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import gradio as gr
from huggingface_hub import InferenceClient
import sys
import io
import traceback
import re # Import the regular expressions module
# Initialize the AI model
model_name = "Qwen/Qwen2.5-72B-Instruct"
client = InferenceClient(model_name)
def llm_inference(user_sample):
eos_token = "<|endoftext|>"
output = client.chat.completions.create(
messages=[
{
"role": "system",
"content": "You are a Python language guide. Write code on the user topic. If the input is code, correct it for mistakes."
},
{
"role": "user",
"content": f"Write only python code without any explanation: {user_sample}"
},
],
stream=False,
temperature=0.7,
top_p=0.1,
max_tokens=412,
stop=[eos_token]
)
response = ''
for choice in output.choices:
response += choice['message']['content']
return response
def execute_code(code):
old_stdout = sys.stdout
redirected_output = sys.stdout = io.StringIO()
try:
exec(code, {})
output = redirected_output.getvalue()
except Exception as e:
output = f"Error: {e}\n{traceback.format_exc()}"
finally:
sys.stdout = old_stdout
return output
def is_math_task(user_input):
"""
Simple heuristic to determine if the user input is a math task.
This can be enhanced with more sophisticated methods or NLP techniques.
"""
math_keywords = ['calculate', 'compute', 'solve', 'integrate', 'differentiate', 'derivative', 'integral', 'factorial', 'sum', 'product']
operators = ['+', '-', '*', '/', '^', '**', 'sqrt', 'sin', 'cos', 'tan', 'log', 'exp']
user_input_lower = user_input.lower()
return any(keyword in user_input_lower for keyword in math_keywords) or any(op in user_input for op in operators)
def chat(user_input, history):
"""
Handles the chat interaction. If the user input is detected as a math task,
it generates Python code to solve it, strips any code tags, executes the code,
and returns the result.
"""
if is_math_task(user_input):
# Generate Python code for the math task
generated_code = llm_inference(f"Create a Python program to solve the following math problem:\n{user_input}")
# Strip code tags using regex
# This regex removes ```python and ``` or any other markdown code fences
cleaned_code = re.sub(r"```(?:python)?\n?", "", generated_code).strip()
cleaned_code = re.sub(r"```", "", cleaned_code).strip()
# Execute the cleaned code
execution_result = execute_code(cleaned_code)
# Prepare the responses
assistant_response = f"**Generated Python Code:**\n```python\n{cleaned_code}\n```\n\n**Execution Result:**\n```\n{execution_result}\n```"
else:
# For regular chat messages, use the AI's response
assistant_response = llm_inference(user_input)
# Append to chat history
history.append((user_input, assistant_response))
return history, history
with gr.Blocks() as demo:
gr.Markdown("# 🐍 Python Helper Chatbot")
with gr.Tab("Chat"):
chatbot = gr.Chatbot()
msg = gr.Textbox(placeholder="Type your message here...")
msg.submit(chat, inputs=[msg, chatbot], outputs=[chatbot, chatbot])
with gr.Tab("Interpreter"):
gr.Markdown("### 🖥️ Test Your Code")
code_input = gr.Code(language="python")
run_button = gr.Button("Run Code")
code_output = gr.Textbox(label="Output")
run_button.click(execute_code, inputs=code_input, outputs=code_output)
with gr.Tab("Logs"):
gr.Markdown("### 📜 Logs")
log_output = gr.Textbox(label="Logs", lines=10, interactive=False)
# Launch the Gradio app
demo.launch()
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