added changes for new api for entity and topic classification

pebblo/app/api/v1.py

@@ -0,0 +1,21 @@
+from fastapi import APIRouter
+
+from pebblo.app.config.config import var_server_config_dict
+from pebblo.app.service.classification import Classification
+
+config_details = var_server_config_dict.get()
+
+
+class APIv1:
+    """
+    Controller Class for all the api endpoints for App resource.
+    """
+
+    def __init__(self, prefix: str):
+        self.router = APIRouter(prefix=prefix)
+
+    @staticmethod
+    def classify_data(data: dict):
+        cls_obj = Classification(data)
+        response = cls_obj.process_request()
+        return response

pebblo/app/config/service.py

@@ -15,7 +15,7 @@
 from pebblo.app.routers.redirection_router import redirect_router_instance
 
 with redirect_stdout(StringIO()), redirect_stderr(StringIO()):
-    from pebblo.app.routers.routers import router_instance
+    from pebblo.app.routers.routers import api_v1_router_instance, router_instance
 from pebblo.log import get_logger, get_uvicorn_logconfig
 
 logger = get_logger(__name__)
@@ -42,6 +42,7 @@ def __init__(self, config_details):
         self.app = FastAPI(exception_handlers=exception_handlers)
         # Register the router instance with the main app
         self.app.include_router(router_instance.router)
+        self.app.include_router(api_v1_router_instance.router)
         self.app.include_router(local_ui_router_instance.router)
         self.app.include_router(redirect_router_instance.router)
         # Adding cors

pebblo/app/models/models.py

@@ -22,10 +22,10 @@ class LoaderMetadata(BaseModel):
 
 class AiDataModel(BaseModel):
     data: Optional[Union[list, str]] = None
-    entityCount: int
+    entityCount: Optional[int] = 0
     entities: dict
     entityDetails: Optional[dict] = {}
-    topicCount: Optional[int] = None
+    topicCount: Optional[int] = 0
     topics: Optional[dict] = {}
     topicDetails: Optional[dict] = {}
 

pebblo/app/routers/routers.py

@@ -3,9 +3,11 @@
 """
 
 from pebblo.app.api.api import App
+from pebblo.app.api.v1 import APIv1
 
 # Create an instance of APp with a specific prefix
 router_instance = App(prefix="/v1")
+api_v1_router_instance = APIv1(prefix="/api/v1")
 
 # Add routes to the class-based router
 router_instance.router.add_api_route(
@@ -32,3 +34,11 @@
     response_model=dict,
     response_model_exclude_none=True,
 )
+
+api_v1_router_instance.router.add_api_route(
+    "/classify",
+    APIv1.classify_data,
+    methods=["POST"],
+    response_model=dict,
+    response_model_exclude_none=True,
+)

