Update app.py

app.py

@@ -1,39 +1,39 @@
 import gradio as gr
-from sentence_transformers import SentenceTransformer, util
-
-model_name = 'nq-distilbert-base-v1'
-bi_encoder = SentenceTransformer("./")
-top_k = 5
-sentences = [
- "a happy person is a person how can do what he want with his money",
- "That is a happy dog ho bark alot",
- "Today is a sunny day so that a happy person can walk on the street"
-]
-# vector embeddings created from dataset
-corpus_embeddings = bi_encoder.encode(sentences, convert_to_tensor=True, show_progress_bar=True)
-
-def search(query):
- # Encode the query using the bi-encoder and find potentially relevant passages
- question_embedding = bi_encoder.encode(query)
- hits = util.semantic_search(question_embedding, corpus_embeddings, top_k=top_k)
- hits = hits[0] # Get the hits for the first query
-
- # Output of top-k hits
- print("Input question:", query)
- print("Results")
- for hit in hits:
- print("\t{:.3f}\t{}".format(hit['score'], sentences[hit['corpus_id']]))
- return hits
-
-def greet(name):
- hittt = search(query=name)
- x=dict()
- for hit in hittt:
- score=hit['score']
- sentence=sentences[hit['corpus_id']]
- buffer={sentence:score}
- x.update(buffer)
- return x
+# from sentence_transformers import SentenceTransformer, util
+# 
+# model_name = 'nq-distilbert-base-v1'
+# bi_encoder = SentenceTransformer("./")
+# top_k = 5
+# sentences = [
+# "a happy person is a person how can do what he want with his money",
+# "That is a happy dog ho bark alot",
+# "Today is a sunny day so that a happy person can walk on the street"
+# ]
+# # vector embeddings created from dataset
+# corpus_embeddings = bi_encoder.encode(sentences, convert_to_tensor=True, show_progress_bar=True)
+# 
+# def search(query):
+# # Encode the query using the bi-encoder and find potentially relevant passages
+# question_embedding = bi_encoder.encode(query)
+# hits = util.semantic_search(question_embedding, corpus_embeddings, top_k=top_k)
+# hits = hits[0] # Get the hits for the first query
+# 
+# # Output of top-k hits
+# print("Input question:", query)
+# print("Results")
+# for hit in hits:
+# print("\t{:.3f}\t{}".format(hit['score'], sentences[hit['corpus_id']]))
+# return hits
+# 
+# def greet(name):
+# hittt = search(query=name)
+# x=dict()
+# for hit in hittt:
+# score=hit['score']
+# sentence=sentences[hit['corpus_id']]
+# buffer={sentence:score}
+# x.update(buffer)
+# return x
 import dill
 def greet1(data):
  # pdf=data.get('pdf')
@@ -49,8 +49,8 @@ iface = gr.Blocks()
 with iface:
  name = gr.Textbox(label="Name")
  output = gr.Textbox(label="Output Box")
- greet_btn = gr.Button("Greet")
- greet_btn.click(fn=greet, inputs=name, outputs=output, api_name="greet")
+ # greet_btn = gr.Button("Greet")
+ # greet_btn.click(fn=greet, inputs=name, outputs=output, api_name="greet")
  greet1_btn = gr.Button("Greet1")
  greet1_btn.click(fn=greet1, inputs=name, outputs=output, api_name="testing")