app.py

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"

# 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"])

    # Additional fashion-related questions
    fashion_questions = [
        ("What do you prioritize when choosing an outfit?", ["Comfort", "Style", "Affordability", "Brand"]),
        ("How often do you experiment with new trends?", ["Always", "Sometimes", "Rarely", "Never"]),
        ("What kind of accessories do you usually wear?", ["Watches", "Rings", "Necklaces", "Bracelets", "Earrings"]),
        ("Do you follow fashion trends?", ["Always", "Sometimes", "Never"]),
        ("How satisfied are you with your wardrobe?", ["Yes", "No"]),
        ("Do you consider your style unique?", ["Yes", "No"]),
        ("How confident do you feel in your style?", ["Very Confident", "Somewhat Confident", "Not Confident"]),
        ("Where do you look for fashion inspiration?", ["Social Media", "Fashion Magazines", "Friends", "Other"]),
        ("Do you have specific attire for special occasions?", ["Yes", "No"]),
        ("Do you wear gender-neutral clothing?", ["Yes", "No"])
    ]

    for question, choices in fashion_questions:
        gr.Radio(label=question, choices=choices)

    # 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, "title"])
    user_input.submit(chat, inputs=[user_input, chatbox], outputs=[chatbox, user_input])

# Run the app
demo.launch()