Create app.py

app.py

@@ -0,0 +1,57 @@
+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
+import dill
+def greet1(data):
+ # pdf=data.get('pdf')
+ print(data)
+ x=eval(data)
+ y=x.get('pdf')
+ print(y)
+ print(type(y))
+ print(type(dill.loads(eval(y))))
+ print(dill.loads(eval(y)).read())
+ return y
+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")
+ greet1_btn = gr.Button("Greet1")
+ greet1_btn.click(fn=greet1, inputs=name, outputs=output, api_name="testing")
+
+iface.launch()