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from sentence_transformers import SentenceTransformer
import gradio as gr
import faiss
import numpy as np
model = SentenceTransformer('sentence-transformers/all-mpnet-base-v2')
#model.save('models/all-mpnet-base-v2')
#model.to('cuda')
def EmdeddingVect(sentences):
return model.encode([sentences]) # Assume model.encode() function exists
# Load the arrays
all_embeddings = np.load('all_embeddings.npy')
all_labels = np.load('all_labels.npy')
index = faiss.IndexFlatL2(768)
index.add(all_embeddings.astype('float32'))
def search_query(k, query_sentence):
query_embeddings = EmdeddingVect(query_sentence)
distances, indices = index.search(query_embeddings.astype('float32'), int(k))
ai_count = np.sum(all_labels[indices[0]] == 'AI')
ai_probability = (ai_count / int(k)) * 100
human_probability = 100 - ai_probability
return f"Probability of being AI: {ai_probability:.2f}%", f"Probability of being Human: {human_probability:.2f}%"
iface = gr.Interface(
fn=search_query,
inputs=[
gr.inputs.Slider(minimum=1, maximum=10, default=3, label="k (Number of Neighbors)"),
gr.inputs.Textbox(label="Text to be Detected")
],
outputs=[
gr.outputs.Textbox(label="AI Probability"),
gr.outputs.Textbox(label="Human Probability")
]
)
iface.launch()
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