upd

src/streamlit_app.py

@@ -1,40 +1,24 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
 
-"""
-# Welcome to Streamlit!
+@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)
 
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
+st.set_page_config(page_title="LLM Demo", layout="centered")
+st.title("🚀 FLAN-T5 Small - HuggingFace Demo")
 
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
+pipe = load_model()
 
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
+user_input = st.text_area("Enter your instruction or question:", "")
 
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+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)