Create app.py

app.py

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