Implementing inference api

app.py

@@ -1,4 +1,5 @@
-from fastapi import FastAPI
+from fastapi import FastAPI, HTTPException, responses
+from pydantic import BaseModel
 
 from controller import stats
 from controller import integration
@@ -7,8 +8,6 @@
 # API creation and description
 app = FastAPI()
 
-from pydantic import BaseModel
-
 
 class WeakSupervisionRequest(BaseModel):
     project_id: str
@@ -29,6 +28,11 @@ class SourceStatsRequest(BaseModel):
     user_id: str
 
 
+class ExportWsStatsRequest(BaseModel):
+    project_id: str
+    labeling_task_id: str
+
+
 @app.post("/fit_predict")
 async def weakly_supervise(request: WeakSupervisionRequest) -> int:
     session_token = general.get_ctx_token()
@@ -43,7 +47,7 @@ async def weakly_supervise(request: WeakSupervisionRequest) -> int:
 
 
 @app.post("/labeling_task_statistics")
-async def calculate_stats(request: TaskStatsRequest):
+async def calculate_task_stats(request: TaskStatsRequest):
     session_token = general.get_ctx_token()
     stats.calculate_quality_statistics_for_labeling_task(
         request.project_id, request.labeling_task_id, request.user_id
@@ -53,7 +57,7 @@ async def calculate_stats(request: TaskStatsRequest):
 
 
 @app.post("/source_statistics")
-async def calculate_stats(request: SourceStatsRequest):
+async def calculate_source_stats(request: SourceStatsRequest):
     session_token = general.get_ctx_token()
     has_coverage = stats.calculate_quantity_statistics_for_labeling_task_from_source(
         request.project_id, request.source_id, request.user_id
@@ -64,3 +68,16 @@ async def calculate_stats(request: SourceStatsRequest):
         )
     general.remove_and_refresh_session(session_token)
     return None, 200
+
+
+@app.post("/export_ws_stats")
+async def export_ws_stats(request: ExportWsStatsRequest) -> responses.HTMLResponse:
+    session_token = general.get_ctx_token()
+    status_code, message = integration.export_weak_supervision_stats(
+        request.project_id, request.labeling_task_id
+    )
+    general.remove_and_refresh_session(session_token)
+
+    if status_code != 200:
+        raise HTTPException(status_code=status_code, detail=message)
+    return responses.HTMLResponse(status_code=status_code)

controller/integration.py

@@ -1,6 +1,8 @@
+import os
 from typing import Any, Dict, List, Tuple
 import traceback
 import pandas as pd
+import pickle
 from collections import defaultdict
 
 from submodules.model.models import (
@@ -26,6 +28,7 @@ def fit_predict(
     try:
         if task_type == enums.LabelingTaskType.CLASSIFICATION.value:
             results = integrate_classification(df)
+
         else:
             results = integrate_extraction(df)
         weak_supervision.store_data(
@@ -37,7 +40,7 @@ def fit_predict(
             weak_supervision_task_id,
             with_commit=True,
         )
-    except:
+    except Exception:
         print(traceback.format_exc(), flush=True)
         general.rollback()
         weak_supervision.update_state(
@@ -48,6 +51,44 @@ def fit_predict(
         )
 
 
+def export_weak_supervision_stats(
+    project_id: str, labeling_task_id: str
+) -> Tuple[int, str]:
+
+    task_type, df = collect_data(project_id, labeling_task_id, False)
+    try:
+        if task_type == enums.LabelingTaskType.CLASSIFICATION.value:
+            cnlm = util.get_cnlm_from_df(df)
+            stats_df = cnlm.quality_metrics()
+        elif task_type == enums.LabelingTaskType.INFORMATION_EXTRACTION.value:
+            enlm = util.get_enlm_from_df(df)
+            stats_df = enlm.quality_metrics()
+        else:
+            return 404, f"Task type {task_type} not implemented"
+
+        if len(stats_df) != 0:
+            stats_lkp = stats_df.set_index(["identifier", "label_name"]).to_dict(
+                orient="index"
+            )
+        else:
+            return 404, "Can't compute weak supervision"
+
+        os.makedirs(os.path.join("/inference", project_id), exist_ok=True)
+        with open(
+            os.path.join(
+                "/inference", project_id, f"weak-supervision-{labeling_task_id}.pkl"
+            ),
+            "wb",
+        ) as f:
+            pickle.dump(stats_lkp, f)
+
+    except Exception:
+        print(traceback.format_exc(), flush=True)
+        general.rollback()
+        return 500, "Internal server error"
+    return 200, "OK"
+
+
 def integrate_classification(df: pd.DataFrame):
     cnlm = util.get_cnlm_from_df(df)
     weak_supervision_results = cnlm.weakly_supervise()

start

@@ -5,6 +5,20 @@ echo -ne 'stopping old container...'
 docker stop refinery-weak-supervisor > /dev/null 2>&1
 echo -ne '\t [done]\n'
 
+INFERENCE_DIR=${PWD%/*}/dev-setup/inference/
+if [ ! -d "$_DIR" ] 
+then
+    INFERENCE_DIR=${PWD%/*/*}/dev-setup/inference/
+    if [ ! -d "$INFERENCE_DIR" ] 
+    then
+         # to include volume for local development, use the dev-setup inference folder:  
+        # alternative use manual logic with
+        # -v /path/to/dev-setup/inference:/models \
+        echo "Can't find model data directory: $INFERENCE_DIR -> stopping" 
+        exit 1
+    fi
+fi
+
 echo -ne 'building container...'
 docker build -t refinery-weak-supervisor-dev -f dev.Dockerfile . > /dev/null 2>&1
 echo -ne '\t\t [done]\n'
@@ -17,6 +31,7 @@ docker run -d --rm \
 -e WS_NOTIFY_ENDPOINT="http://refinery-websocket:8080" \
 --mount type=bind,source="$(pwd)"/,target=/app \
 -v /var/run/docker.sock:/var/run/docker.sock \
+-v "$INFERENCE_DIR":/inference \
 --network dev-setup_default \
 refinery-weak-supervisor-dev > /dev/null 2>&1
 echo -ne '\t\t\t [done]\n'