init

app.py

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+from sentence_transformers import SentenceTransformer
+import numpy as np
+import requests
+
+import gradio as gr
+
+def request_and_response(url):
+    response = requests.get(url)
+    papers = response.json()
+    return papers
+
+def extract_abstracts_and_ids(papers):
+    abstracts = [paper["paper"]["summary"] for paper in papers]
+    paper_ids = [paper["paper"]["id"] for paper in papers]
+    return abstracts, paper_ids
+
+def get_embeddings(model, texts):
+    embeddings = model.encode(texts)
+    return embeddings
+
+def compute_similarity(model, embeddings1, embeddings2):
+    similarities =  model.similarity(embeddings1, embeddings2)
+    return similarities
+
+def find_closest(similarities, paper_ids):
+    best_match_idx = np.argmax(similarities)
+    best_match_id = paper_ids[best_match_idx]
+    return best_match_id
+
+# Step 0: Get the model
+model = SentenceTransformer("all-MiniLM-L6-v2")
+
+# Step 1: Get papers from API
+papers = request_and_response("https://hf.co/api/daily_papers")
+
+# Step 2: Extract abstracts and paper ids
+abstracts, paper_ids = extract_abstracts_and_ids(papers)
+
+# Step 3: Embed Query and the Abstracts of papers
+abstract_embeddings = get_embeddings(model, abstracts)
+
+def get_closest_paper(query):
+    query_embeddings = get_embeddings(model, [query])
+
+    # Step 4: Find similarity scores
+    similarities = compute_similarity(model, query_embeddings, abstract_embeddings)
+
+    # Step 5: Find the closest match
+    best_match_id = find_closest(similarities, paper_ids)
+
+    # Step 6: Get the best match paper title and id
+    paper = request_and_response(f"https://hf.co/api/papers/{best_match_id}")
+    title = paper["title"]
+    summary = paper["summary"]
+
+    return title, f"https://huggingface.co/papers/{best_match_id}", summary
+
+
+with gr.Blocks() as iface:
+    gr.Markdown("""\
+# Query AK's Daily Paper Collection
+Query papers you want to read from AK's daily paper collection. Ask what you want to read,
+and we will provide you with the paper id that serves your query the best. It is a work in progress (please be kind)\
+
+Thanks to Tom Arsen for reviewing the code and working alongside.""")
+    query = gr.Textbox(placeholder="What do you have in mind?")
+    with gr.Row():
+        title = gr.Textbox()
+        paper_link = gr.Textbox()
+        abstract = gr.Textbox()
+    btn = gr.Button(value="Submit")
+    btn.click(get_closest_paper, query, [title, paper_link, abstract])
+
+
+if __name__ == "__main__":
+    iface.launch()

requirements.txt

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+gradio
+sentence-transformers