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Update app.py
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app.py
CHANGED
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@@ -3,26 +3,28 @@ from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
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import torch
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import os
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model_id = "TrabbyPatty/mistral-7b-instruct-finetuned-flashcards"
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hf_token = os.getenv("alluse") #
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tokenizer
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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device_map="cpu",
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torch_dtype=
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)
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pipe = pipeline(
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"text-generation",
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model=model,
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tokenizer=tokenizer,
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torch_dtype=torch.
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device_map="
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)
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# === SYSTEM MESSAGE ===
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SYSTEM_MESSAGE = """<<SYS>>
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You are a strict flashcard generator.
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@@ -31,8 +33,8 @@ You are a strict flashcard generator.
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- Always follow the requested format exactly.
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<</SYS>>"""
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def generate(user_input, max_new_tokens=800, temperature=0.5):
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# Wrap input with system instruction + prompt template
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prompt = (
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f"<s>[INST] {SYSTEM_MESSAGE}\n\n"
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f"Create a variety of study aids with 10 items each, strictly using only the information provided.\n\n"
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@@ -47,7 +49,7 @@ def generate(user_input, max_new_tokens=800, temperature=0.5):
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return output[0]["generated_text"]
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# Gradio UI
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demo = gr.Interface(
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fn=generate,
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inputs=[
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import torch
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import os
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# === Model ID and Token ===
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model_id = "TrabbyPatty/mistral-7b-instruct-finetuned-flashcards"
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hf_token = os.getenv("alluse") # Hugging Face token from Space secrets
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# === Load tokenizer & model with authentication ===
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tokenizer = AutoTokenizer.from_pretrained(model_id, token=hf_token)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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device_map="cpu", # force CPU
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torch_dtype=torch.float32, # safest for CPU
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token=hf_token
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)
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# === Create pipeline ===
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pipe = pipeline(
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"text-generation",
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model=model,
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tokenizer=tokenizer,
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torch_dtype=torch.float32,
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device_map="cpu"
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)
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# === SYSTEM MESSAGE ===
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SYSTEM_MESSAGE = """<<SYS>>
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You are a strict flashcard generator.
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- Always follow the requested format exactly.
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<</SYS>>"""
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# === Generation function ===
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def generate(user_input, max_new_tokens=800, temperature=0.5):
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prompt = (
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f"<s>[INST] {SYSTEM_MESSAGE}\n\n"
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f"Create a variety of study aids with 10 items each, strictly using only the information provided.\n\n"
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)
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return output[0]["generated_text"]
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# === Gradio UI ===
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demo = gr.Interface(
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fn=generate,
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inputs=[
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