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README.md

@@ -1,3 +1,4 @@
+[![Review Assignment Due Date](https://classroom.github.com/assets/deadline-readme-button-22041afd0340ce965d47ae6ef1cefeee28c7c493a6346c4f15d667ab976d596c.svg)](https://classroom.github.com/a/tLTGCA4G)
 ---
 title: "Activity 1 - Hello, Azure AI"
 type: lab

app/main.py

@@ -45,15 +45,14 @@ def _get_openai_client():
     global _openai_client
     if _openai_client is None:
         # TODO: Uncomment and configure
-        #   from openai import AzureOpenAI
-        #   _openai_client = AzureOpenAI(
-        #       azure_endpoint=os.environ["AZURE_OPENAI_ENDPOINT"],
-        #       api_key=os.environ["AZURE_OPENAI_API_KEY"],
-        #       api_version="2024-10-21",
-        #   )
-        raise NotImplementedError("Configure the Azure OpenAI client")
+        from openai import AzureOpenAI
+        _openai_client = AzureOpenAI(
+            azure_endpoint=os.environ["AZURE_OPENAI_ENDPOINT"],
+            api_key=os.environ["AZURE_OPENAI_API_KEY"],
+            api_version="2024-10-21",
+           )
     return _openai_client
-
+    
 
 def _get_content_safety_client():
     """Lazily initialize the Azure Content Safety client."""
@@ -62,13 +61,12 @@ def _get_content_safety_client():
         # NOTE: The Content Safety SDK handles API versioning internally --
         # no api_version parameter is needed (unlike the OpenAI SDK).
         # TODO: Uncomment and configure
-        #   from azure.ai.contentsafety import ContentSafetyClient
-        #   from azure.core.credentials import AzureKeyCredential
-        #   _content_safety_client = ContentSafetyClient(
-        #       endpoint=os.environ["AZURE_CONTENT_SAFETY_ENDPOINT"],
-        #       credential=AzureKeyCredential(os.environ["AZURE_CONTENT_SAFETY_KEY"]),
-        #   )
-        raise NotImplementedError("Configure the Content Safety client")
+           from azure.ai.contentsafety import ContentSafetyClient
+           from azure.core.credentials import AzureKeyCredential
+           _content_safety_client = ContentSafetyClient(
+               endpoint=os.environ["AZURE_CONTENT_SAFETY_ENDPOINT"],
+               credential=AzureKeyCredential(os.environ["AZURE_CONTENT_SAFETY_KEY"]),
+           )
     return _content_safety_client
 
 
@@ -79,13 +77,12 @@ def _get_language_client():
         # NOTE: The Language SDK handles API versioning internally --
         # no api_version parameter is needed (unlike the OpenAI SDK).
         # TODO: Uncomment and configure
-        #   from azure.ai.textanalytics import TextAnalyticsClient
-        #   from azure.core.credentials import AzureKeyCredential
-        #   _language_client = TextAnalyticsClient(
-        #       endpoint=os.environ["AZURE_AI_LANGUAGE_ENDPOINT"],
-        #       credential=AzureKeyCredential(os.environ["AZURE_AI_LANGUAGE_KEY"]),
-        #   )
-        raise NotImplementedError("Configure the AI Language client")
+        from azure.ai.textanalytics import TextAnalyticsClient
+        from azure.core.credentials import AzureKeyCredential
+        _language_client = TextAnalyticsClient(
+            endpoint=os.environ["AZURE_AI_LANGUAGE_ENDPOINT"],
+            credential=AzureKeyCredential(os.environ["AZURE_AI_LANGUAGE_KEY"]),
+        )
     return _language_client
 
 
@@ -102,14 +99,39 @@ def classify_311_request(request_text: str) -> dict:
         dict with keys: category, confidence, reasoning
     """
     # TODO: Step 1.1 - Get the OpenAI client
+    AiClient = _get_openai_client()
     # TODO: Step 1.2 - Call client.chat.completions.create() with:
-    #   model=os.environ.get("AZURE_OPENAI_DEPLOYMENT", "gpt-4o")
-    #   A system message that classifies into: Pothole, Noise Complaint,
-    #   Trash/Litter, Street Light, Water/Sewer, Other
-    #   response_format={"type": "json_object"}, temperature=0
+    X = AiClient.chat.completions.create(
+        model=os.environ.get("AZURE_OPENAI_DEPLOYMENT", "gpt-4o"),
+        #   A system message that classifies into: Pothole, Noise Complaint,
+        #   Trash/Litter, Street Light, Water/Sewer, Other
+        messages = [
+        {
+            "role": "system",
+            "content": (
+                "You are a complaint classification system. "
+                "Classify the user's complaint text into one of these six categories: "
+                "Pothole, Noise Complaint, Trash/Litter, Street Light, Water/Sewer, Other. "
+                "Return a response as a JSON object with three fields: "
+                "'category', 'confidence', and 'reasoning'."
+            ),
+        },
+            {
+            "role": "user",
+            "content": request_text,
+            },
+        ],
+        response_format={"type": "json_object"},
+        temperature=0,
+        )
+
     # TODO: Step 1.3 - Parse the JSON response with json.loads()
-    raise NotImplementedError("Implement classify_311_request in Step 1")
 
+    Y = json.loads(X.choices[0].message.content)
+
+    return Y
+
+    raise NotImplementedError("Implement classify_311_request in Step 1")
 
 # ---------------------------------------------------------------------------
 # TODO: Step 2 - Check content safety
@@ -124,10 +146,26 @@ def check_content_safety(text: str) -> dict:
         dict with keys: safe (bool), categories (dict of category: severity)
     """
     # TODO: Step 2.1 - Get the Content Safety client
+
+    Sclient = _get_content_safety_client()
+
     # TODO: Step 2.2 - Call client.analyze_text() with AnalyzeTextOptions
+
+    from azure.ai.contentsafety.models import AnalyzeTextOptions
+
+    Z = Sclient.analyze_text(AnalyzeTextOptions(text=text))
+
+
     # TODO: Step 2.3 - Return safety results
-    raise NotImplementedError("Implement check_content_safety in Step 2")
 
+    categories = {c.category: c.severity for c in Z.categories_analysis}
+    safe = all(severity == 0 for severity in categories.values())
+
+    # Return final structured response
+    return {
+            "safe": safe,
+            "categories": categories
+    }
 
 # ---------------------------------------------------------------------------
 # TODO: Step 3 - Extract key phrases
@@ -142,9 +180,23 @@ def extract_key_phrases(text: str) -> list[str]:
         List of key phrase strings.
     """
     # TODO: Step 3.1 - Get the Language client
+
+    LClient = _get_language_client()
+
     # TODO: Step 3.2 - Call client.extract_key_phrases([text])
+
+    response = LClient.extract_key_phrases([text])
+
     # TODO: Step 3.3 - Return the list of key phrases
-    raise NotImplementedError("Implement extract_key_phrases in Step 3")
+
+    key_phrases = [
+        phrase
+        for item in response
+        if not item.is_error
+        for phrase in item.key_phrases
+    ]
+
+    return key_phrases
 
 
 def main():