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.gitattributes

@@ -32,3 +32,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+TA_embeddings.csv filter=lfs diff=lfs merge=lfs -text

TA_embeddings.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8afcfddb6b6aa42dd3513a2e801d676c012da0d9199825064123c6a5812048af
+size 244603667

TaBot.py

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+import gradio as gr
+import openai
+import pandas as pd 
+import numpy as np
+import csv
+openai.api_key="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"
+from openai.embeddings_utils import get_embedding
+from openai.embeddings_utils import cosine_similarity
+df = pd.read_csv("TA_embeddings.csv")
+df["embedding"]=df["embedding"].apply(eval).apply(np.array)
+def reply(input):
+    
+    input = input
+    input_vector = get_embedding(input, engine="text-embedding-ada-002")
+    df["similiarities"]=df["embedding"].apply(lambda x: cosine_similarity(x,input_vector))
+    data = df.sort_values("similiarities", ascending=False).head(20)
+    data.to_csv("sorted.csv")
+    context = []
+    for i, row in data.iterrows():
+        context.append(row['text'])
+    context
+    text = "\n".join(context)
+    context = text
+    prompt = f"""
+                Answer the following question If you don't know the answer for certain, say I don't know.
+                Context: {context}
+
+                Q: {input}
+
+                """      
+    return openai.Completion.create(
+                    prompt=prompt,
+                    temperature=1,
+                    max_tokens=500,
+                    top_p=1,
+                    frequency_penalty=0,
+                    presence_penalty=0,
+                    model="text-davinci-003"
+                )["choices"][0]["text"].strip(" \n")
+    
+
+input_text = gr.inputs.Textbox(label="Enter your text here")
+text_output = gr.outputs.Textbox(label="Most similar text")
+
+ui = gr.Interface(fn=reply,
+                  inputs=input_text,
+                  outputs=[text_output],
+                  theme="compact",
+                  layout="vertical",
+                  inputs_layout="stacked",
+                  outputs_layout="stacked",
+                  allow_flagging=False)
+
+
+
+ui.launch()