Update app.py

app.py

@@ -1,40 +1,19 @@
 import json
-import os
 import urllib
 
-import requests
 import streamlit as st
-from dotenv import load_dotenv
 from transformers import pipeline
 
-load_dotenv()
 
 TEST_METRICS = 'https://huggingface.co/nbroad/longformer-base-health-fact/raw/main/test_results.json'
-
-
-def query(payload):
-    API_URL = 'https://api-inference.huggingface.co/models/nbroad/longformer-base-health-fact'
-    headers = {'Authorization': f'Bearer {os.environ["API_TOKEN"]}'}
-    response = requests.post(API_URL, headers=headers, json=payload)
-    return response.json()
-
-
 st.title('Veracity of a claim with pretrained longformer (PUBHEALTH)')
-api = st.selectbox(label='Select inference API to use',
-                   options=(None, 'Pipeline API', 'HF Inference API'),
-                   index=0)
-if api:
-    st.header('Claim')
-    claim = st.text_area(label='Enter a claim text in the box')
-    if api == 'Pipeline API':
-        pl = pipeline('text-classification',
-                      model='nbroad/longformer-base-health-fact')
-        st.header('Results')
-        st.write(pl(claim))
-    elif api == 'HF Inference API':
-        st.header('Results')
-        st.write(query({'inputs': claim}))
-    st.header('Test metrics')
-    with urllib.request.urlopen(TEST_METRICS) as response:
-        metrics = json.load(response)
-    st.write(metrics)
+st.header('Claim')
+claim = st.text_area(label='Enter a claim text in the box')
+pl = pipeline('text-classification',
+              model='nbroad/longformer-base-health-fact')
+st.header('Results')
+st.write(pl(claim))
+st.header('Test metrics')
+with urllib.request.urlopen(TEST_METRICS) as response:
+    metrics = json.load(response)
+st.write(metrics)