Moved Streamlit code to src/streamlit_app.py for proper Space launch

src/streamlit_app.py

@@ -1,40 +1,22 @@
-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 requests
+import os
+
+st.title("My Hugging Face Model Interface")
+
+input_text = st.text_area("Enter your text:", "")
+HF_TOKEN = os.getenv("HF_TOKEN")  # Loaded from Hugging Face Secrets
+
+if st.button("Get Prediction") and input_text:
+    headers = {"Authorization": f"Bearer {HF_TOKEN}"} if HF_TOKEN else {}
+    response = requests.post(
+        "https://api-inference.huggingface.co/models/rajan3208/uzmi-gpt",
+        headers=headers,
+        json={"inputs": input_text}
+    )
+
+    if response.status_code == 200:
+        st.success("Prediction:")
+        st.write(response.json()[0]['generated_text'])
+    else:
+        st.error(f"Error {response.status_code}: {response.text}")