--- a
+++ b/fast_api.py
@@ -0,0 +1,62 @@
+"""API to generate tags for each token in a given EHR"""
+
+from fastapi import FastAPI
+from pydantic import BaseModel
+from fastapi.middleware.cors import CORSMiddleware
+
+from predict import get_ner_predictions, get_re_predictions
+from utils import display_ehr, get_long_relation_table, display_knowledge_graph, get_relation_table
+
+
+class NERTask(BaseModel):
+    ehr_text: str
+    model_choice: str
+
+
+app = FastAPI()
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+with open("sample_ehr/104788.txt") as f:
+    SAMPLE_EHR = f.read()
+
+
+@app.post("/")
+def get_ehr_predictions(ner_input: NERTask):
+    """Request EHR text data and the model choice for NER Task"""
+
+    ner_predictions = get_ner_predictions(
+        ehr_record=ner_input.ehr_text,
+        model_name=ner_input.model_choice)
+
+    re_predictions = get_re_predictions(ner_predictions)
+    relation_table = get_long_relation_table(re_predictions.relations)
+
+    html_ner = display_ehr(
+        text=ner_input.ehr_text,
+        entities=ner_predictions.get_entities(),
+        relations=re_predictions.relations,
+        return_html=True)
+
+    graph_img = display_knowledge_graph(relation_table, return_html=True)
+    
+    if len(relation_table) > 0:
+        relation_table_html = get_relation_table(relation_table)
+    else:
+        relation_table_html = "<p>No relations found</p>"
+
+    if graph_img is None:
+        graph_img = "<p>No Relation found!</p>"
+
+    return {'tagged_text': html_ner, 're_table': relation_table_html, 'graph': graph_img}
+
+
+@app.get("/sample/")
+def get_sample_ehr():
+    """Returns a sample EHR record"""
+    return {"data": SAMPLE_EHR}