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Update app.py
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app.py
CHANGED
@@ -1,27 +1,11 @@
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import os
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from fastapi import FastAPI
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from transformers import AutoModelForCausalLM, AutoTokenizer
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# Set cache directories to /tmp which is writable
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os.environ["TRANSFORMERS_CACHE"] = "/tmp/transformers_cache"
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os.environ["HF_HOME"] = "/tmp/hf_home"
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os.environ["XDG_CACHE_HOME"] = "/tmp/cache"
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# Create cache directories if they don't exist
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os.makedirs("/tmp/transformers_cache", exist_ok=True)
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os.makedirs("/tmp/hf_home", exist_ok=True)
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os.makedirs("/tmp/cache", exist_ok=True)
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# Load model with explicit cache directory
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model_name = "mynuddin/chatbot"
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)
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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cache_dir="/tmp/model_cache"
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).to("cpu")
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app = FastAPI()
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@@ -30,4 +14,4 @@ def generate_text(prompt: str):
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inputs = tokenizer(prompt, return_tensors="pt")
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output = model.generate(**inputs, max_length=128)
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generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
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return {"generated_query": generated_text}
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from fastapi import FastAPI
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "mynuddin/chatbot"
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# Load model and tokenizer without setting a custom cache
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(model_name).to("cpu")
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app = FastAPI()
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inputs = tokenizer(prompt, return_tensors="pt")
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output = model.generate(**inputs, max_length=128)
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generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
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return {"generated_query": generated_text}
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