Update app.py

app.py

@@ -1,21 +1,12 @@
 import gradio as gr
-import json
 from transformers import pipeline
 
 # Load Hugging Face models
 code_analyzer = pipeline("text-classification", model="microsoft/codebert-base")
 nlp_model = pipeline("text2text-generation", model="google/flan-t5-large")
 
-# Load FAQ fallback (if available)
-try:
-    with open("faq.json", "r") as f:
-        faq_fallbacks = json.load(f)
-except FileNotFoundError:
-    faq_fallbacks = {}
-    print("⚠️ faq.json not found. AI will be used for all queries.")
-
 # --------------------------
-# Code Review Endpoint Logic
+# Code Review Function
 # --------------------------
 def analyze_code(code):
     if not code.strip():
@@ -24,27 +15,22 @@ def analyze_code(code):
     return result[0]["label"], "Consider refactoring for better performance", "Medium"
 
 # --------------------------
-# Metadata Validation Logic
+# Metadata Validator (Mock)
 # --------------------------
 def validate_metadata(metadata):
     if not metadata.strip():
         return "No metadata provided.", "", ""
-    # Example: Simulate unused field
     return "Field", "Unused field detected", "Remove it to improve performance"
 
 # --------------------------
-# Ask AI with Fallback Logic
+# AI Natural Language Q&A (No Fallback)
 # --------------------------
 def process_nlp_query(query):
     if not query.strip():
         return "No query provided."
 
-    normalized = query.lower().strip()
-    if normalized in faq_fallbacks:
-        return faq_fallbacks[normalized]
-
     prompt = f"""
-    You are a Salesforce and Apex expert. Provide a clear, accurate answer to the following question:\n\n{query}\n\nAnswer:
+    You are a Salesforce and Apex expert. Provide a clear and technically accurate answer to the following question:\n\n{query}\n\nAnswer:
     """
     result = nlp_model(
         prompt,
@@ -57,10 +43,11 @@ def process_nlp_query(query):
     )
 
     output = result[0]["generated_text"]
+
     if "Answer:" in output:
         output = output.split("Answer:")[-1]
 
-    # Remove duplicate lines
+    # Clean response (remove duplicates, spacing)
     lines = output.strip().splitlines()
     seen = set()
     unique_lines = [line.strip() for line in lines if line.strip() not in seen and not seen.add(line.strip())]
@@ -74,7 +61,7 @@ with gr.Blocks() as demo:
     gr.Markdown("# 🤖 Salesforce AI Code Review & Metadata Assistant")
 
     with gr.Tab("Code Review"):
-        code_input = gr.Textbox(label="Apex / LWC Code", lines=8, placeholder="Paste Apex or LWC code here")
+        code_input = gr.Textbox(label="Apex / LWC Code", lines=8)
         issue_type = gr.Textbox(label="Issue Type")
         suggestion = gr.Textbox(label="AI Suggestion")
         severity = gr.Textbox(label="Severity")
@@ -82,7 +69,7 @@ with gr.Blocks() as demo:
         code_button.click(analyze_code, inputs=code_input, outputs=[issue_type, suggestion, severity])
 
     with gr.Tab("Metadata Validation"):
-        metadata_input = gr.Textbox(label="Metadata XML", lines=8, placeholder="Paste Salesforce metadata here")
+        metadata_input = gr.Textbox(label="Metadata XML", lines=8)
         mtype = gr.Textbox(label="Type")
         issue = gr.Textbox(label="Issue")
         recommendation = gr.Textbox(label="Recommendation")