Dev merge

.github/workflows/azfunctionapp_cicd.yml

@@ -12,12 +12,12 @@ on:
       GIT_FunctionApp_Name:
         description: 'Provide the Function App Name'
         required: true
-        default: 'functionappsrs'
+        default: 'bicep-funcgdekhfv'
 
       GIT_FunctionApp_URL:
         description: 'Provide the Function App URL'
         required: true
-        default: 'https://functionappsrs.azurewebsites.net'
+        default: 'https://bicep-funcgdekhfv.azurewebsites.net'
 
        # This is the path of your Function app in Git.
       GIT_FunctionApp_PATH:

FunctionApp/DemoSample/.funcignore

@@ -1,5 +1,8 @@
-.git*
-.vscode
-local.settings.json
-test
+.git*
+.vscode
+__azurite_db*__.json
+__blobstorage__
+__queuestorage__
+local.settings.json
+test
 .venv

FunctionApp/DemoSample/.gitignore

@@ -131,5 +131,5 @@ local.settings.json
 # Azurite artifacts
 __blobstorage__
 __queuestorage__
-__azurite_db*__.json
+__azurite_db*__.json
 .python_packages

FunctionApp/DemoSample/HttpTrigger1/__init__.py

@@ -1,24 +1,134 @@
-import logging
-
-import azure.functions as func
-
-
-def main(req: func.HttpRequest) -> func.HttpResponse:
-    logging.info('Python HTTP trigger function processed a request.')
-
-    name = req.params.get('name')
-    if not name:
-        try:
-            req_body = req.get_json()
-        except ValueError:
-            pass
-        else:
-            name = req_body.get('name')
-
-    if name:
-        return func.HttpResponse(f"Hello, {name}. This HTTP triggered function executed successfully.")
-    else:
-        return func.HttpResponse(
-             "This HTTP triggered function executed successfully. Pass a name in the query string or in the request body for a personalized response.Hello i am Sumit",
-             status_code=200
-        )
+import logging
+import azure.functions as func
+from openai import AzureOpenAI
+import os
+from azure.ai.vision.imageanalysis import ImageAnalysisClient
+from azure.ai.vision.imageanalysis.models import VisualFeatures
+from azure.core.credentials import AzureKeyCredential
+import re
+import json
+# from langchain.callbacks import get_openai_callback
+
+def preprocess_text(text):
+    return re.sub(r'\W+', ' ', text.lower())
+
+def DocProcessor(image_url):
+    endpoint = "https://test-ocr-puc.cognitiveservices.azure.com/"
+    key = "d20dff6d7c094caeb6f86f7f7c126005"
+
+    client = ImageAnalysisClient(
+        endpoint=endpoint,
+        credential=AzureKeyCredential(key)
+    )
+
+    visual_features = [
+        VisualFeatures.TAGS, VisualFeatures.OBJECTS, VisualFeatures.CAPTION,
+        VisualFeatures.DENSE_CAPTIONS, VisualFeatures.READ, VisualFeatures.SMART_CROPS, VisualFeatures.PEOPLE,
+    ]
+
+    result = client.analyze_from_url(
+        image_url=image_url,
+        visual_features=visual_features,
+        smart_crops_aspect_ratios=[0.9, 1.33],
+        gender_neutral_caption=True,
+        language="en"
+    )
+
+    descriptions_to_match = []
+
+    if result.caption is not None:
+        main_caption = result.caption.text
+        descriptions_to_match.append(main_caption)
+
+    if result.dense_captions is not None:
+        for caption in result.dense_captions.list:
+            descriptions_to_match.append(caption.text)
+
+    client = AzureOpenAI(
+        azure_endpoint="https://aisqlintegration.openai.azure.com/",
+        api_key="d912d0d7acee490ab6e6adc797abe8ff",
+        api_version="2024-02-15-preview"
+    )
+
+    # Predefined list of categories
+    categories = [
+        "Abandoned Vehicle", "Accessibility", "Animal - Deceased", "Animal - Domestic", "Animal - General",
+        "Damaged Road", "Damaged Street Sign", "Dumped Rubbish", "Dumped Tyres", "Facility - General Request",
+        "Fallen Tree", "General - Abandoned Trolley", "General Request", "Graffiti - General", "Graffiti - Public Property",
+        "Graffiti - Signage", "Illegal Parking", "Litter", "Noise - Animal", "Noise - Construction", "Noise - General",
+        "Overgrown Vegetation", "Park - General Request", "Parking - Disabled", "Pavement - Damaged", "Pavement - General",
