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| import gradio as gr | |
| from huggingface_hub import InferenceClient | |
| import ast | |
| from graphviz import Digraph | |
| client = InferenceClient("Qwen/Qwen2.5-72B-Instruct") | |
| def sampling(num_samples, num_associations): | |
| outputs = ast.literal_eval(client.chat.completions.create( | |
| messages=[ | |
| {"role": "system", "content": "generate one json object, no explanation or additional text, use the following structure:\n" | |
| "words: []\n" | |
| f"{num_samples} samples in a list" | |
| }, | |
| {"role": "user", | |
| "content": f"synthesize {num_samples} random but widespread words for semantic modeling"}, | |
| ], | |
| response_format={ | |
| "type": "json", | |
| "value": { | |
| "properties": { | |
| "words": {"type": "array", "items": {"type": "string"}}, | |
| } | |
| } | |
| }, | |
| stream=False, | |
| max_tokens=1024, | |
| temperature=0.7, | |
| top_p=0.1 | |
| ).choices[0].get('message')['content']) | |
| fields = {} | |
| for word in outputs['words']: | |
| fields[word] = ast.literal_eval(client.chat.completions.create( | |
| messages=[ | |
| {"role": "system", "content": 'generate one json object, no explanation or additional text, use the following structure:\n' | |
| 'associations: []' | |
| }, | |
| {"role": "user", | |
| "content": f"synthesize {num_associations} associations for the word {word}"}, | |
| ], | |
| response_format={ | |
| "type": "json", | |
| "value": { | |
| "properties": { | |
| "associations": {"type": "array", "items": {"type": "string"}} | |
| } | |
| } | |
| }, | |
| stream=False, | |
| max_tokens=2000, | |
| temperature=0.7, | |
| top_p=0.1 | |
| ).choices[0].get('message')['content']) | |
| triplets = [] | |
| for cluster in fields: | |
| for association in fields[cluster]['associations']: | |
| triplets.append(ast.literal_eval(client.chat.completions.create( | |
| messages=[ | |
| {"role": "system", "content": "generate one json object, no explanation or additional text, use the following structure:\n" | |
| "properties: [subject, predicate, object]\n" | |
| "use chain-of-thought for predictions" | |
| }, | |
| {"role": "user", | |
| "content": f"form triplet based on semantics: generate predicate between the word {cluster} (subject) and the word {association} (object); return list with [subject, predicate, object]"}, | |
| ], | |
| response_format={ | |
| "type": "json", | |
| "value": { | |
| "properties": { | |
| "properties": {"type": "array", "items": {"type": "string"}} | |
| } | |
| } | |
| }, | |
| stream=False, | |
| max_tokens=128, | |
| temperature=0.7, | |
| top_p=0.1 | |
| ).choices[0].get('message')['content'])) | |
| dot = Digraph(comment=f'SynthNet, {num_samples} samples, {num_associations} associations', graph_attr={'rankdir': 'LR'}) | |
| for entry in triplets: | |
| source, label, target = entry['properties'] | |
| dot.node(source, source) | |
| dot.node(target, target) | |
| dot.edge(source, target, label=label) | |
| dot.render('synthnet', format='png') | |
| return 'synthnet.png' | |
| demo = gr.Interface( | |
| inputs=[ | |
| gr.Slider(minimum=1, maximum=256, label="Number of Samples"), | |
| gr.Slider(minimum=1, maximum=256, label="Number of Associations to each Sample"), | |
| ], | |
| fn=sampling, | |
| outputs=gr.Image(type="filepath"), | |
| title="SynthNet", | |
| description="Select a number of samples and associations to each sample to generate a graph.", | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() | |