Update app.py

app.py

@@ -1,13 +1,15 @@
 import requests
-import gradio as gr
-import bs4 
 from bs4 import BeautifulSoup
+import gradio as gr
+import os 
 
 
-# Configuration de l'API (à ajuster selon votre setup dans le Space)
-API_TOKEN = "votre_token_api"
+api_token = os.environ.get("TOKEN")
 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}"}
+
+url = "https://huggingface.co/posts" 
+
 
 def query(payload):
     response = requests.post(API_URL, headers=headers, json=payload)
@@ -15,14 +17,14 @@ def query(payload):
 
 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|>
+        "inputs": f'''
+system
+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", or something else.
+
+user
 {text}
-<|eot_id|>
-<|start_header_id|>assistant<|end_header_id|>
+
+assistant
 '''
     })
 
@@ -35,38 +37,48 @@ you are going to analyse the prompt that i'll give to you and tell me if they ar
             return "AI dev"
         else:
             return "autre"
+    return "autre"
 
 def scrape_and_analyze(url):
     try:
         response = requests.get(url)
+        if response.status_code != 200:
+            return f"Erreur lors de la requête : {response.status_code}"
+        
         soup = BeautifulSoup(response.text, 'html.parser')
         
         # Ajustez ce sélecteur selon la structure du site cible
-        posts = soup.find_all('div', class_='post')
+        posts = soup.find_all('div', class_='cursor-pointer')
         
         categories = {"chat bot": 0, "AI dev": 0, "autre": 0}
+        total_posts = 0
+        result = ""
         
         for post in posts:
-            content = post.find('div', class_='content').text.strip() if post.find('div', class_='content') else "Pas de contenu"
+            total_posts += 1
+            content = post.find('div', class_='relative').text.strip() if post.find('div', class_='relative') else "Pas de contenu"
+            
+            # Analyse du texte
             category = analyze_sentiment(content)
             categories[category] += 1
+            
+            # Affichage en temps réel
+            print(f"Post {total_posts} analysé. Catégorie : {category}")
+            print(f"Compteurs actuels : {categories}")
+            print("---")
+            
+            # Ajout des résultats à la chaîne finale
+            result += f"Post {total_posts} : Catégorie {category}\n"
         
-        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']}"
+        # Résultat final
+        result += f"\nTotal des posts analysés : {total_posts}\n"
+        for cat, count in categories.items():
+            result += f"{cat} : {count}\n"
         
         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."
-)
+result = scrape_and_analyze(url)
+print(result)
 
-iface.launch()