app.py

import gradio as gr
import requests
import os
import json
import google.generativeai as genai
from bs4 import BeautifulSoup
from googleapiclient.discovery import build

# 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 google_search(search_term, **kwargs):
    service = build("customsearch", "v1", developerKey=api_key) # Ersetze YOUR_GOOGLE_API_KEY mit deinem API-Key
    res = service.cse().list(q=search_term, cx="77f1602c0ff764edb", **kwargs).execute() # Ersetze YOUR_CUSTOM_SEARCH_ENGINE_ID mit deiner Custom Search Engine ID
    return res

def get_impressum_text(search_term):

    search_results = google_search(search_term)

    if 'items' in search_results:
        for item in search_results['items']:
            link = item['link']
                response = requests.get(link, timeout=5) # Timeout hinzugefügt für Fehlerbehandlung
                response.raise_for_status() # Wirft eine Exception, wenn der Statuscode nicht 200 ist
                soup = BeautifulSoup(response.content, 'html.parser')

                impressum_div = soup.find('div', class_='MjjYud')

                return impressum_div.text.strip()



def websearch(prompt):
    
    url = f"https://www.google.com/search?q={prompt}"
    headers = {
        "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36"
    }
    
    try:
        response = requests.get(url, headers=headers)
        response.raise_for_status() # Wirft eine Exception für Fehlercodes
    except requests.exceptions.RequestException as e:
       print(f"Fehler beim Abrufen der Google-Seite: {e}")
       return None

    soup = BeautifulSoup(response.content, 'html.parser')
    
    first_div = soup.find('div', class_='MjjYud')
    
    if first_div:
        return first_div.text.strip()
    else:
        print("Kein div mit der Klasse 'MjjYud' gefunden.")
        return None

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=get_impressum_text, inputs=ort_input, outputs=details_output)   

# Launch the Gradio application
demo.launch()