Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,36 @@
-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 os
+os.environ["MPLCONFIGDIR"] = "/tmp"  # Prevent matplotlib config errors
+os.environ["STREAMLIT_BROWSER_GATHER_USAGE_STATS"] = "false"
+os.environ["STREAMLIT_SERVER_HEADLESS"] = "true"
+
+import streamlit as st
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+# Title and UI
+st.set_page_config(page_title="DeepSeek-R1 Chatbot", page_icon="🤖")
+st.title("🧠 DeepSeek-R1 CPU Chatbot")
+st.caption("Running entirely on CPU using Hugging Face Transformers")
+
+# Load the model and tokenizer
+@st.cache_resource
+def load_model():
+    tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-Coder-1.3B-base")
+    model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-Coder-1.3B-base")
+    return tokenizer, model
+
+
+tokenizer, model = load_model()
+
+# Prompt input
+user_input = st.text_area("📥 Enter your prompt here:", "Explain what a neural network is.")
+
+if st.button("🧠 Generate Response"):
+    with st.spinner("Thinking..."):
+        inputs = tokenizer(user_input, return_tensors="pt")
+        outputs = model.generate(**inputs, max_new_tokens=100)
+        response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+        
+        st.markdown("### 🤖 Response:")
+        st.write(response)