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import requests
import gradio as gr
import bs4
from bs4 import BeautifulSoup
# Configuration de l'API (à ajuster selon votre setup dans le Space)
API_TOKEN = "votre_token_api"
API_URL = "https://api-inference.huggingface.co/models/meta-llama/Meta-Llama-3-8B-Instruct"
headers = {"Authorization": f"Bearer {API_TOKEN}"}
def query(payload):
response = requests.post(API_URL, headers=headers, json=payload)
return response.json()
def analyze_sentiment(text):
output = query({
"inputs": f'''<|begin_of_text|>
<|start_header_id|>system<|end_header_id|>
you are going to analyse the prompt that i'll give to you and tell me if they are either talking about "chat bot", "AI dev",
<|eot_id|>
<|start_header_id|>user<|end_header_id|>
{text}
<|eot_id|>
<|start_header_id|>assistant<|end_header_id|>
'''
})
if isinstance(output, list) and len(output) > 0:
response = output[0].get('generated_text', '').strip().lower()
if "chat bot" in response:
return "chat bot"
elif "ai dev" in response:
return "AI dev"
else:
return "autre"
def scrape_and_analyze(url):
try:
response = requests.get(url)
soup = BeautifulSoup(response.text, 'html.parser')
# Ajustez ce sélecteur selon la structure du site cible
posts = soup.find_all('div', class_='post')
categories = {"chat bot": 0, "AI dev": 0, "autre": 0}
for post in posts:
content = post.find('div', class_='content').text.strip() if post.find('div', class_='content') else "Pas de contenu"
category = analyze_sentiment(content)
categories[category] += 1
total_posts = sum(categories.values())
result = f"Total des posts analysés : {total_posts}\n"
result += f"chat bot : {categories['chat bot']}\n"
result += f"AI dev : {categories['AI dev']}\n"
result += f"autre : {categories['autre']}"
return result
except Exception as e:
return f"Une erreur s'est produite : {str(e)}"
iface = gr.Interface(
fn=scrape_and_analyze,
inputs="text",
outputs="text",
title="Analyse de posts de blog",
description="Entrez l'URL d'un blog pour analyser ses posts."
)
iface.launch() |