Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,42 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
-
-"""
-# Welcome to Streamlit!
-
-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).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-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])),
-    ))
+# Import the high-level pipeline API from Hugging Face Transformers
+# It simplifies loading models/tokenizers and running common tasks
+from transformers import pipeline
+
+# 1. Cache the pipeline so it loads once
+@st.cache_resource
+def get_generator():
+    # Initialize a text-to-text generation pipeline:
+    # - "text2text-generation" tells the pipeline we want a seq2seq model (T5 family)
+    # - model="google/flan-t5-small" specifies which pretrained model to load
+    # The pipeline object wraps both tokenizer and model for you.
+    return pipeline("text2text-generation", model="google/flan-t5-small", use_auth_token=True)
+
+generator = get_generator()
+
+st.title("📝 FLAN-T5 Text-to-Text Generator")
+st.write("Enter a prompt below and hit Generate to see the model’s output.")
+
+# 2. Prompt the user for input
+user_input = st.text_area("Your prompt:", height=120)
+
+# 3. Generation settings in the sidebar
+with st.sidebar:
+    st.header("Generation Settings")
+    max_length = st.slider("Max output length",  min_value=16, max_value=200, value=50)
+    num_beams  = st.slider("Beam search width",  min_value=1,  max_value=8,   value=4)
+    do_sample  = st.checkbox("Enable sampling",  value=False)
+    top_k      = st.slider("Top-k sampling",    min_value=0,  max_value=100, value=50)
+    temperature= st.slider("Temperature",       min_value=0.1, max_value=2.0, value=1.0, step=0.1)
+
+# 4. Generate button
+if st.button("🔄 Generate"):
+    if not user_input.strip():
+        st.error("Please enter a prompt first.")
+    else:
+        with st.spinner("Generating…"):
+            outputs = generator(user_input)
+        # pipeline returns list of dicts with key "generated_text"
+        result = outputs[0]["generated_text"]
+        st.subheader("Output")
+        st.write(result)