app.py

import streamlit as st
import os
import google.generativeai as genai

# Streamlit Seite konfigurieren
st.set_page_config(
    page_title="Gemini Chatbot mit Google Search",
    page_icon="🤖"
)


genai.configure(api_key=os.environ["geminiapi"])

# Modell-Konfiguration
generation_config = {
    "temperature": 0.4,
    "top_p": 0.95,
    "top_k": 40,
    "max_output_tokens": 8192,
    "response_mime_type": "text/plain",
}

grounding_config = genai.types.GroundingConfig(
    source_type=genai.types.GroundingSource.GOOGLE_SEARCH
)

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

# Chat Session State initialisieren
if "chat_session" not in st.session_state:
    st.session_state.chat_session = model.start_chat(history=[])

# UI Komponenten
st.title("🤖 Gemini Chatbot mit Google Search")
user_input = st.text_input("Stelle deine Frage:", key="user_input")

if user_input:
    # Prompt mit Sprachaufforderung kombinieren
    full_prompt = f"{user_input}\nAntworte immer auf Deutsch"
    
    # Antwort generieren
    response = st.session_state.chat_session.send_message(full_prompt)
    
    # Antwort extrahieren
    if response.candidates:
        response_text = response.candidates[0].content.parts[0].text
    else:
        response_text = "Keine Antwort erhalten"
    
    # Antwort anzeigen
    st.subheader("Antwort:")
    st.write(response_text)
    
    # Quellen anzeigen falls vorhanden
    if response.grounding_metadata:
        st.subheader("Quellen:")
        for source in response.grounding_metadata.sources:
            st.markdown(f"- [{source.url}]({source.url})")









"""
# Flask API
from flask import Flask, jsonify
import threading
from flask_cors import CORS, cross_origin

# Streamlit Frontend
import streamlit as st
import requests
st.title("huhu")
#st.text(data)


data="ok"
app = Flask(__name__)
cors = CORS(app)
@app.route('/endpoint', methods=['GET'])
def my_endpoint():
    #get data from GET Request
    data = request.args  # Die Daten, die vom Client gesendet wurden
    
    #post request
    #data = request.form.get('variable')
    #print(prompt)
    #st.text(data)
    return "Daten erfolgreich empfangen!"

    
    #result = selenium(prompt)
    # Das Ergebnis an PHP zurückgeben
    #import streamlit as st
    #st.write(prompt) 
    #prompting(prompt)
    #return prompt

def run_flask():
    app.run(port=5000)


# Starte den Flask-Server in einem separaten Thread
flask_thread = threading.Thread(target=run_flask)
flask_thread.start()




#response = requests.get('https://huggingface.co/spaces/mgokg/PandasAI:5000/api?data=huhu')
#data = response.json()

#st.write(response)  # Zeigt die Daten in der Streamlit-Oberfläche an

"""