Update app.py

app.py

@@ -29,9 +29,8 @@ def analyze_code(code):
 
     try:
         result = code_analyzer(code)
-        label = result[0]["label"].strip()  # e.g., LABEL_0
-        issue_type = label_to_issue_type.get(label, "Error")  # fallback to valid picklist
-
+        label = result[0]["label"].strip()
+        issue_type = label_to_issue_type.get(label, "Error")
         suggestion = "Consider refactoring for better performance"
         severity = "Medium"
 
@@ -81,18 +80,22 @@ def process_nlp_query(query):
         return "No question provided."
 
     prompt = (
-        "You are a certified Salesforce Apex expert. Answer the question below accurately and clearly. "
-        "Include governor limits, code examples, best practices, and terminology when appropriate. Do not repeat the question. "
-        "Give only a factual answer.\n\n"
+        "You are a certified Salesforce Apex expert. "
+        "Answer the question below clearly and accurately using Salesforce best practices, terminology, and examples.\n\n"
         f"Question: {query.strip()}\n\nAnswer:"
     )
 
     try:
-        result = qa_pipeline(prompt, max_length=512, temperature=0.3, top_p=0.9, repetition_penalty=1.1, do_sample=False)
+        result = qa_pipeline(
+            prompt,
+            max_new_tokens=256  # ✅ Fixed warning: avoids conflict with max_length
+        )
+
         output = result[0]["generated_text"]
         return output.split("Answer:")[-1].strip() if "Answer:" in output else output.strip()
+
     except Exception as e:
-        return f"⚠️ AI failed to answer: {str(e)}"
+        return f"⚠️ AI failed to respond: {str(e)}"
 
 # ---------- Gradio UI ----------
 with gr.Blocks() as demo: