Rename src/streamlit_app.py to streamlit_app.py

src/streamlit_app.py

@@ -1,40 +0,0 @@
-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])),
-    ))

streamlit_app.py

@@ -0,0 +1,28 @@
+import streamlit as st
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+st.title("Niche AI Chat")
+st.write("Chat with your trained Niche model from Hugging Face!")
+
+# Load model once
+@st.cache_resource
+def load_model():
+    model_name = "Neon-AI/Niche"  # Your HF repo
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")
+    return tokenizer, model
+
+tokenizer, model = load_model()
+
+# User input
+prompt = st.text_input("You:", "")
+
+if st.button("Send"):
+    if prompt.strip() == "":
+        st.warning("Please type something!")
+    else:
+        inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+        outputs = model.generate(**inputs, max_new_tokens=100, do_sample=True, temperature=0.7)
+        response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+        st.text_area("Niche:", value=response, height=200)