app.py

import gradio as gr
import requests
import os
import json
import google.generativeai as genai

# Load environment variables
genai.configure(api_key=os.environ["geminiapikey"])
read_key = os.environ.get('HF_TOKEN', None)

custom_css = """
#md {
    height: 400px;  
    font-size: 30px;
    background: #202020;
    padding: 20px;
    color: white;
    border: 1 px solid white;
}
"""

def predict(prompt):
    # Create the model
    generation_config = {
        "temperature": 0.3,
        "top_p": 0.95,
        "top_k": 40,
        "max_output_tokens": 2048,
        "response_mime_type": "text/plain",
    }

    model = genai.GenerativeModel(
        #model_name="gemini-1.5-pro",
        model_name="gemini-2.0-flash-exp",
        generation_config=generation_config,
    )

    chat_session = model.start_chat(
        history=[
        ]
    )
    
    response = chat_session.send_message(prompt)
    #response = model.generate_content(contents=prompt, tools='google_search_retrieval')
    return response.text

# Create the Gradio interface
with gr.Blocks(css=custom_css) as demo:
    with gr.Row():
        details_output = gr.Markdown(label="answer", elem_id="md")        
        #details_output = gr.Textbox(label="Ausgabe", value = f"\n\n\n\n")  
    with gr.Row():
        ort_input = gr.Textbox(label="prompt", placeholder="ask anything...")      
    with gr.Row():         
        button = gr.Button("Senden")    

    # Connect the button to the function
    button.click(fn=predict, inputs=ort_input, outputs=details_output)   

# Launch the Gradio application
demo.launch()