Update app.py

app.py

@@ -19,9 +19,6 @@ model = AutoModelForCausalLM.from_pretrained(model_id)
 # Create a text generation pipeline
 pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
 
-# Define the pre-prompt
-PRE_PROMPT = "You are a helpful customer service assistant. Answer the customer's question clearl, with care and concisely."
-
 # Define request body schema
 class TextGenerationRequest(BaseModel):
     prompt: str
@@ -37,12 +34,9 @@ async def generate_text(request: TextGenerationRequest):
     try:
         logger.info("Generating text...")
         
-        # Combine the pre-prompt and user's prompt
-        combined_input = f"{PRE_PROMPT} {request.prompt}"
-        
-        # Generate text using the pipeline
+        # Generate text using the pipeline with the user's prompt
         outputs = pipe(
-            combined_input,  # Use the combined input
+            request.prompt,  # Use the user's prompt directly
             max_new_tokens=request.max_new_tokens,
             temperature=request.temperature,
             top_k=request.top_k,