Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,29 @@
-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])),
-    ))
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# Load model
+MODEL_NAME = "Vinushaanth/metaphor-create-withoutlora"  # replace with your HF model repo
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+model = AutoModelForCausalLM.from_pretrained(MODEL_NAME)
+
+# Streamlit app
+st.set_page_config(page_title="Metaphor Assistant", layout="wide")
+
+st.title("✨ Metaphor & Lyric Assistant")
+st.write("Generate poetic Tamil lines with figurative language.")
+
+# Input form
+prompt = st.text_area("Enter your phrase or theme:", height=120)
+
+max_len = st.slider("Max Output Length", 30, 200, 80)
+
+if st.button("Generate"):
+    if prompt.strip() == "":
+        st.warning("Please enter some text.")
+    else:
+        with st.spinner("Generating..."):
+            inputs = tokenizer(prompt, return_tensors="pt")
+            outputs = model.generate(**inputs, max_length=max_len, do_sample=True, top_p=0.9, temperature=0.7)
+            result = tokenizer.decode(outputs[0], skip_special_tokens=True)
+        st.success("✅ Generated Text")
+        st.write(result)