Update app.py
Browse files
app.py
CHANGED
@@ -1,23 +1,25 @@
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import gradio as gr
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# Define function to translate code
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def translate_code(input_code, prompt=""):
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# Combine input code and prompt
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input_text = f"{prompt}\n\n{input_code}"
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# Tokenize input text
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input_ids = tokenizer.encode(input_text, return_tensors="pt", max_length=1024, truncation=True)
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# Generate output sequence
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output = model.generate(input_ids, max_length=1024, num_return_sequences=1, temperature=0.7)
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# Decode output sequence
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translated_code = tokenizer.decode(output[0], skip_special_tokens=True)
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return translated_code
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# Launch Gradio interface
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gr.Interface(
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fn=translate_code,
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inputs=["text", "text"],
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import gradio as gr
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import os
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huggingface_token = os.getenv("HUGGINGFACE_TOKEN")
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if huggingface_token is None:
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print("Token Hugging Face tidak ditemukan. Pastikan Anda telah menetapkan variabel lingkungan HUGGINGFACE_TOKEN.")
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exit()
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tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3-8B", token=huggingface_token)
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model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3-8B", token=huggingface_token)
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def translate_code(input_code, prompt=""):
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input_text = f"{prompt}\n\n{input_code}"
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input_ids = tokenizer.encode(input_text, return_tensors="pt", max_length=1024, truncation=True)
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output = model.generate(input_ids, max_length=1024, num_return_sequences=1, temperature=0.7)
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translated_code = tokenizer.decode(output[0], skip_special_tokens=True)
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return translated_code
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gr.Interface(
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fn=translate_code,
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inputs=["text", "text"],
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