Update streamlit_app.py

streamlit_app.py

@@ -333,26 +333,26 @@ if active_df is not None and not active_df.empty:
                     if hf_token:
                         ui_client = InferenceClient("meta-llama/Llama-3.1-8B-Instruct", token=hf_token)
                         for i in range(num_clusters):
-                        cluster_df = weekly_df[weekly_df['cluster'] == i]
-                        sample_texts = "\n".join(cluster_df['title'].head(8).tolist())
-                        
-                        prompt = f"""
-                        You are a highly structured D.C. Tech Policy Taxonomist. Categorize these related article titles into a SINGLE, broad policy or industry bucket.
-                        
-                        RULES:
-                        1. MACRO CATEGORIES ONLY: Use 1 to 3 words maximum. Think of these as slide deck section headers.
-                        2. NO HEADLINES: Absolutely NO verbs, NO company names, NO numbers, and NO dollar amounts. 
-                           * BAD: "Start-Up Raises $1.3 Billion", "Congress Debates AI Bill"
-                           * GOOD: "Venture Capital", "Legislative Action", "AI Infrastructure"
-                        3. EXAMPLES OF IDEAL LABELS: "AI Infrastructure", "Export Controls", "AI Safety", "Defense & Security", "Consumer Regulation", "Industry Update".
-                        4. FILTER NOISE: If the articles are not about AI, compute, or tech policy, reply EXACTLY with: REJECT.
-                        5. FORMAT: Just the category name. No quotes, no extra text.
-                        
-                        UPDATES:
-                        {sample_texts}
-                        """
-                        
-                        messages = [{"role": "user", "content": prompt}]
+                            cluster_df = weekly_df[weekly_df['cluster'] == i]
+                            sample_texts = "\n".join(cluster_df['title'].head(8).tolist())
+                            
+                            prompt = f"""
+                            You are a highly structured D.C. Tech Policy Taxonomist. Categorize these related article titles into a SINGLE, broad policy or industry bucket.
+                            
+                            RULES:
+                            1. MACRO CATEGORIES ONLY: Use 1 to 3 words maximum. Think of these as slide deck section headers.
+                            2. NO HEADLINES: Absolutely NO verbs, NO company names, NO numbers, and NO dollar amounts. 
+                               * BAD: "Start-Up Raises $1.3 Billion", "Congress Debates AI Bill"
+                               * GOOD: "Venture Capital", "Legislative Action", "AI Infrastructure"
+                            3. EXAMPLES OF IDEAL LABELS: "AI Infrastructure", "Export Controls", "AI Safety", "Defense & Security", "Consumer Regulation", "Industry Update".
+                            4. FILTER NOISE: If the articles are not about AI, compute, or tech policy, reply EXACTLY with: REJECT.
+                            5. FORMAT: Just the category name. No quotes, no extra text.
+                            
+                            UPDATES:
+                            {sample_texts}
+                            """
+                            
+                            messages = [{"role": "user", "content": prompt}]
                             try:
                                 response = ui_client.chat_completion(messages, max_tokens=15, temperature=0.0)
                                 topic_name = response.choices[0].message.content.strip(' "').upper()