app.py

import os
from dotenv import load_dotenv
import gradio as gr
from langchain_huggingface import HuggingFaceEndpoint

# Load environment variables
load_dotenv()
HF_TOKEN = os.getenv("HF_TOKEN")

# Initialize the Hugging Face endpoint for inference
llm = HuggingFaceEndpoint(
    repo_id="mistralai/Mistral-7B-Instruct-v0.3",  # Replace with your model repo
    huggingfacehub_api_token=HF_TOKEN.strip(),
    temperature=0.7,
    max_new_tokens=100
)

# Function to handle chatbot response
def chatbot_response(message):
    try:
        response = llm(message)
        return response
    except Exception as e:
        return f"Error: {e}"


# Gradio Interface for Chatbot without Guardrails
with gr.Blocks() as app_without_guardrails:
    gr.Markdown("## Chatbot Without Guardrails")
    gr.Markdown("This chatbot uses the model directly without applying any content filtering.")

    # Input and output
    with gr.Row():
        user_input = gr.Textbox(label="Your Message", placeholder="Type here...")
    response_output = gr.Textbox(label="Response", placeholder="Bot will respond here...")
    submit_button = gr.Button("Send")

    # Button click event
    submit_button.click(
        chatbot_response,
        inputs=[user_input],
        outputs=[response_output]
    )

# Launch the app
if __name__ == "__main__":
    app_without_guardrails.launch()