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import os
import gradio as gr
from groq import Groq
# Groq API Configuration
keyment = os.getenv ("Mental")
client = Groq(api_key = keyment)
# Function to handle responses from Groq
def chatbot(message, history, system_message, max_tokens, temperature, top_p):
messages = [{"role": "system", "content": system_message}]
# Add conversation history
for val in history:
if val[0]:
messages.append({"role": "user", "content": val[0]})
if val[1]:
messages.append({"role": "assistant", "content": val[1]})
# Append current user message
messages.append({"role": "user", "content": message})
# Call the Groq API to generate the response
response = ""
for chunk in client.chat.completions.create(
model="llama-3.3-70b-versatile",
messages=messages,
temperature=temperature,
max_tokens=max_tokens,
top_p=top_p,
stream=True,
):
response += chunk.choices[0].delta.content or ""
yield response
# System message to inform the AI about its purpose
system_message = "You are Jude Mental Health AI Chat. You are an experienced expert in mental health, psychology, and psychiatry. You have a wide range of knowledge, skills, and tools for dealing with mental health issues, and you are also a good educator on mental health issues. Provide concise, informed answers to various prompts on mental health"
# Define the Gradio interface
def create_interface():
with gr.Blocks() as demo:
# Display the main header
gr.Markdown("# Welcome to Jude Mental Health AI Chat\n # What can we help you with?")
# Chatbot output area (AI's response)
chatbot_output = gr.Chatbot(label="Chat with Jude Mental Health AI")
# Textbox for user input (prompt)
with gr.Row():
user_input = gr.Textbox(placeholder="Enter your question here...", label="Ask a question", interactive=True, elem_id="user_input", lines=3)
submit_button = gr.Button("Send", elem_id="submit_button") # Shorter button
# Mental Health Topics Buttons
with gr.Row():
topic_button_anxiety = gr.Button("Anxiety")
topic_button_depression = gr.Button("Depression")
topic_button_sleep = gr.Button("Impact of Sleep")
topic_button_drugs = gr.Button("Drugs & Mental Health")
topic_button_more = gr.Button("More")
# Gradio sliders to control the parameters for response generation
max_tokens_slider = gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max Tokens")
temperature_slider = gr.Slider(minimum=0.1, maximum=2.0, value=0.7, step=0.1, label="Temperature")
top_p_slider = gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)")
# Action when submit button is clicked
def on_submit(message, history, max_tokens, temperature, top_p):
history.append((message, ""))
for response in chatbot(message, history, system_message, max_tokens, temperature, top_p):
history[-1] = (message, response) # Update the history with the new response
return history, gr.update(value="") # Clear the input textbox after submission
submit_button.click(on_submit, inputs=[user_input, gr.State([]), max_tokens_slider, temperature_slider, top_p_slider], outputs=[chatbot_output, user_input])
# Action when topic button is clicked
def on_topic_click(topic, history, max_tokens, temperature, top_p):
topic_message = f"Tell me about {topic}."
history.append((topic_message, ""))
for response in chatbot(topic_message, history, system_message, max_tokens, temperature, top_p):
history[-1] = (topic_message, response)
return history, gr.update(value="") # Clear the input textbox after topic selection
topic_button_anxiety.click(on_topic_click, inputs=[topic_button_anxiety, gr.State([]), max_tokens_slider, temperature_slider, top_p_slider], outputs=[chatbot_output, user_input])
topic_button_depression.click(on_topic_click, inputs=[topic_button_depression, gr.State([]), max_tokens_slider, temperature_slider, top_p_slider], outputs=[chatbot_output, user_input])
topic_button_sleep.click(on_topic_click, inputs=[topic_button_sleep, gr.State([]), max_tokens_slider, temperature_slider, top_p_slider], outputs=[chatbot_output, user_input])
topic_button_drugs.click(on_topic_click, inputs=[topic_button_drugs, gr.State([]), max_tokens_slider, temperature_slider, top_p_slider], outputs=[chatbot_output, user_input])
topic_button_more.click(on_topic_click, inputs=[topic_button_more, gr.State([]), max_tokens_slider, temperature_slider, top_p_slider], outputs=[chatbot_output, user_input])
return demo
# Launch the interface
demo = create_interface()
demo.launch()
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