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("## FRIDAY") # 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 Inputs") 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?", ["Very Satisfied", "Satisfied", "Neutral", "Unsatisfied"]), ("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"]), ("Which clothing materials do you prefer?", ["Cotton", "Silk", "Denim", "Synthetic", "Wool"]), ("How important is sustainability in your fashion choices?", ["Very Important", "Somewhat Important", "Not Important"]), ("Do you prefer shopping online or in physical stores?", ["Online", "Physical Stores"]), ("How often do you update your wardrobe?", ["Seasonally", "Every Few Months", "Once a Year", "Rarely"]), ("Do you participate in clothing swaps or second-hand shopping?", ["Yes", "No"]), ] # Create a button for filling the questionnaire fill_questionnaire_btn = gr.Button("Fill Questionnaire") # Output message for questionnaire submission questionnaire_output = gr.Textbox(label="Questionnaire Submission", interactive=False) # Function to collect all questionnaire responses def collect_questionnaire_responses(): # Store questionnaire responses in a DataFrame questionnaire_data = { "Style Preference": style_preference.value, "Color Palette": color_palette.value, "Everyday Style": everyday_style.value } # Append additional responses for question, choices in fashion_questions: questionnaire_data[question] = gr.Radio(label=question, choices=choices).value 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!" # Connect the buttons to their respective functions submit_btn.click(submit_questionnaire, inputs=[name, age, location, gender, ethnicity, height, weight, style_preference, color_palette, everyday_style], outputs=questionnaire_output) fill_questionnaire_btn.click(collect_questionnaire_responses, outputs=questionnaire_output) reset_btn.click(reset_chat, outputs=[gr.Chatbot(type='messages'), "title"]) user_input = gr.Textbox(label="Your Message", placeholder="Type your message here...") chatbox = gr.Chatbot(type='messages') user_input.submit(chat, inputs=[user_input, chatbox], outputs=[chatbox, user_input]) # Run the app demo.launch()