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import gradio as gr
import pandas as pd
from groq import Groq
# Initialize the Groq client with your API key
client = Groq(api_key="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")
# Define the system message for the model
system_message = {
"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, asks questions only if the user has any concerns over your provided suggestions."
}
# Function to reset the chat
def reset_chat():
return [], "New Chat" # Returns an empty chat history and a new title
# Function to handle the questionnaire submission
def submit_questionnaire(name, age, location, gender, ethnicity, height, weight,
style_preference, color_palette, everyday_style):
# Store questionnaire responses in a DataFrame
questionnaire_data = {
"Name": name,
"Age": age,
"Location": location,
"Gender": gender,
"Ethnicity": ethnicity,
"Height": height,
"Weight": weight,
"Style Preference": style_preference,
"Color Palette": color_palette,
"Everyday Style": everyday_style
}
df = pd.DataFrame([questionnaire_data]) # Create DataFrame from dictionary
# Append to CSV file
df.to_csv("questionnaire_responses.csv", mode='a', header=not pd.io.common.file_exists("questionnaire_responses.csv"), index=False)
return "Thank you for completing the questionnaire!"
# Function to handle chat
def chat(user_input, messages):
if user_input:
# Prepare messages for the API call
messages.append({"role": "user", "content": user_input})
try:
# Generate a response from the Groq API
completion = client.chat.completions.create(
model="llama3-8b-8192",
messages=messages,
temperature=1,
max_tokens=1024,
top_p=1,
stream=False,
)
# Ensure response is valid
if completion.choices and len(completion.choices) > 0:
response_content = completion.choices[0].message.content
else:
response_content = "Sorry, I couldn't generate a response."
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") # Define an output component
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, chatbox], outputs=[chatbox, user_input])
# Run the app
demo.launch()
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