Update app.py

app.py

@@ -1,12 +1,13 @@
-import os
 import requests
-import bs4
-from bs4 import BeautifulSoup
 import gradio as gr
+import bs4 
+from bs4 import BeautifulSoup
+
 
-api_token = os.environ.get("TOKEN")
+# 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}"}
+headers = {"Authorization": f"Bearer {API_TOKEN}"}
 
 def query(payload):
     response = requests.post(API_URL, headers=headers, json=payload)
@@ -35,45 +36,37 @@ you are going to analyse the prompt that i'll give to you and tell me if they ar
         else:
             return "autre"
 
-def scrape_huggingface_posts(url):
-    response = requests.get(url)
-    soup = BeautifulSoup(response.text, 'html.parser')
-    
-    # Ajustez ce sélecteur selon la structure réelle de la page
-    posts = soup.find_all('div', class_='space-y-3 pl-7')
-    
-    extracted_posts = []
-    for post in posts:
-        # Extrayez les informations pertinentes de chaque post
-        title = post.find('h2', class_='post-title').text.strip()
-        content = post.find('div', class_='post-content').text.strip()
-        author = post.find('span', class_='post-author').text.strip()
+def scrape_and_analyze(url):
+    try:
+        response = requests.get(url)
+        soup = BeautifulSoup(response.text, 'html.parser')
         
-        extracted_posts.append({
-            'title': title,
-            'content': content,
-            'author': author
-        })
-    
-    return extracted_posts
-
-# Utilisation des fonctions
-url = "https://huggingface.co/posts"
-all_posts = scrape_huggingface_posts(url)
-
-# Analyse de chaque post
-for post in all_posts:
-    category = analyze_sentiment(post['content'])
-    print(f"Post titre: {post['title']}")
-    print(f"Auteur: {post['author']}")
-    print(f"Catégorie: {category}")
-    print("---")
+        # 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)}"
 
-# Interface Gradio (si vous voulez la garder)
-demo = gr.Interface(
-    fn=analyze_sentiment,
+iface = gr.Interface(
+    fn=scrape_and_analyze,
     inputs="text",
-    outputs="text"
+    outputs="text",
+    title="Analyse de posts de blog",
+    description="Entrez l'URL d'un blog pour analyser ses posts."
 )
 
-demo.launch()
+iface.launch()