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")