Initial Implementation

app.py

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+import streamlit as st
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
+
+@st.cache_resource
+def load_model():
+    tokenizer = AutoTokenizer.from_pretrained("google/flan-t5-small")
+    model = AutoModelForSeq2SeqLM.from_pretrained("google/flan-t5-small")
+    return pipeline("text2text-generation", model=model, tokenizer=tokenizer)
+
+st.set_page_config(page_title="LLM Demo", layout="centered")
+st.title("🚀 FLAN-T5 Small - HuggingFace Demo")
+
+pipe = load_model()
+
+user_input = st.text_area("Enter your instruction or question:", "")
+
+if st.button("Generate Response"):
+    if user_input.strip() == "":
+        st.warning("Please enter some text.")
+    else:
+        with st.spinner("Generating..."):
+            output = pipe(user_input, max_new_tokens=100)[0]["generated_text"]
+            st.success("### Response:")
+            st.write(output)

requirements.txt

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+transformers
+torch
+streamlit