Changed setting to FastAPI

Dockerfile

@@ -8,4 +8,4 @@ RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
 
 COPY . .
 
-CMD ["panel", "serve", "/code/app.py", "--address", "0.0.0.0", "--port", "7860", "--allow-websocket-origin", "spitzc32-BiLSTM-api.hf.space:80"]
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

@@ -1,69 +0,0 @@
-from model.layer import Bi_LSTM_CRF
-from flair.data import Sentence
-import panel as pn
-
-pn.extension()
-pn.extension('tabulator')
-
-
-# Variables for Interactive selections
-tagger = Bi_LSTM_CRF.load("checkpoints/best-model.pt")
-text_widget = pn.widgets.TextAreaInput(value="George Washington lives in Washington", height=300, name='Add text')
-button = pn.widgets.Button(name="Click me to run!")
-explanation = pn.pane.Markdown(
-"""
-## Redaction API
-This app provides a text classification for a given text. To fully understand the redaction process, 
-given below is the classification of each text under the file we need the redaction on.
-
-"""
-)
-                               
-
-def model(word: str):
-    """
-    An function for serving the model for the PHI classification.
-    :param word: list of word tokens in a paragraph.
-
-    :returns: dict that contains labeled 
-        tags their respective classification.
-    """
-    txt = Sentence(word)
-    tagger.predict(txt)
-    labels, tags = [], []
-
-    for entity in txt.get_spans('ner'):
-        labels.append(entity.text)
-        tags.append(entity.get_label("ner").value)
-    message = f"""
-        'labels': {labels},
-        'tags': {tags}
-    """
-    #return pn.pane.Markdown(f"""{
-    #        'labels': {labels},
-    #        'tags': {tags}
-    #    }""")
-    return pn.pane.Markdown(message)
-
-def get_tag_results(_):
-    return pn.Column(
-        explanation,
-        pn.pane.Markdown("""
-        ##Results:"""),
-        model(text_widget.value.replace("\n", ""))
-    )
-
-
-        
-interactive = pn.bind(get_tag_results, button)
-template = pn.template.FastListTemplate(
-    title='Model API', 
-    sidebar=[
-        button,
-        text_widget
-    ],
-    main=[pn.panel(interactive, loading_indicator=False)],
-    accent_base_color="#88d8b0",
-    header_background="#88d8b0",
-)
-template.servable()

main.py

@@ -0,0 +1,68 @@
+from model.layer import Bi_LSTM_CRF
+from flair.data import Sentence
+from schema import WordSchema
+from settings import app
+
+from fastapi import  APIRouter
+from starlette import status
+from starlette.responses import JSONResponse
+import uvicorn
+
+api_router = APIRouter()
+
+
+
+# Variables for Interactive selections
+tagger = Bi_LSTM_CRF.load("checkpoints/best-model.pt")
+
+@api_router.get("/")
+def health():
+    return JSONResponse(
+            status_code=status.HTTP_201_CREATED,
+            content={
+                "code": 200,
+                "version": "1.0.0"
+            },
+        )
+
+                               
+
+@api_router.get("/api/model", response_model=WordSchema)
+def model(
+    *, 
+    word: str,
+    file_name: str
+    ):
+    """
+    An api for serving the model for the PHI classification.
+    :param word: list of word tokens in a paragraph.
+    :param file_name: name of the wile.
+
+    :returns: json response that contains labeled 
+        tags their respective classification.
+    """
+    txt = Sentence(word)
+    tagger.predict(txt)
+    labels, tags = [], []
+
+    for entity in txt.get_spans('ner'):
+        labels.append(entity.text)
+        tags.append(entity.get_label("ner").value)
+    
+
+    return JSONResponse(
+            status_code=status.HTTP_201_CREATED,
+            content={
+                "code": 200,
+                "data": {
+                    "labels": labels,
+                    "tags": tags
+                }
+            },
+        )
+
+
+app.include_router(api_router)
+
+if __name__ == "__main__":
+    uvicorn.run("main:app", host="0.0.0.0", port=8000, reload=True, debug=True)

requirements.txt

@@ -5,5 +5,6 @@ flair
 numpy
 pandas
 nltk
-panel
-hvplot
+uvicorn
+fastapi
+starlette

schema.py

@@ -0,0 +1,5 @@
+from pydantic import BaseModel
+
+
+class WordSchema(BaseModel):
+    word_text: str

settings.py

@@ -0,0 +1,35 @@
+# PROGRAM:      settings.py --> Program to handle all configurations inside the API
+# PROGRAMMER:   Jayra Gaile Ortiz
+# VERSION 1:    08-29-22 Initial API setup and i2b2 converted sets
+# REVISION 1.1: 11-05-22 Fizes to port web
+# PURPOSE:      Handles configurations for the API
+# ALGORITHM:    Stores app documentations, handles API configurations
+
+
+"""
+    This file includes all the configuration file for API
+"""
+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+
+
+# Handles documentation title
+app = FastAPI(
+    version="version 1.0.0",
+    title="Redaction Backend",
+    description="List of all endpoints for the Backend API",
+    docs_url="/", redoc_url= None
+)
+
+# Handles cors
+origins = [
+    "http://localhost:8000"
+]
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=origins,
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)