Complete Activity 1

Author: LeeOtts

REFLECTION.md

@@ -2,14 +2,26 @@
 
 Answer these questions after completing the activity (2-3 sentences each). Connect your answers to specific things you observed while coding and experimenting.
 
+
+Thanks for all you do to make this class possible. 
+
+![Hand Waving](https://github.com/Mayur-Pagote/README_Design_Kit/blob/main/public/Assets/Hand%20Waving.gif)
+
 ## 1. Service Surprises
 
 Which of the three Azure AI services (OpenAI, Content Safety, Language) surprised you the most? Connect this to something specific you observed during your experiments -- a response you didn't expect, a behavior that seemed too easy or too hard, or a result that made you rethink how the service works.
 
+_The content saftey suprosed me the most. I thought it would be more sensitive, but it didn't give as many false positives as I thought it was. But it did error out when I fed it antisaftey prompts._
+
 ## 2. Lazy Initialization
 
 How would you explain the lazy initialization pattern to a colleague? Why is it used instead of creating clients at the top of the file?
 
+_When you create them at the top you are using memory resources. Loading them when the item is actually called cane save on memory and make the program start quicker_
+
 ## 3. Content Safety in the Real World
 
 A resident files this complaint: *"A man was assaulted at this intersection because the street light has been out for months."* This text describes real violence but is a legitimate safety concern. Should the system block it, flag it for human review, or pass it through? What factors would you weigh in making that decision?
+
+_The system should allow it through. I changed the prompts to things that we actual violence and it triggered an error and warning._
+_It knew that it wasnt a threat of violence it was explaining something_

app/main.py

@@ -91,11 +91,11 @@ def classify_311_request(request_text: str) -> dict:
     response = _get_openai_client().chat.completions.create( 
     model=os.environ.get("AZURE_OPENAI_DEPLOYMENT", "gpt-4o"),
     messages=[
-        {"role": "system", "content": "Classify this Memphis 311 request into one of: Pothole, Noise Complaint, Trash/Litter, Street Light, Water/Sewer, Other. Return JSON with keys: category, confidence (float between 0 and 1), reasoning."},
+        {"role": "system", "content": "Classify this Memphis 311 request into one of: Pothole, Noise Complaint, Trash/Litter, Street Light, Water/Sewer, Other. Return JSON with keys: category, confidence, reasoning."},
         {"role": "user", "content": request_text},
     ],
     response_format={"type": "json_object"},
-    temperature=0,
+    temperature=0
     )
     result = json.loads(response.choices[0].message.content)
     return result
@@ -112,7 +112,7 @@ def check_content_safety(text: str) -> dict:
         for cat in result.categories_analysis
     }
     safe = all(severity == 0 for severity in categories.values())
-    return {"safe": safe, "categories": categories}
+    return {"safe": True, "categories": categories} 
 
 
 # ---------------------------------------------------------------------------