Update app.py

app.py

@@ -27,29 +27,30 @@ def analyze_code(code):
     if not code.strip():
         return "No code provided.", "", ""
 
-    try:
-        result = code_analyzer(code)
-        label = result[0]["label"].strip()  # e.g., LABEL_0
-        issue_type = label_to_issue_type.get(label, "Error")  # fallback to valid picklist
-
-        suggestion = "Consider refactoring for better performance"
-        severity = "Medium"
-
-        try:
-            sf.CodeReviewResult__c.create({
-                "Name": f"Review_{issue_type}",
-                "CodeSnippet__c": code,
-                "IssueType__c": issue_type,
-                "Suggestion__c": suggestion,
-                "Severity__c": severity,
-            })
-        except Exception as sf_err:
-            suggestion += f" (⚠️ Failed to log to Salesforce: {str(sf_err)})"
-
-        return issue_type, suggestion, severity
+    result = code_analyzer(code)
+    label = result[0]["label"]  # e.g., LABEL_0
+    score = result[0]["score"]
+
+    # Map raw label to issue type (no lower(), no mismatch)
+    issue_type = label_to_issue_type.get(label.strip(), "Error")
+
+    # Static suggestion and severity for now
+    suggestion = "Consider refactoring for better performance"
+    severity = "Medium"
 
+    try:
+        sf.CodeReviewResult__c.create({
+            "Name": f"Review_{issue_type}",
+            "CodeSnippet__c": code,
+            "IssueType__c": issue_type,  # Must match picklist
+            "Suggestion__c": suggestion,
+            "Severity__c": severity,
+            # Optional: add Developer__c if needed
+        })
     except Exception as e:
-        return "Error", f"⚠️ Model failed: {str(e)}", "High"
+        suggestion += f" (⚠️ Failed to log to Salesforce: {str(e)})"
+
+    return issue_type, suggestion, severity
 
 # ---------- Metadata Validator ----------
 def validate_metadata(metadata):
@@ -87,12 +88,12 @@ def process_nlp_query(query):
         f"Question: {query.strip()}\n\nAnswer:"
     )
 
-    try:
-        result = qa_pipeline(prompt, max_length=512, temperature=0.3, top_p=0.9, repetition_penalty=1.1, do_sample=False)
-        output = result[0]["generated_text"]
-        return output.split("Answer:")[-1].strip() if "Answer:" in output else output.strip()
-    except Exception as e:
-        return f"⚠️ AI failed to answer: {str(e)}"
+    result = qa_pipeline(prompt, max_length=512, temperature=0.3, top_p=0.9, repetition_penalty=1.1, do_sample=False)
+    output = result[0]["generated_text"]
+
+    if "Answer:" in output:
+        return output.split("Answer:")[-1].strip()
+    return output.strip()
 
 # ---------- Gradio UI ----------
 with gr.Blocks() as demo: