Create app.py

app.py

@@ -0,0 +1,48 @@
+import openai
+import faiss
+import numpy as np
+
+text_file_path = 'risale3.txt'
+
+# read the text file
+with open(text_file_path, 'r', encoding='utf-8') as file:
+    text_content = file.read()
+
+# split text into sentences
+allText = [sentence.strip() for sentence in text_content.split('<br>') if sentence.strip()]
+
+def encode_open(input):
+# you can change model if you want different
+    MODEL = "text-embedding-ada-002"
+    res = openai.Embedding.create(
+       input=input, engine=MODEL
+    )
+    embeds = [record['embedding'] for record in res['data']]
+    import torch
+    embeds=torch.FloatTensor(embeds)
+    return embeds
+
+encoded_data=encode_open(allText)
+d=encoded_data.shape[1]
+index = faiss.IndexIDMap(faiss.IndexFlatIP(d))
+index.add_with_ids(encoded_data, np.arange(encoded_data.shape[0]))
+
+def search_openai_pdf(query, k=10):
+    query_vector = encode_open([query])
+    top_k = index.search(query_vector, k)
+    print(top_k[1])
+    return [
+        allText[_id] for _id in top_k[1][0]
+    ]
+
+search_openai_pdf("ibadet neden etmeliyim")
+
+iface = gr.Interface(
+    fn=search_openai_pdf,
+    inputs=gr.Textbox(text="ibadet neden etmeliyim", label="query"),
+    outputs=gr.Textbox(label="Sonuçlar"),
+    live=True
+)
+
+iface.launch()
+