Create app.py

app.py

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+import gradio as gr
+import numpy as np
+from usearch.index import Index
+from sentence_transformers import SentenceTransformer
+from datasets import load_dataset
+from sentencex import segment
+
+model = SentenceTransformer("Corran/SciGenAllMiniLM")
+
+train = load_dataset("Corran/SciGenColbertTriplets")['train']
+
+rf = sorted(list(set(train['query'])))
+rf_emb = model.encode(rf)
+index = Index(ndim=rf_emb[0].size)
+index.add(range(len(classes)), classes_emb)
+
+def get_matches(input):
+  global index, model, rf
+  emb = model.encode(input,batch_size=128)
+  matches = index.search(emb,4)
+  if type(input)==list and len(input)>1:
+    matches = [m[0] for m in matches]
+  else:
+    matches = [m for m in matches]
+  return [(rf[m.key],m.distance) for m in matches]
+
+
+
+def return_rf_scores(paragraph):
+    
+    sentences = list(segment("en", paragraph))
+    matches = get_matches(sentences)
+    
+    output = {}
+    for s,m in zip(sentences,matches):
+        output[s] = m
+        
+    return output
+
+demo = gr.Interface(fn=return_rf_scores, inputs="text", outputs="json")
+demo.launch()