pushing to hub

app.py

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+import gradio as gr
+import galai as gal
+import re
+import urllib
+
+model = gal.load_model("base")
+
+def cite(prompt):
+    text = model.generate(prompt+'[START_REF]')
+    pattern = r'\[START_REF\](.*?)\[END_REF\]'
+    references = re.findall(pattern, text)
+
+    base_url = 'https://scholar.google.com/scholar?q='
+    search_links = [base_url + urllib.parse.quote(reference) for reference in references]
+
+    # Print the constructed links
+    for i in range(len(references)):
+
+        references[i] = f'<a href="{search_links[i]}" target="_blank">{references[i]}</a>'
+    
+    references = list(set(references))
+    return '<br>'.join(references)
+
+iface = gr.Interface(fn=cite, inputs="text", outputs="html",examples=["The cosine scheduler has been used in several papers as a scheduler for training large language models.",
+	"We propose a new simple network architecture based on the original Transformer.",
+	"The loss scales as a power-law with model size, dataset size, and the amount of compute used for training, with some trends spanning more than seven orders of magnitude.",
+    "Molecular species that emerge and destroy during the birth of stars can be used to track the starforming processes within molecular clumps and cores",
+    "Large Language Models (LLMs) have issues with document question answering (QA) in situations where the document is unable to fit in the small context length of an LLM"
+	]) 
+iface.launch()

requirements.txt

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+galai
+gradio