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PredictionHeadline2
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import streamlit as st
import requests
from bs4 import BeautifulSoup
import csv
import os
import pandas as pd
import random
from huggingface_hub import Repository, HfApi, HfFolder
import openai

api = HfApi()
token = os.getenv("token")
tokenread = os.getenv("tokenread")
localdir = "HeadlinePrediction"
repo = Repository(local_dir=localdir, clone_from="https://huggingface.co/spaces/Add1E/HeadlinePrediction", token=token)


def add_to_csv(var1, var2, var3, var4, var5, var6, var7, filename):
    # Öffnen der CSV-Datei im Anhängemodus
    with open(os.path.abspath(f'{localdir}/results.csv'), 'a', newline='', encoding = "utf-8") as file:
        writer = csv.writer(file)

        # Hinzufügen der Variablen als neue Zeile in die CSV-Datei
        writer.writerow([var1, var2, var3, var4, var5, var6, var7])


# Definiert die Funktion zum Scrapen der Webseite
def scrape_website(url):
    try:
        response = requests.get(url)
        response.raise_for_status()
        soup = BeautifulSoup(response.text, 'html.parser')
        content = {
            'scraped_html': ' '.join(p.get_text() for p in soup.find_all('p', class_="sc-beqWaB jOAegM")),
            'heute_überschrift': ' '.join(p.get_text() for p in soup.find_all('h1', class_="sc-beqWaB iTcspr")),
            'heute_zsm': ' '.join(p.get_text() for p in soup.find_all('p', class_="sc-beqWaB iOdRIJ"))
        }
        return content
    except Exception as e:
        return str(e)


def send_to_chatgpt(api_key, prompt_file, transcript):
    try:
        # Liest den Prompt aus der Datei
        with open(prompt_file, 'r', encoding='utf-8') as file:
            prompt = file.read().strip()

        openai.api_key = api_key
        response = openai.ChatCompletion.create(
            model="gpt-4-1106-preview",
            messages=[
                {"role": "system", "content": prompt},
                {"role": "system", "content": transcript}
            ],
        )
        return response.choices[0].message["content"]
    except Exception as e:
        return str(e)


st.title("Webseiten-Scraper")
# Beispiel für die Verwendung der Funktiona
api_key = os.getenv("api_key")  # Setzen Sie hier Ihren OpenAI-API-Schlüssel ein
base_prompt = "txt.txt"
csv_name = "results.csv"
df = pd.read_csv(csv_name, encoding='utf-8')
df.columns = [col.replace(' ', '_') for col in df.columns] 
# Eingabefelder für URL und p_class
url = st.text_input("URL eingeben", "https://www.beispielwebsite.com")


if st.button("Scrape"):
    if url:
        scraped_html = scrape_website(url)
        response = send_to_chatgpt(api_key, base_prompt, scraped_html['scraped_html'])
        st.code(response)

        teile = response.split("Kernaussagen:")
        ueberschriften_teil, kernaussagen_teil = teile[0], teile[1]

        ueberschriften = ueberschriften_teil.split("\n")[1:]  # Erste Zeile überspringen
        ueberschrift_1 = ueberschriften[0].split(" ", 1)[1]  # "1." entfernen
        ueberschrift_2 = ueberschriften[1].split(" ", 1)[1]  # "2." entfernen

        kernaussagen = kernaussagen_teil.split("\n")[1:]  # Erste Zeile überspringen
        kernaussage_1 = kernaussagen[0].split(" ", 1)[1]  # "1." entfernen
        kernaussage_2 = kernaussagen[1].split(" ", 1)[1]  # "2." entfernen

        add_to_csv(
            url, scraped_html['heute_überschrift'], ueberschrift_1, ueberschrift_2, scraped_html['heute_zsm'],
            kernaussage_1, kernaussage_2, csv_name
        )

    else:
        st.error("Bitte geben Sie eine gültige URL ein.")


if st.sidebar.button("Upload Data"):
    repo.git_add(os.path.abspath(f'{localdir}/results.csv'))
    repo.git_commit("Add new headlines.csv")
    repo.git_push()
    

auswahl = st.sidebar.selectbox("Wählen Sie eine Prediction:", ["None", "Prediction 1", "Prediction 2", "Prediction 3", "Prediction 4", "Prediction 5"])
random_numbers = set()
while len(random_numbers) < 5:
    random_numbers.add(random.randint(0, len(df)-1))

random_numbers = list(random_numbers)


if auswahl == "Prediction 1":
    st.dataframe(df.iloc[random_numbers[0]])
elif auswahl == "Prediction 2":
    st.dataframe(df.iloc[random_numbers[1]])
elif auswahl == "Prediction 3":
    st.dataframe(df.iloc[random_numbers[2]])
elif auswahl == "Prediction 4":
    st.dataframe(df.iloc[random_numbers[3]])
elif auswahl == "Prediction 5":
    st.dataframe(df.iloc[random_numbers[4]])


if st.sidebar.button("Show Full Data"):
    df = pd.read_csv(csv_name, encoding='utf-8')
    st.dataframe(df)