Update app.py

Dockerfile

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+FROM python:3.9-slim
+
+# Set working directory
+WORKDIR /app
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y build-essential
+
+# Copy requirements and install Python packages
+COPY requirements.txt .
+RUN pip install --upgrade pip
+RUN pip install -r requirements.txt
+
+# Copy the rest of the app
+COPY . .
+
+# Expose Streamlit port
+EXPOSE 8501
+
+# Run the app
+CMD ["streamlit", "run", "app.py", "--server.port=8501", "--server.address=0.0.0.0"]

app.py

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+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)

requirements.txt

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+transformers==4.52.3
+torch==2.7.0
+streamlit==1.45.1
+nltk