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| 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: #121212; | |
| padding: 20px; | |
| color: white; | |
| border: 1 px solid white; | |
| } | |
| """ | |
| #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): | |
| response = requests.post(API_URL, json=payload) | |
| return response.json() | |
| def querys(payloads): | |
| output = query({ | |
| "question": f"{payloads}", | |
| }) | |
| print(output.text) | |
| #return result_text | |
| texte="" | |
| for o in output: | |
| texte +=o | |
| return output | |
| #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) | |
| 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=querys, inputs=ort_input, outputs=details_output) | |
| # Launch the Gradio application | |
| demo.launch() |