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lucidmorto
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Commit
•
0cc4a5a
1
Parent(s):
0f051eb
refactor: Replace custom model loading with Gradio Client
Browse filesSimplified the code by using Gradio Client to interact with the model hosted on 'umutbozdag/humanizer_model' Space. This removes the need for manual model loading and preprocessing, reducing potential points of failure and streamlining text generation.
app.py
CHANGED
@@ -1,42 +1,16 @@
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import gradio as gr
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from
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from huggingface_hub import HfApi
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#
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space_name = "umut-bozdag/humanizer_model" # Replace with your actual space name
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model_files = api.list_repo_files(space_name)
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model_file = next(file for file in model_files if file.endswith('.bin'))
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model_revision = api.get_repo_info(space_name).sha
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# Load the model and tokenizer from the space
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tokenizer = AutoTokenizer.from_pretrained(space_name, revision=model_revision)
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model = AutoModelForSeq2SeqLM.from_pretrained(space_name, revision=model_revision)
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def generate_text(input_text):
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#
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input_ids = tokenizer.encode(input_text, return_tensors="pt", max_length=256, truncation=True)
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# Generate text with parameters matching your training setup
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outputs = model.generate(
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input_ids,
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max_length=256,
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num_return_sequences=1,
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no_repeat_ngram_size=2,
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top_k=30,
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top_p=0.9,
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temperature=0.7,
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do_sample=True,
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early_stopping=True,
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num_beams=4
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# Decode and clean up the generated text
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generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
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return generated_text.strip()
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iface = gr.Interface(
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fn=generate_text,
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import gradio as gr
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from gradio_client import Client
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# Create a client to interact with your Space
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client = Client("umutbozdag/humanizer_model")
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def generate_text(input_text):
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# Use the client to predict using your Space
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result = client.predict(
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input_text, # str in 'Input Text' Textbox component
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api_name="/predict"
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)
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return result
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iface = gr.Interface(
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fn=generate_text,
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