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import gradio as gr
from groq import Groq
# Initialize Groq client with your API key
client = Groq(api_key="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")
def chat_with_groq(message, history, model_choice, temperature, max_tokens):
"""
Function to interact with Groq API
"""
try:
# Prepare conversation history in the format Groq expects
messages = []
# Add conversation history
for human, assistant in history:
messages.append({"role": "user", "content": human})
messages.append({"role": "assistant", "content": assistant})
# Add current message
messages.append({"role": "user", "content": message})
# Create chat completion
chat_completion = client.chat.completions.create(
messages=messages,
model=model_choice,
temperature=temperature,
max_tokens=max_tokens,
top_p=1,
stream=False
)
# Get the response
response = chat_completion.choices[0].message.content
return response
except Exception as e:
return f"Error: {str(e)}"
# Available Groq models
available_models = [
"llama-3.1-8b-instant",
"llama-3.1-70b-versatile",
"mixtral-8x7b-32768",
"gemma2-9b-it"
]
def predict(message, chat_history, model_choice, temperature, max_tokens):
"""
Predict function for Gradio ChatInterface
"""
if not message.strip():
return chat_history
# Get response from Groq
bot_response = chat_with_groq(message, chat_history, model_choice, temperature, max_tokens)
# Add to chat history
chat_history.append((message, bot_response))
return chat_history
# Custom CSS for better styling
custom_css = """
#chatbot {
min-height: 500px;
}
.container {
max-width: 1200px;
margin: auto;
}
"""
with gr.Blocks(
theme=gr.themes.Soft(),
title="Groq AI Chatbot",
css=custom_css
) as demo:
gr.Markdown(
"""
# 🤖 Groq AI Chatbot
Fast AI-powered chatbot powered by Groq API
**Note**: This chatbot uses Groq's inference API for fast responses.
"""
)
# Store chat history
chatbot = gr.Chatbot(
label="Conversation",
height=500,
show_copy_button=True,
elem_id="chatbot"
)
with gr.Row():
msg = gr.Textbox(
label="Your Message",
placeholder="Type your message here and press Enter...",
lines=2,
scale=4,
container=False
)
with gr.Row():
send_btn = gr.Button("Send 🚀", variant="primary", size="lg")
clear_btn = gr.Button("Clear Chat 🗑️", variant="secondary")
with gr.Accordion("⚙️ Model Settings", open=False):
with gr.Row():
model_choice = gr.Dropdown(
choices=available_models,
value="llama-3.1-8b-instant",
label="Select Model",
info="Choose which AI model to use"
)
with gr.Row():
temperature = gr.Slider(
minimum=0.1,
maximum=1.0,
value=0.7,
step=0.1,
label="Temperature",
info="Controls creativity: Lower = more deterministic, Higher = more creative"
)
max_tokens = gr.Slider(
minimum=100,
maximum=4096,
value=1024,
step=100,
label="Max Tokens",
info="Maximum length of the response"
)
# Function to handle user message
def user_message(user_msg, history):
return "", history + [[user_msg, None]]
# Function to handle bot response
def bot_message(history, model, temp, tokens):
user_msg = history[-1][0]
bot_msg = chat_with_groq(user_msg, history[:-1], model, temp, tokens)
history[-1][1] = bot_msg
return history
# Event handlers
msg.submit(
user_message,
inputs=[msg, chatbot],
outputs=[msg, chatbot]
).then(
bot_message,
inputs=[chatbot, model_choice, temperature, max_tokens],
outputs=[chatbot]
)
send_btn.click(
user_message,
inputs=[msg, chatbot],
outputs=[msg, chatbot]
).then(
bot_message,
inputs=[chatbot, model_choice, temperature, max_tokens],
outputs=[chatbot]
)
clear_btn.click(
lambda: None,
outputs=[chatbot],
queue=False
).then(
lambda: [],
None,
[chatbot]
)
gr.Markdown(
"""
### 💡 About the Models:
- **Llama 3.1 8B Instant**: Fast and efficient for general conversations
- **Llama 3.1 70B Versatile**: More capable for complex tasks
- **Mixtral 8x7B**: Excellent for coding and reasoning tasks
- **Gemma2 9B**: Balanced performance across various tasks
### ⚠️ Important:
- Your conversations are processed through Groq's API
- The API key is embedded in this application
- For personal use only
"""
)
if __name__ == "__main__":
demo.launch(share=True, debug=True) |