Create app.py

app.py

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+from sentence_transformers import SentenceTransformer
+import gradio as gr
+import faiss
+import numpy as np
+model = SentenceTransformer('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('models/all_embeddings.npy')
+all_labels = np.load('models/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()