app.py

import gradio as gr
import requests
import os
import json
import google.generativeai as genai
from bs4 import BeautifulSoup
#from groq import Groq
# Load environment variables
genai.configure(api_key=os.environ["geminiapikey"])
read_key = os.environ.get('HF_TOKEN', None)
cx="77f1602c0ff764edb"

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

#api_key = os.getenv('groq')
google_api_key = os.getenv('google_search')
#if api_key is None:
    #raise ValueError("groq_whisper environment variable is not set")

# Initialize the Groq client
#client = Groq(api_key=api_key)

#very simple (and extremly fast) websearch  
def websearch(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"
    }
    #url = f"https://www.googleapis.com/customsearch/v1?key={google_api_key}&cx={cx}&q={prompt}"
    url = f"https://www.google.com/search?q={prompt}"

    response = requests.get(url, headers=headers)
    soup = BeautifulSoup(response.content, 'html.parser')
    response_text = soup.find('body')
    #prompt = f"{search_term}\n use this result from a google search to answer the question \n {response_text.text}"
    #result = predict(prompt)
    return response_text.text


    
    response = requests.get(url, headers=headers)
    data = response.json()  # JSON-Daten direkt verarbeiten
    # Extrahieren des Textes aus den Ergebnissen
    items = data.get('items', [])       
    results = [item['snippet'] for item in items]
    result_text = '\n'.join(results)
    #return results[0]
    return result_text
    
    # URL der Google Custom Search API
    url = f"https://www.googleapis.com/customsearch/v1?key={google_api_key}&cx={cx}&q={prompt}"
    response = requests.get(url, headers=headers)
    soup = BeautifulSoup(response.content, 'html.parser')
    response_text = soup.find('body')
    #prompt = f"{search_term}\n use this result from a google search to answer the question \n {response_text.text}"
    #result = predict(prompt)
    return response_text.text



def perform_search(prompt):
    if prompt.strip() == '':
        return ''  # Return empty string for empty search

    # URL der Google Custom Search API
    url = f"https://www.googleapis.com/customsearch/v1?key={google_api_key}&cx={cx}&q={prompt}"

    try:
        # HTTP GET-Anfrage an die Google Custom Search API
        response = requests.get(url)
        # JSON-Antwort parsen
        data = response.json()       
        # Extrahiere die Suchergebnisse
        items = data.get('items', [])       
        results = [item['snippet'] for item in items]
        #return results[0]
        # Kombiniere die Ergebnisse zu einem String
        result_text = '\n'.join(results)
        #return results[0]
        # Formuliere die Antwort
        #search_query = f"{prompt} antworte kurz und knapp. antworte auf deutsch. du findest die antwort hier: {result_text}"
        #result = predict(search_query)
        #return result
        return result_text

    except requests.exceptions.RequestException as e:
        print(f"An error occurred: {e}")
        return ''

def predict(prompt):
    generation_config = {
      "temperature": 0.4,
      "top_p": 0.95,
      "top_k": 40,
      "max_output_tokens": 8192,
      "response_mime_type": "text/plain",
    }

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

    chat_session = model.start_chat(
      history=[]
    )
      
    response = chat_session.send_message(f"{prompt}\n antworte immer auf deutsch")
    response_value = response.candidates[0].content.parts[0].text
    return response_value

# 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...")
        #audio_input=gr.Microphone(type="filepath")
    with gr.Row():         
        button = gr.Button("Senden")    

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

# Launch the Gradio application
demo.launch()