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import gradio as gr
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Load the Zephyr-7B model
model_name = "HuggingFaceH4/zephyr-7b-beta"
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.bfloat16,
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 using chat template
chat_input = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
# Generate a response using the model
try:
model_inputs = tokenizer(chat_input, return_tensors="pt").to(model.device)
generated_ids = model.generate(
**model_inputs,
max_new_tokens=256,
do_sample=True,
temperature=0.7,
top_k=50,
top_p=0.95
)
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0].strip()
except Exception as e:
response = f"Error: {str(e)}"
# Store assistant response in the chat history
messages.append({"role": "assistant", "content": response})
return messages, response
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")
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]) # Reset chat
user_input.submit(chat, inputs=user_input, outputs=[chatbox, user_input]) # Connect chat input
# Run the app
demo.launch()
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