Create app.py

app.py

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+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()