File size: 4,449 Bytes
31b863d 9002a71 31b863d 50238a6 9002a71 |
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 |
import gradio as gr
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Load the Qwen2.5-72B-Instruct model
model_name = "Qwen/Qwen2.5-72B-Instruct"
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Initial messages list for chat history
messages = [
{"role": "system", "content": "You are an experienced Fashion designer who starts conversation with proper greeting, "
"giving valuable and catchy fashion advice and suggestions, stays to the point and precise."}
]
# Function to reset the chat
def reset_chat():
global messages
messages = [] # Reset the message history
return [], "New Chat"
# Function to handle questionnaire submission
def submit_questionnaire(name, age, location, gender, ethnicity, height, weight,
style_preference, color_palette, everyday_style):
# Store questionnaire responses as needed
# Placeholder logic for storing responses
return "Thank you for completing the questionnaire!"
# Function to handle chat
def chat(user_input):
global messages
if user_input:
# Append user message to the conversation history
messages.append({"role": "user", "content": user_input})
# Prepare input for the model
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response using the model
try:
generated_ids = model.generate(
**model_inputs,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response_content = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0].strip()
except Exception as e:
response_content = f"Error: {str(e)}"
# Store assistant response in the chat history
messages.append({"role": "assistant", "content": response_content})
return messages, response_content
return messages, ""
# Gradio Interface
with gr.Blocks() as demo:
gr.Markdown("## Fashion Assistant Chatbot")
# Sidebar for user inputs
with gr.Row():
with gr.Column():
name = gr.Textbox(label="Name")
age = gr.Number(label="Age", value=25, minimum=1, maximum=100)
location = gr.Textbox(label="Location")
gender = gr.Radio(label="Gender", choices=["Male", "Female", "Other"])
ethnicity = gr.Radio(label="Ethnicity", choices=["Asian", "Black", "Hispanic", "White", "Other"])
height = gr.Number(label="Height (cm)", value=170, minimum=50, maximum=250)
weight = gr.Number(label="Weight (kg)", value=70, minimum=20, maximum=200)
with gr.Column():
submit_btn = gr.Button("Submit Questionnaire")
reset_btn = gr.Button("Reset Chat")
# Questionnaire with fashion-related questions
style_preference = gr.Radio(label="Which style do you prefer the most?", choices=["Casual", "Formal", "Streetwear", "Athleisure", "Baggy"])
color_palette = gr.Radio(label="What color palette do you wear often?", choices=["Neutrals", "Bright Colors", "Pastels", "Dark Shades"])
everyday_style = gr.Radio(label="How would you describe your everyday style?", choices=["Relaxed", "Trendy", "Elegant", "Bold"])
# Chat functionality
chatbox = gr.Chatbot(type='messages')
user_input = gr.Textbox(label="Your Message", placeholder="Type your message here...")
# Connect the buttons to their respective functions
output_message = gr.Textbox(label="Output Message", interactive=False)
submit_btn.click(submit_questionnaire, inputs=[name, age, location, gender, ethnicity, height, weight,
style_preference, color_palette, everyday_style], outputs=output_message)
reset_btn.click(reset_chat, outputs=[chatbox, output_message]) # Corrected outputs
user_input.submit(chat, inputs=user_input, outputs=[chatbox, user_input]) # Fixed to include chatbox output
# Run the app
demo.launch()
|