Update app.py

app.py

@@ -11,7 +11,7 @@ sf = Salesforce(
     domain="login"
 )
 
-# ---------- Load Pipelines ----------
+# ---------- Load HuggingFace Pipelines ----------
 code_analyzer = pipeline("text-classification", model="microsoft/codebert-base")
 qa_pipeline = pipeline("text2text-generation", model="google/flan-t5-xl")
 
@@ -23,7 +23,6 @@ def analyze_code(code):
     result = code_analyzer(code)
     label = result[0]["label"].strip()
 
-    # Strict label-to-picklist mapping
     label_to_issue_type = {
         "LABEL_0": "Performance",
         "LABEL_1": "Error",
@@ -38,6 +37,9 @@ def analyze_code(code):
     suggestion = "Consider refactoring for better performance"
     severity = "Medium"
 
+    # ✅ FIX: Replace Apex-style UserInfo with hardcoded or environment-driven value
+    developer_id = os.getenv("DEVELOPER_ID", "005XXXXXXXXXXXXXXX")  # <-- Replace with actual User Id
+
     try:
         sf.CodeReviewResult__c.create({
             "Name": f"Review_{issue_type}",
@@ -45,14 +47,14 @@ def analyze_code(code):
             "IssueType__c": issue_type,
             "Suggestion__c": suggestion,
             "Severity__c": severity,
-            "Developer__c": UserInfo.getUserId()  # Apex equivalent only, remove if not applicable
+            "Developer__c": developer_id
         })
     except Exception as e:
         suggestion += f" (⚠️ Failed to log to Salesforce: {str(e)})"
 
     return issue_type, suggestion, severity
 
-# ---------- Metadata Validation ----------
+# ---------- Metadata Validator ----------
 def validate_metadata(metadata):
     mtype = "Field"
     issue = "Unused field detected"
@@ -71,7 +73,7 @@ def validate_metadata(metadata):
 
     return mtype, issue, recommendation
 
-# ---------- AI Natural Language Assistant ----------
+# ---------- Natural Language AI Assistant ----------
 def process_nlp_query(query):
     try:
         query = query.strip()
@@ -79,9 +81,9 @@ def process_nlp_query(query):
             return "Please enter a valid Salesforce-related question."
 
         prompt = (
-            "You are a Salesforce-certified Apex expert. Answer the following question clearly and accurately. "
-            "Include Salesforce concepts such as governor limits, SOQL/DML best practices, trigger design, LWC, flows, "
-            "bulkification, etc. Provide examples and use bullet points if necessary.\n\n"
+            "You are a Salesforce-certified Apex developer. Answer the question clearly and completely. "
+            "Include governor limits, DML operations, SOQL queries, CPU limits, heap size, best practices, and real examples. "
+            "Be specific and educational.\n\n"
             f"Question: {query}\n\nAnswer:"
         )
 
@@ -123,7 +125,7 @@ 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. How many DML operations are allowed?")
+        query_input = gr.Textbox(label="Your question", lines=2, placeholder="e.g. What is a governor limit in Apex?")
         response_output = gr.Textbox(label="AI Response", lines=8)
         nlp_button = gr.Button("Ask")
         nlp_button.click(process_nlp_query, inputs=query_input, outputs=response_output)