OPENAI_API_KEY")
HF_TOKEN = getenv("HUGGING_FACE_HUB_TOKEN")
# hf_client = InferenceClient(
# "mistralai/Mistral-7B-Instruct-v0.1",
# token=HF_TOKEN
# )
hf_client = InferenceClient(
"mistralai/Mixtral-8x7B-Instruct-v0.1",
token=HF_TOKEN
)
def format_prompt(message: str, api_kind: str):
"""
Formats the given message using a chat template.
Args:
message (str): The user message to be formatted.
Returns:
str: Formatted message after applying the chat template.
"""
# Create a list of message dictionaries with role and content
messages: List[Dict[str, Any]] = [{'role': 'user', 'content': message}]
if api_kind == "openai":
return messages
elif api_kind == "hf":
return tokenizer.apply_chat_template(messages, tokenize=False)
elif api_kind:
raise ValueError("API is not supported")
def generate_hf(prompt: str, history: str, temperature: float = 0.9, max_new_tokens: int = 4000,
top_p: float = 0.95, repetition_penalty: float = 1.0) -> Generator[str, None, str]:
"""
Generate a sequence of tokens based on a given prompt and history using Mistral client.
Args:
prompt (str): The initial prompt for the text generation.
history (str): Context or history for the text generation.
temperature (float, optional): The softmax temperature for sampling. Defaults to 0.9.
max_new_tokens (int, optional): Maximum number of tokens to be generated. Defaults to 256.
top_p (float, optional): Nucleus sampling probability. Defaults to 0.95.
repetition_penalty (float, optional): Penalty for repeated tokens. Defaults to 1.0.
Returns:
Generator[str, None, str]: A generator yielding chunks of generated text.
Returns a final string if an error occurs.
"""
temperature = max(float(temperature), 1e-2) # Ensure temperature isn't too low
top_p = float(top_p)
generate_kwargs = {
'temperature': temperature,
'max_new_tokens': max_new_tokens,
'top_p': top_p,
'repetition_penalty': repetition_penalty,
'do_sample': True,
'seed': 42,
}
formatted_prompt = format_prompt(prompt, "hf")
try:
stream = hf_client.text_generation(formatted_prompt, **generate_kwargs,
stream=True, details=True, return_full_text=False)
output = ""
for response in stream:
output += response.token.text
yield output
except Exception as e:
if "Too Many Requests" in str(e):
print("ERROR: Too many requests on Mistral client")
gr.Warning("Unfortunately Mistral is unable to process")
return "Unfortunately, I am not able to process your request now."
elif "Authorization header is invalid" in str(e):
print("Authetification error:", str(e))
gr.Warning("Authentication error: HF token was either not provided or incorrect")
return "Authentication error"
else:
print("Unhandled Exception:", str(e))
gr.Warning("Unfortunately Mistral is unable to process")
return "I do not know what happened, but I couldn't understand you."
def generate_openai(prompt: str, history: str, temperature: float = 0.9, max_new_tokens: int = 256,
top_p: float = 0.95, repetition_penalty: float = 1.0) -> Generator[str, None, str]:
"""
Generate a sequence of tokens based on a given prompt and history using Mistral client.
Args:
prompt (str): The initial prompt for the text generation.
history (str): Context or history for the text generation.
temperature (float, optional): The softmax temperature for sampling. Defaults to 0.9.
max_new_tokens (int, optional): Maximum number of tokens to be generated. Defaults to 256.
top_p (float, optional): Nucleus sampling probability. Defaults to 0.95.
repetition_penalty (float, optional): Penalty for repeated tokens. Defaults to 1.0.
Returns:
Generator[str, None, str]: A generator yielding chunks of generated text.
Returns a final string if an error occurs.
"""
temperature = max(float(temperature), 1e-2) # Ensure temperature isn't too low
top_p = float(top_p)
generate_kwargs = {
'temperature': temperature,
'max_tokens': max_new_tokens,
'top_p': top_p,
'frequency_penalty': max(-2., min(repetition_penalty, 2.)),
}
formatted_prompt = format_prompt(prompt, "hf")
try:
stream = openai.ChatCompletion.create(model="gpt-3.5-turbo-0301",
messages=formatted_prompt,
**generate_kwargs,
stream=True)
output = ""
for chunk in stream:
output += chunk.choices[0].delta.get("content", "")
yield output
except Exception as e:
if "Too Many Requests" in str(e):
print("ERROR: Too many requests on OpenAI client")
gr.Warning("Unfortunately OpenAI is unable to process")
return "Unfortunately, I am not able to process your request now."
elif "You didn't provide an API key" in str(e):
print("Authetification error:", str(e))
gr.Warning("Authentication error: OpenAI key was either not provided or incorrect")
return "Authentication error"
else:
print("Unhandled Exception:", str(e))
gr.Warning("Unfortunately OpenAI is unable to process")
return "I do not know what happened, but I couldn't understand you."
def generate_gemini(prompt: str, history: str, temperature: float = 0.9, max_new_tokens: int = 4000,
top_p: float = 0.95, repetition_penalty: float = 1.0):
# For better security practices, retrieve sensitive information like API keys from environment variables.
# Initialize genai models
model = genai.GenerativeModel('gemini-pro')
api_key = os.environ.get("GOOGEL_API_KEY")
genai.configure(api_key=api_key)
#model = genai.GenerativeModel('gemini-pro')
#chat = model.start_chat(history=[])
candidate_count=1
max_output_tokens=max_new_tokens
temperature=temperature
top_p=top_p
formatted_prompt = format_prompt(prompt, "gemini")
try:
stream = model.generate_content(formatted_prompt,generation_config=genai.GenerationConfig(temperature=temperature,candidate_count=1 ,max_output_tokens=max_new_tokens,top_p=top_p),
stream=True)
output = ""
for response in stream:
output += response.text
yield output
except Exception as e:
if "Too Many Requests" in str(e):
print("ERROR: Too many requests on Mistral client")
gr.Warning("Unfortunately Mistral is unable to process")
return "Unfortunately, I am not able to process your request now."
elif "Authorization header is invalid" in str(e):
print("Authetification error:", str(e))
gr.Warning("Authentication error: HF token was either not provided or incorrect")
return "Authentication error"
else:
print("Unhandled Exception:", str(e))
gr.Warning("Unfortunately Mistral is unable to process")
return "I do not know what happened, but I couldn't understand you."
# def gemini(input, file, chatbot=[]):
# """
# Function to handle gemini model and gemini vision model interactions.
# Parameters:
# input (str): The input text.
# file (File): An optional file object for image processing.
# chatbot (list): A list to keep track of chatbot interactions.
# Returns:
# tuple: Updated chatbot interaction list, an empty string, and None.
# """
# messages = []
# print(chatbot)
# # Process previous chatbot messages if present
# if len(chatbot) != 0:
# for messages_dict in chatbot:
# user_text = messages_dict[0]['text']
# bot_text = messages_dict[1]['text']
# messages.extend([
# {'role': 'user', 'parts': [user_text]},
# {'role': 'model', 'parts': [bot_text]}
# ])
# messages.append({'role': 'user', 'parts': [input]})
# else:
# messages.append({'role': 'user', 'parts': [input]})
# try:
# response = model.generate_content(messages)
# gemini_resp = response.text
# # Construct list of messages in the required format
# user_msg = {"text": input, "files": []}
# bot_msg = {"text": gemini_resp, "files": []}
# chatbot.append([user_msg, bot_msg])
# except Exception as e:
# # Handling exceptions and raising error to the modal
# print(f"An error occurred: {e}")
# raise gr.Error(e)
# return chatbot, "", None
# # Define the Gradio Blocks interface
# with gr.Blocks() as demo:
# # Add a centered header using HTML
# gr.HTML("<center><h1>Gemini Chat PRO API</h1></center>")
# # Initialize the MultimodalChatbot component
# multi = MultimodalChatbot(value=[], height=800)
# with gr.Row():
# # Textbox for user input with increased scale for better visibility
# tb = gr.Textbox(scale=4, placeholder='Input text and press Enter')
# # Define the behavior on text submission
# tb.submit(gemini, [tb, multi], [multi, tb])
# # Launch the demo with a queue to handle multiple users
# demo.queue().launch() |