+        "Pest / Vermin", "Pit and Equipment - General", "Playground Equipment", "Poles and Signage - General",
+        "Pollution - General", "Pothole", "Public Toilet", "Request Bin Repair or Replacement", "Road Blockage",
+        "Road Signage", "Roads - General", "Rubbish and Bins - General", "Street Cleaning", "Street Gutters / Storm Water",
+        "Trees - General", "Vandalism - General", "Water Fountain"
+    ]
+
+    prompt_messages = []
+
+    prompt_messages.append({"role": "system", "content": "You are an AI assistant. Only suggest categories from the following list based on the description:"})
+
+    prompt_messages.append({"role": "user", "content": main_caption})
+
+    for description in descriptions_to_match:
+        prompt_messages.append({"role": "user", "content": description})
+
+    # for category in categories:
+    prompt_messages.append({"role": "system", "content": f"Suggest one best suitable category strictly from the following list only based on the description: {categories}"})
+
+    # for category in categories:
+    #     prompt_messages.append({"role": "system", "content": f"Based on MainCaption, DenseCaptions identify best suggested category Type using Enhanced Keyword Extraction, Contextual Analysis and Named Entity Recognition (NER)techniques.Provide suggested category strictly from the category only don't fabricate category on your own.: {category} . Provide answer only strictly."})
+
+
+    completion = client.chat.completions.create(
+        model="sqlintegration",
+        messages=prompt_messages,
+        temperature=0,
+        max_tokens=1000,
+        top_p=0,
+        frequency_penalty=0,
+        presence_penalty=0,
+        stop=None
+    )
+
+    suggested_category = completion.choices[0].message.content
+
+    # result_with_source = {
+    #     'main_caption': main_caption,
+    #     'dense_captions': list(descriptions_to_match),
+    #     'suggested_category': suggested_category
+    # }
+    # json_data = json.dumps(result_with_source)
+    # return json_data
+    input_cost=0.00003*completion.usage.prompt_tokens
+    output_cost=0.00006*completion.usage.completion_tokens
+    price= input_cost+output_cost
+    result_with_source = {
+        'main_caption': main_caption,
+        'dense_captions': list(descriptions_to_match),
+        'suggested_category': suggested_category,
+        'completion_token':completion.usage.completion_tokens,
+        'total_token':completion.usage.total_tokens,
+        'prompt_token': completion.usage.prompt_tokens,
+        'total_cost': round(price,4)
+    }
+    json_data = json.dumps(result_with_source)
+    return json_data
+def main(req: func.HttpRequest) -> func.HttpResponse:
+    logging.info('Python HTTP trigger function processed a request.')
+
+    image_url = req.params.get('imageurl')
+    if not image_url:
+        try:
+            req_body = req.get_json()
+        except ValueError:
+            pass
+        else:
+            image_url = req_body.get('imageurl')
+
+    if image_url:
+        return DocProcessor(image_url)
+    else:
+        return func.HttpResponse(
+            "Please provide an image URL in the query string or in the request body.",
+            status_code=400
+        )

FunctionApp/DemoSample/HttpTrigger1/function.json

@@ -2,7 +2,7 @@
   "scriptFile": "__init__.py",
   "bindings": [
     {
-      "authLevel": "function",
+      "authLevel": "anonymous",
       "type": "httpTrigger",
       "direction": "in",
       "name": "req",
@@ -17,4 +17,4 @@
       "name": "$return"
     }
   ]
-}
+}

FunctionApp/DemoSample/HttpTrigger1/sample.dat

@@ -1,3 +1,3 @@
-{
-    "name": "Azure"
+{
+    "name": "Azure"
 }

FunctionApp/DemoSample/host.json

@@ -10,6 +10,6 @@
   },
   "extensionBundle": {
     "id": "Microsoft.Azure.Functions.ExtensionBundle",
-    "version": "[2.*, 3.0.0)"
+    "version": "[4.*, 5.0.0)"
   }
-}
+}

FunctionApp/DemoSample/requirements.txt

@@ -3,3 +3,7 @@
 # Manually managing azure-functions-worker may cause unexpected issues
 
 azure-functions
+azure-cognitiveservices-vision-computervision
+azure-ai-vision-imageanalysis
+openai
+langchain