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import gradio as gr
from huggingface_hub import InferenceClient
import os
import logging
import asyncio
from dotenv import load_dotenv
# Load environment variables
load_dotenv()
api_key = os.getenv("HUGGING_FACE_API_TOKEN")
# Available models
models = {
"Llama-3B": "meta-llama/Llama-3.2-3B-Instruct",
"DeepSeek-Coder": "deepseek-ai/deepseek-coder-1.3b-instruct",
"DeepSeek-R1": "deepseek-ai/DeepSeek-R1-Distill-Qwen-32B",
"Mistral-7B": "mistralai/Mistral-7B-Instruct-v0.3"
}
# Initialize client
client = InferenceClient(api_key=api_key)
# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
# Function to interact with selected model
async def chat_with_model(user_query, model_name):
if model_name not in models:
return "❌ Invalid model selection. Please choose a valid model."
model_id = models[model_name]
messages = [
{"role": "system", "content":
"""
"""
},
{"role": "user", "content": user_query}
]
max_retries = 3
for attempt in range(1, max_retries + 1):
try:
response = client.chat.completions.create(
model=model_id,
messages=messages,
temperature=0.5,
max_tokens=1024, # Reduce for faster response
top_p=0.7,
stream=False
)
return response.choices[0].message.content
except Exception as e:
logger.warning(f"Attempt {attempt}/{max_retries} failed: {str(e)}")
if attempt < max_retries:
await asyncio.sleep(1) # Short delay before retrying
return "❌ The model is currently unavailable after multiple retries. Please try again later."
# Create Gradio UI
def chat_interface(user_query, model_name):
return asyncio.run(chat_with_model(user_query, model_name))
with gr.Blocks() as demo:
gr.Markdown("## Harry's AI Chatbot")
gr.Markdown("### Select a model and ask your question to get a response from the AI.")
with gr.Row():
model_dropdown = gr.Dropdown(
choices=list(models.keys()),
label="Select AI Model",
value="Mistral-7B"
)
user_input = gr.Textbox(label="Enter your message", placeholder="Type your question here...")
chat_button = gr.Button("Chat")
output_text = gr.Textbox(label="AI Response", interactive=False)
chat_button.click(chat_interface, inputs=[user_input, model_dropdown], outputs=output_text)
# Launch Gradio app
demo.launch()
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