Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,42 +1,7 @@
-import os
-
-os.environ["MPLCONFIGDIR"] = "/tmp"  # Prevent matplotlib config errors
+os.environ["MPLCONFIGDIR"] = "/tmp"
 os.environ["STREAMLIT_BROWSER_GATHER_USAGE_STATS"] = "false"
 os.environ["STREAMLIT_SERVER_HEADLESS"] = "true"
 os.environ["STREAMLIT_SERVER_ENABLE_FILE_WATCHER"] = "false"
-
+os.environ["STREAMLIT_CONFIG_DIR"] = "/tmp/.streamlit"
 os.environ["HF_HOME"] = "/tmp/huggingface"
-
-import streamlit as st
-import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-# 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)
+os.environ["TRANSFORMERS_CACHE"] = "/tmp/huggingface"