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Create app.py
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app.py
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import streamlit as st
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from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
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# Load the locally saved fine-tuned model inside your space
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MODEL_DIR = "./laptop-tinyllama"
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@st.cache_resource
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def load_pipeline():
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tokenizer = AutoTokenizer.from_pretrained(MODEL_DIR)
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model = AutoModelForCausalLM.from_pretrained(MODEL_DIR)
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return pipeline("text-generation", model=model, tokenizer=tokenizer)
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# Load model pipeline
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generator = load_pipeline()
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# Streamlit UI
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st.title("💻 Laptop Recommendation with TinyLlama")
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st.write("Enter a question like: *Suggest a laptop for gaming under 1 lakh BDT.*")
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# Prompt input
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prompt = st.text_area("Enter your query", value="Suggest a laptop for programming under 70000 BDT.")
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if st.button("Generate Response"):
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with st.spinner("Generating..."):
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result = generator(prompt, max_new_tokens=100, temperature=0.7)
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st.success(result[0]["generated_text"])
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