Update app.py

app.py

@@ -5,7 +5,7 @@ from datetime import datetime
 from transformers import pipeline
 from simple_salesforce import Salesforce, SalesforceLogin
 from dotenv import load_dotenv
-import xml.etree.ElementTree as ET  # NEW: for parsing metadata
+import xml.etree.ElementTree as ET
 
 # ---------- Load Environment Variables ----------
 load_dotenv()
@@ -35,7 +35,7 @@ severities = {
     "Best Practice": "Low"
 }
 
-# ---------- Knowledge Base ----------
+# ---------- Mock Knowledge Base ----------
 salesforce_knowledge_base = {
     "governor limits soql": "In Salesforce, the governor limit for SOQL queries is 100 per synchronous transaction and 200 per asynchronous transaction.",
     "governor limits dml": "The governor limit for DML statements is 150 per transaction.",
@@ -104,7 +104,7 @@ def analyze_code(code):
 
     return issue_type, suggestion, severity
 
-# ---------- Metadata Validator (Updated with dynamic XML parsing) ----------
+# ---------- Metadata Validator ----------
 def validate_metadata(metadata, admin_id=None):
     if not metadata.strip():
         return "No metadata provided.", "", ""
@@ -115,7 +115,6 @@ def validate_metadata(metadata, admin_id=None):
 
     try:
         root = ET.fromstring(metadata)
-        # Detect missing <description> tag
         description_found = any(elem.tag.endswith('description') for elem in root)
 
         if not description_found:
@@ -124,7 +123,6 @@ def validate_metadata(metadata, admin_id=None):
         else:
             issue = "Unused field detected"
             recommendation = "Remove it to improve performance or document its purpose."
-
     except Exception as e:
         issue = "Invalid XML"
         recommendation = f"Could not parse metadata XML. Error: {str(e)}"
@@ -156,7 +154,7 @@ def validate_metadata(metadata, admin_id=None):
 
     return mtype, issue, recommendation
 
-# ---------- Salesforce Chatbot ----------
+# ---------- Salesforce Chatbot (Updated Prompt) ----------
 conversation_history = []
 
 def salesforce_chatbot(query, history=[]):
@@ -166,11 +164,11 @@ def salesforce_chatbot(query, history=[]):
 
     salesforce_keywords = [
         "apex", "soql", "trigger", "lwc", "aura", "visualforce", "salesforce", "governor limits",
-        "dml", "metadata", "batch apex", "queueable", "future method", "api", "sfdc"
+        "dml", "metadata", "batch apex", "queueable", "future method", "api", "sfdc", "heap", "limits"
     ]
 
     if not any(keyword.lower() in query.lower() for keyword in salesforce_keywords):
-        return "Please ask a Salesforce-related question."
+        return "Please ask a Salesforce-related question (e.g., Apex, SOQL, LWC, limits, etc)."
 
     query_key = query.lower().strip()
     for kb_key, kb_answer in salesforce_knowledge_base.items():
@@ -181,23 +179,34 @@ def salesforce_chatbot(query, history=[]):
             return kb_answer
 
     history_summary = "\n".join([f"User: {q}\nAssistant: {a}" for q, a in conversation_history[-4:]])
+
     prompt = f"""
-You are an expert Salesforce developer...
+You are an expert Salesforce developer and certified architect. You provide 100% accurate, clear, and practical answers for topics like Apex, SOQL, LWC, governor limits, triggers, metadata, and more.
+
+When answering:
+- ALWAYS give at least 2 lines of explanation.
+- Be clear, concise, and technically correct.
+- Mention official Salesforce limits if applicable.
+- Use bullet points or code snippets when helpful.
+- Avoid speculation — if unknown, say so and suggest Trailhead or official docs.
+- Examples must be realistic and follow best practices.
 
 Conversation History:
 {history_summary}
 
-Question: {query.strip()}
-Answer:
+User: {query.strip()}
+Assistant:
 """
 
     try:
         result = qa_pipeline(prompt, max_new_tokens=1024, do_sample=False, temperature=0.1, top_k=50)
         output = result[0]["generated_text"].strip()
-        if output.startswith("Answer:"):
-            output = output[7:].strip()
+        if output.startswith("Assistant:"):
+            output = output.replace("Assistant:", "").strip()
+
         if len(output) < 20:
-            output = f"I'm sorry, I couldn't find a precise answer for '{query}'. Please refer to Salesforce docs."
+            output = f"I'm sorry, I couldn't generate a detailed answer for '{query}'. You can also check https://developer.salesforce.com/docs."
+
         conversation_history.append((query, output))
         conversation_history = conversation_history[-6:]
         log_to_console({"Question": query, "Answer": output}, "Chatbot Query")
@@ -227,7 +236,7 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
 
     with gr.Tab("Salesforce Chatbot"):
         chatbot_output = gr.Chatbot(label="Conversation History", height=400)
-        query_input = gr.Textbox(label="Your Question", placeholder="e.g., How do I bulkify an Apex trigger?")
+        query_input = gr.Textbox(label="Your Question", placeholder="e.g., What is heap size in Apex?")
         with gr.Row():
             chatbot_button = gr.Button("Ask")
             clear_button = gr.Button("Clear Chat")