Create app.py

app.py

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+from sentence_transformers import SentenceTransformer
+import numpy as np
+import faiss
+import gradio as gr
+
+def read_text_from_file(file_path):
+    with open(file_path, "r") as text_file:
+        text = text_file.read()
+    return text
+
+text_file_path = "Southampton.txt"
+texts = read_text_from_file(text_file_path)
+texts = texts.split("&&")
+
+model = SentenceTransformer('sentence-transformers/multi-qa-MiniLM-L6-cos-v1')
+
+doc_emb = model.encode(texts)
+d = doc_emb.shape[1]  # Dimension of vectors
+print(doc_emb.shape)
+index = faiss.IndexFlatL2(d)
+index.add(doc_emb)
+
+def embed_query(query):
+    query_emb = model.encode(query)
+    return query_emb
+
+def question(query):
+  query_vector = np.asarray(embed_query(query))
+  query_vector=np.expand_dims(query_vector,axis=0)
+  print(query_vector.shape)
+  k = 2 # Number of nearest neighbors to retrieve
+  D, I = index.search(query_vector, k)
+  relevant_paragraph=""
+  for i in range(k):
+    relevant_paragraph_index = I[0][i]
+    relevant_paragraph += texts[relevant_paragraph_index] + "\n"
+
+  return relevant_paragraph
+
+demo = gr.Interface(fn=question, inputs="text", outputs="text")
+    
+if __name__ == "__main__":
+    demo.launch()