Update app/main.py

app/main.py

@@ -9,8 +9,15 @@ toxicity_pipe = pipeline("text-classification", model="textdetox/twitter-xlmr-to
 
 # 2. UPGRADED: Use a highly capable Chat Model (Phi-3)
 model_id = "microsoft/Phi-3-mini-4k-instruct"
-tokenizer = AutoTokenizer.from_pretrained(model_id)
-llm_pipe = pipeline("text-generation", model=model_id, tokenizer=tokenizer)
+
+# --- ADD trust_remote_code=True HERE ---
+tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+llm_pipe = pipeline(
+    "text-generation", 
+    model=model_id, 
+    tokenizer=tokenizer, 
+    trust_remote_code=True # <--- THE FIX
+)
 
 profanity_filter = Filter({"languages": ["english", "spanish", "french"], "replace_with": "***"})
 
@@ -50,32 +57,26 @@ async def moderate_text(request: TextRequest):
     
     rewritten = None
     if is_flagged:
-        # 3. Craft a strict prompt asking for 5 different options
         messages = [
             {"role": "system", "content": "You are a helpful, polite assistant. Rewrite the user's toxic message into exactly 5 different safe, polite, and non-threatening sentences. Number them 1 to 5. Do not include any other text in your response."},
             {"role": "user", "content": text}
         ]
         
-        # Generate response
         prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
         outputs = llm_pipe(prompt, max_new_tokens=200, do_sample=True, temperature=0.7)
         
         generated_text = outputs[0]['generated_text'][len(prompt):].strip()
         
-        # 4. Parse the output into a clean list
         lines = generated_text.split('\n')
         rewritten = []
         for line in lines:
             line = line.strip()
-            if line and line[0].isdigit(): # Only keep lines that start with a number
-                # Remove the numbering (e.g., "1. " or "1- ")
+            if line and line[0].isdigit():
                 clean_line = re.sub(r'^\d+[\.\-\)]\s*', '', line).strip()
-                # Remove quotes if the AI added them
                 clean_line = clean_line.strip('"\'')
                 if clean_line:
                     rewritten.append(clean_line)
         
-        # Keep only the first 5, and strip any duplicates
         rewritten = list(dict.fromkeys(rewritten))[:5]
 
         if not rewritten: