import gradio as gr
from openai import OpenAI
import os
from IPython.display import display, Markdown

# Liste der verfügbaren Modelle
MODELS = [
    "llama3-70b-8192",
    "llama3-8b-8192",
    "qwen-qwq-32b",
    "mistral-saba-24b",
    "qwen-2.5-coder-32b",
    "qwen-2.5-32b",
    "deepseek-r1-distill-qwen-32b",
    "deepseek-r1-distill-llama-70b-specdec",
    "deepseek-r1-distill-llama-70b",
    "llama-3.2-3b-preview",
    "llama-3.2-11b-vision-preview"
]

def predict(model, input_text):
    # Initialisiere den Groq Client
    client = OpenAI(
        base_url="https://api.groq.com/openai/v1",
        api_key=os.environ.get("GROQ_API_KEY"),
    )

    # Sende Anfrage an die Groq API
    completion = client.chat.completions.create(
        model=model,
        messages=[
            {
                "role": "user",
                "content": input_text
            }
        ],
        temperature=0.1,
        max_tokens=4096,
        top_p=1,
        stream=False,
        stop=None,
    )

    # Hole die Antwort des Modells
    response = completion.choices[0].message.content

    # Zeige die Antwort als Markdown an
    display(Markdown(f"**Antwort des Modells ({model}):**\n\n{response}"))

    return response

# Erstelle die Gradio Oberfläche
with gr.Blocks() as demo:
    gr.Markdown("# Groq API Chat Interface")
    
    with gr.Row():
        model_dropdown = gr.Dropdown(
            choices=MODELS,
            value=MODELS[0],
            label="Wähle ein Modell"
        )
       
    output_text = gr.Markdown()
    
    with gr.Row():
        input_text = gr.Textbox()
        
    input_text.submit(
        fn=predict,
        inputs=[model_dropdown, input_text],
        outputs=output_text
    )   
    #submit_btn = gr.Button("Absenden")
    #submit_btn.click

if __name__ == "__main__":
    demo.launch()