update app.py

app.py

@@ -4,24 +4,25 @@ from PIL import Image
 from transformers import pipeline, AutoTokenizer
 from datasets import load_dataset, Dataset
 import os
-HUGGINGFACE_TOKEN = os.environ['HUGGINGFACE_TOKEN']
+from supabase import create_client, Client
 
-DATASET_REPO = "tiedaar/question_scoring_stresstest"
+url: str = os.environ.get("SUPABASE_URL")
+key: str = os.environ.get("SUPABASE_KEY")
+supabase: Client = create_client(url, key)
 
-pipe = pipeline('text-classification', model='tiedaar/short-answer-classification')
-bleurt_pipe = pipeline('text-classification', model="vaiibhavgupta/finetuned-bleurt-large")
-tokenizer = AutoTokenizer.from_pretrained("vaiibhavgupta/finetuned-bleurt-large")
 
-subsections = pd.read_csv('tp-subsections.csv').dropna(axis=0, how='any', subset=['question'])
-output = load_dataset(DATASET_REPO)['train'].to_pandas()
+# pipe = pipeline('text-classification', model='tiedaar/short-answer-classification')
+# bleurt_pipe = pipeline('text-classification', model="vaiibhavgupta/finetuned-bleurt-large")
+# tokenizer = AutoTokenizer.from_pretrained("vaiibhavgupta/finetuned-bleurt-large")
 
-text = st.text_input("Write your answer here")
+# subsections = pd.read_csv('tp-subsections.csv').dropna(axis=0, how='any', subset=['question'])
 
+text = st.text_input("Write your answer here")
 if text:
-    entry = [1,0,0,0,0,0,text,0]
-    output.loc[len(output)] = (entry)
-    output_ds = Dataset.from_pandas(output)
-    output_ds.push_to_hub(DATASET_REPO, token=HUGGINGFACE_TOKEN)
+    data, count = supabase.table('automatic-short-answer-scoring') \
+        .insert({"subsection": 1, "question": "Denmark", "answer":text}).execute()
+
+
 
 
 # def reset():