Upload 3 files

app.py

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+import gradio as gr
+import openai
+import pandas as pd 
+import numpy as np
+
+openai.api_key="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"
+from openai.embeddings_utils import get_embedding
+from openai.embeddings_utils import cosine_similarity
+
+def similarity(input_text):
+    df= pd.read_csv("meg_embeddings.csv")
+    df['embedding'] = df['embedding'].apply(eval).apply(np.array)
+    input = input_text
+    input_vector = get_embedding(input, engine="text-embedding-ada-002")
+    df["similarities"] = df['embedding'].apply(lambda x: cosine_similarity(x, input_vector))
+    sorted_df =df.sort_values("similarities", ascending=False)
+    top_row = sorted_df.loc[0]
+    return sorted_df.iloc[0]["text"]
+
+input_text = gr.inputs.Textbox(label="Input Text")
+
+
+output_label = gr.outputs.Label(label="Similarity Text")
+
+gr.Interface(fn=similarity, inputs=[input_text], outputs=output_label, title="Semantic Similarity Checker", description="Check if input text is semantically similar to file saved locally using Ada text embeddings.").launch()

requirements.txt

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+openai
+plotly
+scipy