Update app.py

app.py

@@ -19,7 +19,7 @@ KPI_FLAG_DEFAULT = os.getenv('KPI_FLAG', 'True') == 'True'
 ENGAGEMENT_SCORE_DEFAULT = float(os.getenv('ENGAGEMENT_SCORE', '85.0'))
 
 # Load model and tokenizer (Updated to use distilgpt2)
-model_name = "distilgpt2"  # Using distilgpt2
+model_name = "distilgpt2"  # Using distilgpt2 for faster response
 tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
 model = AutoModelForCausalLM.from_pretrained(model_name, low_cpu_mem_usage=True)
 
@@ -145,14 +145,12 @@ def save_to_salesforce(role, project_id, milestones, reflection, checklist, sugg
 # Fallback generation for checklist and suggestions
 def generate_fallback_checklist(role, milestones):
     checklist_items = []
-    # Base checklist on role
     if role.lower() == "safety officer":
         checklist_items.append("- Conduct morning safety briefing")
         checklist_items.append("- Review incident reports from previous day")
     
-    # If milestones are provided, add them
-    if milestones and milestones.strip() and milestones != "A milestone is a numbered marker placed on a route such as a road, railway line, canal or boundary.":
-        kpis = [kpi.strip() for kpi in milestones.split(",")]  # Fixed syntax error here
+    if milestones and milestones.strip():
+        kpis = [kpi.strip() for kpi in milestones.split(",")]
         for kpi in kpis:
             checklist_items.append(f"- Ensure progress on {kpi}")
     else:
@@ -162,11 +160,10 @@ def generate_fallback_checklist(role, milestones):
 
 def generate_fallback_suggestions(reflection):
     suggestions_items = []
-    # Base suggestions on reflection log keywords
     reflection_lower = reflection.lower()
     if "student" in reflection_lower or "learning" in reflection_lower:
         suggestions_items.append("- Ensure students are logging incidents consistently")
-        suggestions_items.append("- Provide guidance on timely incident recording")  # Fixed typo "Provide  Provide"
+        suggestions_items.append("- Provide guidance on timely incident recording")
     if "incident" in reflection_lower:
         suggestions_items.append("- Follow up on reported incidents with corrective actions")
     
@@ -193,7 +190,7 @@ def generate_outputs(role, supervisor_name, project_id, milestones, reflection):
         with torch.no_grad():
             outputs = model.generate(
                 inputs['input_ids'],
-                max_new_tokens=100,
+                max_new_tokens=100,  # Limit output length to avoid delays
                 no_repeat_ngram_size=2,
                 do_sample=True,
                 top_p=0.9,
@@ -203,7 +200,6 @@ def generate_outputs(role, supervisor_name, project_id, milestones, reflection):
         result = tokenizer.decode(outputs[0], skip_special_tokens=True)
     except Exception as e:
         print(f"⚠️ Generation error: {e}")
-        # Use fallback if generation fails
         checklist = generate_fallback_checklist(role, milestones)
         suggestions = generate_fallback_suggestions(reflection)
         save_to_salesforce(role, project_id, milestones, reflection, checklist, suggestions, supervisor_name)
@@ -217,7 +213,6 @@ def generate_outputs(role, supervisor_name, project_id, milestones, reflection):
     checklist = extract_between(result, "Checklist:\n", "Suggestions:")
     suggestions = extract_between(result, "Suggestions:\n", None)
 
-    # Use fallback if checklist or suggestions are empty
     if not checklist.strip():
         checklist = generate_fallback_checklist(role, milestones)
     if not suggestions.strip():
@@ -274,4 +269,3 @@ def create_interface():
 if __name__ == "__main__":
     app = create_interface()
     app.launch()
-