app.py

import gradio as gr
import requests
import re
import os
import json
import google.generativeai as genai
from bs4 import BeautifulSoup
from google.ai.generativelanguage_v1beta.types import content
from IPython.display import display
from IPython.display import Markdown
#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: #121212;
    padding: 20px;
    color: white;
    border: 1 px solid white;
}
"""

generation_config = {
  "temperature": 0.3,
  "top_p": 0.95,
  "top_k": 64,
  "max_output_tokens": 8192,
  "response_mime_type": "text/plain",
}

def ground_search(prompt):
    model = genai.GenerativeModel(
      model_name="gemini-2.0-pro-exp-02-05",
      generation_config=generation_config,
      tools = [
        genai.protos.Tool(
          google_search = genai.protos.Tool.GoogleSearch(),
        ),
      ],
    )

    chat_session = model.start_chat(
      history=[
        {
          "role": "user",
          "parts": [
            "",
          ],
        },
        {
          "role": "model",
          "parts": [
            "",
          ],
        },
      ]
    )

    response = chat_session.send_message(f"{prompt}")

    #print(response.text)
    return response.text

def duckduckgo(search_term):
    url = f"https://duckduckgo.com/?q=impressum+{search_term}&ia=web"
    try:
        response = requests.get(url)
        #response.raise_for_status()  # Raises HTTPError for bad responses
        s1 = response.text
        # Removing HTML tags using Beautiful Soup
        s2 = re.sub(r"<.*?>", "", s1)
        return s1     
        #return response.text  # Return the content of the response
    except requests.exceptions.RequestException as e:
        print(f"An error occurred: {e}")
        return response.text

#api_key = os.getenv('groq')
google_api_key = os.getenv('google_search')
#API_URL = "https://blavken-flowiseblav.hf.space/api/v1/prediction/fbc118dc-ec00-4b59-acff-600648958be3"

def query(payload):
    API_URL = f"https://specialist-it.de/bots.php?json={payload}"
    try:
        response = requests.post(API_URL)
        response.raise_for_status()  # Raises HTTPError for bad responses
        return response.text  # Return the content of the response
    except requests.exceptions.RequestException as e:
        print(f"An error occurred: {e}")
        return response.text

def querys(payloads):
    output = query(payloads)
    print(output)
    #return result_text
      
    # Formuliere die Antwort
    search_query = f"{payloads} antworte kurz und knapp. antworte auf deutsch. du findest die antwort hier:\n {output}"
    result = predict(search_query)
    texte=""
    for o in output:
        texte +=o
    return result

#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}"   
    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)   
    # 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)
    
    display(Markdown(result))
    return result
    return result_text
    return results

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()