Create app.py

app.py

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+from sentence_transformers import SentenceTransformer
+import torch
+import gradio as gr
+from scipy.spatial.distance import cosine
+
+# Disable CUDA
+torch.backends.cudnn.enabled = False
+torch.cuda.is_available = lambda : False
+
+# Load model and tokenizer
+modelname = "algolia/algolia-large-en-generic-v2410"
+model = SentenceTransformer(modelname) 
+def get_embedding(text):
+    embedding = model.encode([text])
+    return embedding[0]
+
+def compute_similarity(query, documents):
+    query_emb = get_embedding(query)
+    doc_embeddings = [get_embedding(doc) for doc in documents]
+    
+    # Calculate cosine similarity
+    similarities = [1 - cosine(query_emb, doc_emb) for doc_emb in doc_embeddings]
+    ranked_docs = sorted(zip(documents, similarities), key=lambda x: x[1], reverse=True)
+    
+    # Format output
+    return [{"document": doc, "similarity_score": round(sim, 4)} for doc, sim in ranked_docs]
+
+# Gradio interface function
+def gradio_compute_similarity(query, documents):
+    # Prefix the query string
+    query = "query: " + query
+    # Split documents by lines for the Gradio input
+    documents_list = documents.split("\n")
+    results = compute_similarity(query, documents_list)
+    return results
+
+# Gradio Interface
+iface = gr.Interface(
+    fn=gradio_compute_similarity,
+    inputs=[
+        gr.Textbox(label="Query", placeholder="Enter your query here"),
+        gr.Textbox(lines=5, label="Documents", placeholder="Enter a list of documents, one per line")
+    ],
+    outputs=gr.JSON(label="Ranked Results"),
+    allow_flagging="never",
+    title="Document Similarity",
+    description="Provide a query and a list of documents. See the ranked similarity scores."
+)
+
+iface.launch()