Create app.py

app.py

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+import torch
+from transformers import AutoTokenizer, AutoModel
+from torch.nn.functional import cosine_similarity
+import gradio as gr
+
+model_name = 'bert-base-multilingual-cased'
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModel.from_pretrained(model_name)
+
+# Function to compute embeddings
+def compute_embedding(text):
+    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
+    with torch.no_grad():
+        outputs = model(**inputs)
+        embedding = outputs.last_hidden_state.mean(dim=1)
+    return embedding
+
+# Function to compute similarity between two sentences
+def compare_sentences(text1, text2):
+    embedding1 = compute_embedding(text1)
+    embedding2 = compute_embedding(text2)
+    similarity_score = cosine_similarity(embedding1, embedding2).item()
+    return f"Similarity Score: {similarity_score:.4f}"
+
+# Gradio interface for input
+iface = gr.Interface(fn=compare_sentences, 
+                     inputs=["text", "text"], 
+                     outputs="text", 
+                     title="Sentence Similarity",
+                     description="Enter two sentences to compute their similarity.")
+
+iface.launch()