Update app.py

app.py

@@ -1,7 +1,42 @@
 import gradio as gr
+from transformers import pipeline
+import numpy as np
+import pandas as pd
+from sklearn.metrics.pairwise import cosine_similarity
+from InstructorEmbedding import INSTRUCTOR
 
-gr.Interface.load(
-    "spaces/jonathanjordan21/lmd_chatbot_embedding",
-    inputs=["text", gr.Slider(0.0, 1.0), "text", gr.Checkbox(label='Allow multiple true classes')],
-    outputs="json"
-).launch()
+# pipe = pipeline(model="facebook/bart-large-mnli")
+pipe = pipeline("zero-shot-classification", model="MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7")
+model = INSTRUCTOR('hkunlp/instructor-large')
+
+df = pd.read_csv('intent.csv', delimiter=';')
+
+data = [
+        [
+            f'Represent the document for retrieval of {x["description"]} information : ',
+            x["message"]
+        ] for _,x in df.iterrows()
+    ]
+
+corpus_embeddings = model.encode(data)
+
+
+def predict(question, lower_threshold, tags, multi_label):
+  query  = [['Represent the question for retrieving supporting documents: ',question]]
+  query_embeddings = model.encode(query)
+  similarities = cosine_similarity(query_embeddings,corpus_embeddings)
+  retrieved_doc_id = np.argmax(similarities)
+
+  if similarities[0][retrieved_doc_id] < float(lower_threshold):
+    ans = pipe(question, candidate_labels=[x.strip() for x in tags.split(",") if x.strip()!=""], multi_label=multi_label)
+    ans['query_similarity_score'] = similarities[0][retrieved_doc_id]
+    return ans
+  return {"chatbot_response" : data[retrieved_doc_id][-1], 'query_similarity_score' : similarities[0][retrieved_doc_id]}
+
+
+
+
+
+gr.Interface(fn=predict,
+             inputs=["text", gr.Slider(0.0, 1.0), "text", gr.Checkbox(label='Allow multiple true classes')],
+             outputs="json").launch()