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. Start the conversation with a greeting, provide valuable fashion advice, and only ask questions if the user has concerns. Gather basic inputs: name, age, gender, location, height, and weight."
}

# Function to reset the chat
def reset_chat():
    return [], "New Chat"

# Function to handle the questionnaire submission
def submit_questionnaire(name, age, location, gender, height, weight):
    # Store basic responses in a DataFrame
    questionnaire_data = {
        "Name": name,
        "Age": age,
        "Location": location,
        "Gender": gender,
        "Height": height,
        "Weight": weight
    }

    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, name, age, location, gender, height, weight):
    if user_input:
        # Create a user profile string
        user_profile_string = (
            f"User profile: Name: {name}, Age: {age}, Location: {location}, "
            f"Gender: {gender}, Height: {height}, Weight: {weight}"
        )

        # Prepare messages for the API call, including the profile and the conversation history
        messages.append({"role": "user", "content": user_input})
        messages.append(system_message)
        messages.append({"role": "user", "content": user_profile_string})

        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"])
            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")

    # Chat functionality
    chatbox = gr.Chatbot()
    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, height, weight], outputs=output_message)
    
    reset_btn.click(reset_chat, outputs=[chatbox, "title"])
    user_input.submit(chat, inputs=[user_input, chatbox, name, age, location, gender, height, weight], outputs=[chatbox, user_input])

# Run the app
demo.launch()