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import gradio as gr
import requests
import os
import json
import google.generativeai as genai
from bs4 import BeautifulSoup
# 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 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=websearch, inputs=ort_input, outputs=details_output)
# Launch the Gradio application
demo.launch() |