question answering model

app.py

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+import pandas as pd
+from sentence_transformers import SentenceTransformer, util
+import gradio
+
+PATH_TO_FILE = 'cabot_dataset_def_csv.csv'
+df = pd.read_csv(PATH_TO_FILE, sep=';')
+df.drop_duplicates(inplace=True, ignore_index=True)
+
+model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
+
+def run_question(domanda):
+    somme = []
+    for jdx, j in enumerate(list(df.Question)):
+        somme.append(float(util.pytorch_cos_sim(
+            model.encode(list(df.Question)[jdx], convert_to_tensor=True),
+            model.encode(domanda, convert_to_tensor=True))))
+    if round(max(somme)*100) < 70:
+        return 'Sorry, could you paraphrase your question? I quide didn\'t catch that...'
+    else:
+        return df.loc[somme.index(max(somme)), 'Answer']
+
+gradio.Interface(run_question, "text", "text",
+                 title='Introducing C.A.BOT',
+                 description='This is Cabot, the Consular Affairs bot. Give it a try asking').launch()
+
+
+
+
+