pebblo/app/service/classification.py

@@ -0,0 +1,134 @@
+"""
+Module for text classification
+"""
+
+import traceback
+from enum import Enum
+from typing import Optional
+
+from pydantic import BaseModel, Field, ValidationError
+
+from pebblo.app.libs.responses import PebbloJsonResponse
+from pebblo.app.models.models import AiDataModel
+from pebblo.entity_classifier.entity_classifier import EntityClassifier
+from pebblo.log import get_logger
+from pebblo.topic_classifier.topic_classifier import TopicClassifier
+
+
+class ClassificationMode(Enum):
+    ENTITY = "entity"
+    TOPIC = "topic"
+    ALL = "all"
+
+
+class ReqClassifier(BaseModel):
+    data: str
+    mode: Optional[ClassificationMode] = Field(default=ClassificationMode.ALL)
+    anonymize: Optional[bool] = Field(default=False)
+
+    class Config:
+        extra = "forbid"
+
+
+logger = get_logger(__name__)
+topic_classifier_obj = TopicClassifier()
+entity_classifier_obj = EntityClassifier()
+
+
+class Classification:
+    """
+    Classification wrapper class for Entity and Semantic classification with anonymization
+    """
+
+    def __init__(self, input: dict):
+        self.input = input
+
+    def _get_classifier_response(self, req: ReqClassifier):
+        """
+        Processes the input prompt through the entity classifier and anonymizer, and returns
+        the resulting information encapsulated in an AiDataModel object.
+
+        Returns:
+            AiDataModel: An object containing the anonymized document, entities, and their counts.
+        """
+        doc_info = AiDataModel(
+            data=None,
+            entities={},
+            entityCount=0,
+            entityDetails={},
+            topics={},
+            topicCount=0,
+            topicDetails={},
+        )
+        try:
+            # Process entity classification
+            if req.mode in [ClassificationMode.ENTITY, ClassificationMode.ALL]:
+                (
+                    entities,
+                    entity_count,
+                    anonymized_doc,
+                    entity_details,
+                ) = entity_classifier_obj.presidio_entity_classifier_and_anonymizer(
+                    req.data,
+                    anonymize_snippets=req.anonymize,
+                )
+                doc_info.entities = entities
+                doc_info.entityCount = entity_count
+                doc_info.entityDetails = entity_details
+                doc_info.data = anonymized_doc if req.anonymize else ""
+
+            # Process topic classification
+            if req.mode in [ClassificationMode.TOPIC, ClassificationMode.ALL]:
+                topics, topic_count, topic_details = topic_classifier_obj.predict(
+                    req.data
+                )
+                doc_info.topics = topics
+                doc_info.topicCount = topic_count
+                doc_info.topicDetails = topic_details
+            return doc_info
+        except (KeyError, ValueError, RuntimeError) as e:
+            logger.error(f"Failed to get classifier response: {e}")
+            return doc_info
+        except Exception as e:
+            logger.error(f"Unexpected error:{e}\n{traceback.format_exc()}")
+            return doc_info
+
+    def process_request(self):
+        """
+        Processes the user request for classification and returns a structured response.
+
+        Returns:
+            PebbloJsonResponse: The response object containing classification results or error details.
+        """
+        try:
+            req = ReqClassifier.model_validate(self.input)
+            if not req.data:
+                return PebbloJsonResponse.build(
+                    body={"error": "Input data is missing"}, status_code=400
+                )
+            doc_info = self._get_classifier_response(req)
+            return PebbloJsonResponse.build(
+                body=doc_info.model_dump(exclude_none=True), status_code=200
+            )
+        except ValidationError as e:
+            logger.error(
+                f"Validation error in Classification API process_request:{e}\n{traceback.format_exc()}"
+            )
+            return PebbloJsonResponse.build(
+                body={"error": f"Validation error: {e}"}, status_code=400
+            )
+        except Exception:
+            response = AiDataModel(
+                data=None,
+                entities={},
+                entityCount=0,
+                topics={},
+                topicCount=0,
+                topicDetails={},
+            )
+            logger.error(
+                f"Error in Classification API process_request: {traceback.format_exc()}"
+            )
+            return PebbloJsonResponse.build(
+                body=response.model_dump(exclude_none=True), status_code=500
+            )

tests/app/service/test_classification.py

@@ -0,0 +1,68 @@
+# test_prompt_gov.py
+from unittest.mock import patch
+
+import pytest
+
+from pebblo.app.libs.responses import PebbloJsonResponse
+from pebblo.app.models.models import AiDataModel
+from pebblo.app.service.classification import Classification
+
+
+@pytest.fixture
+def mock_entity_classifier():
+    with patch("pebblo.app.service.classification.EntityClassifier") as mock:
+        yield mock
+
+
+@pytest.fixture
+def mock_topic_classifier():
+    with patch("pebblo.app.service.classification.TopicClassifier") as mock:
+        yield mock
+
+
+def test_process_request_success(mock_entity_classifier, mock_topic_classifier):
+    mock_entity_classifier_instance = mock_entity_classifier.return_value
+    mock_entity_classifier_instance.presidio_entity_classifier_and_anonymizer.return_value = (
+        {"us-ssn": 1},
+        1,
+        "anonymized document",
+        {
+            "us-ssn": [
+                {
+                    "location": "16_27",
+                    "confidence_score": "HIGH",
+                    "entity_group": "pii-identification",
+                }
+            ]
+        },
+    )
+
+    mock_topic_classifier_instance = mock_topic_classifier.return_value
+    mock_topic_classifier_instance.predict.return_value = ({}, 0, {})
+
+    data = {"data": "Sachin's SSN is 222-85-4836"}
+    cls_obj = Classification(data)
+    response = cls_obj.process_request()
+    expected_response = AiDataModel(
+        data="",
+        entities={"us-ssn": 1},
+        entityCount=1,
+        entityDetails={
+            "us-ssn": [
+                {
+                    "location": "16_27",
+                    "confidence_score": "HIGH",
+                    "entity_group": "pii-identification",
+                }
+            ]
+        },
+        topics={},
+        topicCount=0,
+        topicDetails={},
+    )
+    expected_response = PebbloJsonResponse.build(
+        body=expected_response.model_dump(exclude_none=True), status_code=200
+    )
+
+    assert response.status_code == expected_response.status_code
+    assert response.body == expected_response.body