Update app.py

app.py

@@ -1,9 +1,9 @@
 import gradio as gr
 from transformers import pipeline
 
-# Load models (optimized for performance)
+# Load models (optimized for performance and response quality)
 code_analyzer = pipeline("text-classification", model="microsoft/codebert-base")
-nlp_model = pipeline("text2text-generation", model="google/flan-t5-small")
+nlp_model = pipeline("text2text-generation", model="google/flan-t5-large")  # Upgraded
 
 # Code Review Function
 def analyze_code(code):
@@ -18,12 +18,15 @@ def validate_metadata(metadata):
         return "No metadata provided.", "", ""
     return "Field", "Unused field detected", "Remove it to improve performance"
 
-# Natural Language Processor (faster)
+# Natural Language Processor with better prompt formatting
 def process_nlp_query(query):
     if not query.strip():
         return "No query provided."
-    result = nlp_model(query, max_length=50, do_sample=False)
-    return result[0]["generated_text"]
+    
+    # Add prompt formatting to help the model generate better answers
+    prompt = f"Answer the following software development question in detail:\n{query}"
+    result = nlp_model(prompt, max_length=150, do_sample=False)
+    return result[0]["generated_text"].strip()
 
 # Gradio UI
 with gr.Blocks() as demo:
@@ -47,7 +50,7 @@ with gr.Blocks() as demo:
 
     with gr.Tab("Ask AI (Natural Language)"):
         query_input = gr.Textbox(label="Your question", lines=2, placeholder="e.g. How to optimize SOQL?")
-        response_output = gr.Textbox(label="AI Response")
+        response_output = gr.Textbox(label="AI Response", lines=4)
         nlp_button = gr.Button("Ask")
         nlp_button.click(process_nlp_query, inputs=query_input, outputs=response_output)