import gradio as gr
import os
from openai import OpenAI
import requests

OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
openai_client = OpenAI(api_key=OPENAI_API_KEY)

DEEPSEEK_API_KEY = os.getenv("DEEPSEEK_API_KEY")
deepseek_base_url = "https://api.deepseek.com"  # assuming DeepSeek uses a REST API, you can adjust as needed

def generate_response(Model_provider, prompt, temperature, top_p, max_tokens, repetition_penalty):
    try:
        response = deepseek_client.chat.completions.create（
        model="deepseek-chat", #or "deepseek-reasoner" for R1 model
        messages=[f"role":"user","content": prompt}],
        temperature=temperature,
        top_p=top_p,
        max_tokens=max_tokens,
        presence_penalty=repetition_penalty,
        stream=False
)
    except Exception as e:
        return f"DeepSeek API Error: Istr(e)]"
elif model_provider == "OpenAI":
    try:
        response = openai_client.chat.completions.create（
        model="gpt-3.5-turbo", # or another model of your choice
        messages=[f"role": "user","content":prompt}],
        temperature=temperature,
        top_p=top_P,
        max_tokens=max_tokens,
        presence_penalty=repetition_penalty,
        stream=False
)
        return response.choices[o].message.content.strip()
    except Exception as e:
        return f"OpenAI API Error: [str(e)]"
    else:
        return "Invalid model provider selected."
with gr.Blocks() as demo:
    gr.Markdown("# LLM Chat Interface")
    with gr.Row():
        model_provider = gr.Dropdown(
        choices=["DeepSeek", "OpenAI"],
        value="DeepSeek",
        label="Select Model Provider"
        prompt = gr.Textbox(label="Enter your prompt", lines=4, placeholder="Type your message here..")
iface = gr.Interface(
    fn=generate_response,
    inputs=[
        gr.Dropdown(choices=["DeepSeek", "OpenAI"], value="DeepSeek", label="Model Provider"),
        gr.Textbox(label="Prompt", lines=6, placeholder="Ask something..."),
        gr.Slider(minimum=0.1, maximum=1.5, value=0.7, step=0.1, label="Temperature"),
        gr.Slider(minimum=0.1, maximum=1.0, value=0.9, step=0.05, label="Top-p"),
        gr.Slider(minimum=32, maximum=2048, value=512, step=32, label="Max New Tokens"),
        gr.Slider(minimum=1.0, maximum=2.0, value=1.1, step=0.1, label="Repetition Penalty")
    ],
    outputs="text",
    title="🧠 DeepSeek LLM Chat with Parameter Tuning",
    theme=gr.themes.Soft()
)

iface.launch()