Create app.py

app.py

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+import torch
+from sentence_transformers import SentenceTransformer, util
+import pandas as pd
+import gradio as gr
+
+def save_embeddings(sentences, filename):
+    embeddings = model.encode(sentences, convert_to_tensor=True)
+    torch.save(embeddings, filename)
+
+def load_embeddings(filename):
+    return torch.load(filename)
+
+def preprocess_model_descriptions(file_path):
+    encodings = ['utf-8', 'latin-1', 'utf-16']
+    for encoding in encodings:
+        try:
+            df = pd.read_csv(file_path, sep='\t', header=None, names=['model_id', 'description'])
+            df.dropna(subset=['description'], inplace=True)
+            model_ids = df['model_id'].tolist()
+            descriptions = df['description'].tolist()
+            break
+        except UnicodeDecodeError:
+            continue
+    else:
+        raise UnicodeDecodeError("Unable to decode the file using the available encodings.")
+
+    return model_ids, descriptions
+
+def perform_similarity_search(query_embeddings, embeddings, model_ids, descriptions, top_k=10):
+    cosine_scores = util.cos_sim(query_embeddings, embeddings)
+    similarity_scores = cosine_scores.tolist()
+
+    results = []
+    for i, query_embedding in enumerate(query_embeddings):
+        query_results = sorted(zip(model_ids, descriptions, similarity_scores[i]), key=lambda x: x[2], reverse=True)[:top_k]
+        results.extend(query_results)
+
+    return pd.DataFrame(results, columns=["model_id", "description", "score"])
+
+model = SentenceTransformer('all-MiniLM-L6-v2')
+
+model_ids, descriptions = preprocess_model_descriptions('model_descriptions.tsv')
+embeddings = load_embeddings('embeddings_model_descriptions.pt')
+
+with gr.Blocks() as demo:
+    input = gr.Textbox(label="Enter your query")
+    button = gr.Button(label="Search")
+    
+    df_output = gr.Dataframe(label="Similarity Results", wrap=True)
+    
+    def search(query):
+        query_embedding = model.encode([query], convert_to_tensor=True)
+        return perform_similarity_search(query_embedding, embeddings, model_ids, descriptions)
+    
+    input.submit(search, inputs=input, outputs=df_output)
+    button.click(search, inputs=input, outputs=df_output)
+
+demo.launch()