Create app.py

app.py

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+!pip install streamlit
+import streamlit as st
+from transformers import pipeline
+import torch
+
+# Check if CUDA is available and set the device accordingly
+# device = 0 if torch.cuda.is_available() else -1
+
+# Initialize the Phi model pipeline
+@st.cache_resource
+def load_model():
+    return pipeline(
+        "text-generation",
+        model="microsoft/phi-2",
+        torch_dtype=torch.bfloat16,
+        # device=device,
+    )
+
+phi_model = load_model()
+
+# Function to generate response
+def generate_response(prompt, max_length=512):
+    response = phi_model(prompt, max_length=max_length, do_sample=True, temperature=0.7)
+    return response[0]['generated_text']
+
+# Streamlit UI
+st.title("Chain of Thought vs Traditional Reasoning - Phi Model")
+
+# User input
+user_question = st.text_input("Enter your question:")
+
+if user_question:
+    # Generate responses
+    with st.spinner("Generating responses..."):
+        traditional_prompt = f"Question: {user_question}\nAnswer:"
+        cot_prompt = f"Question: {user_question}\nLet's approach this step by step:\n1)"
+
+        traditional_response = generate_response(traditional_prompt)
+        cot_response = generate_response(cot_prompt)
+
+    # Display results
+    st.subheader("Traditional Output")
+    st.write(traditional_response)
+
+    st.subheader("Chain of Thought Reasoning")
+    st.write(cot_response)
+
+# Add explanatory text
+st.markdown("""
+## About this demo
+This demo showcases the difference between traditional output and chain of thought (CoT) reasoning using the Phi-2 model from Microsoft.
+
+- **Traditional Output**: Provides a direct answer to the question.
+- **Chain of Thought Reasoning**: Shows the step-by-step thought process leading to the answer.
+
+CoT reasoning often results in more detailed and transparent explanations, which can be helpful for complex problems or when understanding the reasoning process is important.
+""")