Update app.py

app.py

@@ -1,9 +1,9 @@
 import gradio as gr
 from transformers import pipeline
 
-# Load models
+# Load models (optimized for performance)
 code_analyzer = pipeline("text-classification", model="microsoft/codebert-base")
-nlp_model = pipeline("text2text-generation", model="google/flan-t5-base")
+nlp_model = pipeline("text2text-generation", model="google/flan-t5-small")
 
 # Code Review Function
 def analyze_code(code):
@@ -12,17 +12,17 @@ def analyze_code(code):
     result = code_analyzer(code)
     return result[0]["label"], "Consider refactoring for better performance", "Medium"
 
-# Metadata Validator (Mock for now)
+# Metadata Validator (Mock)
 def validate_metadata(metadata):
     if not metadata.strip():
         return "No metadata provided.", "", ""
     return "Field", "Unused field detected", "Remove it to improve performance"
 
-# Natural Language Processor (AI-only, no default)
+# Natural Language Processor (faster)
 def process_nlp_query(query):
     if not query.strip():
         return "No query provided."
-    result = nlp_model(query, max_length=60, do_sample=False)
+    result = nlp_model(query, max_length=50, do_sample=False)
     return result[0]["generated_text"]
 
 # Gradio UI
@@ -46,7 +46,7 @@ with gr.Blocks() as demo:
         metadata_button.click(validate_metadata, inputs=metadata_input, outputs=[mtype, issue, recommendation])
 
     with gr.Tab("Ask AI (Natural Language)"):
-        query_input = gr.Textbox(label="Your question", lines=2, placeholder="e.g. What is a governor limit in Apex?")
+        query_input = gr.Textbox(label="Your question", lines=2, placeholder="e.g. How to optimize SOQL?")
         response_output = gr.Textbox(label="AI Response")
         nlp_button = gr.Button("Ask")
         nlp_button.click(process_nlp_query, inputs=query_input, outputs=response_output)