Spaces:
Runtime error
Runtime error
File size: 6,751 Bytes
6823dec 1d92a29 6823dec |
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 |
import json
import psutil
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
import gradio as gr
import os
import tarfile
from typing import List, Tuple
import boto3
import logging
# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
class CustomerSupportBot:
def __init__(self, model_path="models/customer_support_gpt"):
"""
Initialize the customer support bot with the fine-tuned model.
Args:
model_path (str): Path to the saved model and tokenizer
"""
self.process = psutil.Process(os.getpid())
self.model_path = model_path
self.model_file_path = os.path.join(self.model_path, "model.tar.gz")
self.s3 = boto3.client("s3")
self.model_key = "models/model.tar.gz"
self.bucket_name = "customer-support-gpt"
# Download and load the model
self.download_and_load_model()
def download_and_load_model(self):
# Check if the model directory exists
if not os.path.exists(self.model_path):
os.makedirs(self.model_path)
# Download model.tar.gz from S3 if not already downloaded
if not os.path.exists(self.model_file_path):
print("Downloading model from S3...")
self.s3.download_file(self.bucket_name, self.model_key, self.model_file_path)
print("Download complete. Extracting model files...")
# Extract the model files
with tarfile.open(self.model_file_path, "r:gz") as tar:
tar.extractall(self.model_path)
# Load the model and tokenizer from extracted files
self.tokenizer = AutoTokenizer.from_pretrained(self.model_path)
self.model = AutoModelForCausalLM.from_pretrained(self.model_path)
print("Model and tokenizer loaded successfully.")
# Move model to GPU if available
self.device = "cpu" #"cuda" if torch.cuda.is_available() else "cpu"
self.model = self.model.to(self.device)
print(f'Model loaded on device: {self.device}')
def generate_response(self, message: str, max_length=100, temperature=0.7) -> str:
try:
input_text = f"Instruction: {message}\nResponse:"
# Tokenize input text
inputs = self.tokenizer(input_text, return_tensors="pt").to(self.device)
# Generate response using the model
with torch.no_grad():
outputs = self.model.generate(
**inputs,
max_length=max_length,
temperature=temperature,
num_return_sequences=1,
pad_token_id=self.tokenizer.pad_token_id,
eos_token_id=self.tokenizer.eos_token_id,
do_sample=True,
top_p=0.95,
top_k=50
)
# Decode and format the response
response = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
response = response.split("Response:")[-1].strip()
return response
except Exception as e:
return f"An error occurred: {str(e)}"
def monitor_resources(self) -> dict:
usage = {
"CPU (%)": self.process.cpu_percent(interval=1),
"RAM (GB)": self.process.memory_info().rss / (1024 ** 3)
}
return usage
def create_chat_interface():
bot = CustomerSupportBot(model_path="/app/models")
def predict(message: str, history: List[Tuple[str, str]]) -> Tuple[str, List[Tuple[str, str]]]:
if not message:
return "", history
bot_response = bot.generate_response(message)
# Log resource usage
usage = bot.monitor_resources()
print("Resource Usage:", usage)
history.append((message, bot_response))
return "", history
# Create the Gradio interface with custom CSS
with gr.Blocks(css="""
.message-box {
margin-bottom: 10px;
}
.button-row {
display: flex;
gap: 10px;
margin-top: 10px;
}
""") as interface:
gr.Markdown("# Customer Support Chatbot")
gr.Markdown("Welcome! How can I assist you today?")
chatbot = gr.Chatbot(
label="Chat History",
height=500,
elem_classes="message-box",
# type="messages"
)
with gr.Row():
msg = gr.Textbox(
label="Your Message",
placeholder="Type your message here...",
lines=2,
elem_classes="message-box"
)
with gr.Row(elem_classes="button-row"):
submit = gr.Button("Send Message", variant="primary")
clear = gr.ClearButton([msg, chatbot], value="Clear Chat")
# Add example queries in a separate row
with gr.Row():
gr.Examples(
examples=[
"How do I reset my password?",
"What are your shipping policies?",
"I want to return a product.",
"How can I track my order?",
"What payment methods do you accept?"
],
inputs=msg,
label="Example Questions"
)
# Set up event handlers
submit_click = submit.click(
predict,
inputs=[msg, chatbot],
outputs=[msg, chatbot]
)
msg.submit(
predict,
inputs=[msg, chatbot],
outputs=[msg, chatbot]
)
# Add keyboard shortcut for submit
msg.change(lambda x: gr.update(interactive=bool(x.strip())), inputs=[msg], outputs=[submit])
print("Interface created successfully.")
# call the initial query function
# run a query first how are you and predict the output
print(predict("How are you", []))
# run a command which checks the resource usage
print(f'Bot Resource Usage : {bot.monitor_resources()}')
# show full system usage
print(f'CPU Percentage : {psutil.cpu_percent()}')
print(f'RAM Usage : {psutil.virtual_memory()}')
print(f'Swap Memory : {psutil.swap_memory()}')
return interface
if __name__ == "__main__":
demo = create_chat_interface()
print("Starting Gradio server...")
demo.launch(
share=False,
server_name="0.0.0.0",
server_port=7860, # Changed to 7860 for Gradio
debug=True,
inline=False
)
|