app.py

import streamlit as st
from codeInsight.pipeline.prediction_pipeline import PredictionPipeline
from codeInsight.logger import logging

st.set_page_config(
    page_title="CodeInsight Assistant",
    page_icon="🤖",
    layout="wide"
)

@st.cache_resource
def load_pipeline():
    try:
        pipeline = PredictionPipeline()
        return pipeline
    except Exception as e:
        logging.error("Failed to load pipeline in Streamlit app")
        st.error(f"Failed to load model pipeline: {e}")
        return None

pipeline = load_pipeline()

st.title("🤖 CodeInsight Assistant")
st.caption("Fine-tuned Phi-3-mini-128k-instruct model, ready to help with Python.")
st.divider()

st.markdown(
    "Welcome! This assistant is powered by a **fine-tuned Phi-3-mini-128k-instruct model** "
    "to help you with Python programming tasks. Ask it to generate code, "
    "explain concepts, or refactor snippets."
)
col1, col2 = st.columns(2)

with col1:
    st.subheader("🚀 What it can do")
    st.markdown("""
    * **Generate Code:** "Write a function to merge two dictionaries."
    * **Refactor/Debug:** "Can you make this 'for' loop more efficient?"
    """)

with col2:
    st.subheader("⚠️ Important Limitations")
    st.warning("""
    * The model may occasionally produce incorrect or inefficient code.
    * Knowledge is limited to the model's training data.
    """)
st.divider()

if pipeline:
    if "messages" not in st.session_state:
        st.session_state.messages = [
            {"role": "assistant", "content": "Hello! How can I help you with Python programming today?"}
        ]

    for message in st.session_state.messages:
        with st.chat_message(message["role"]):
            st.markdown(message["content"])

    prompt = st.chat_input("Ask me to write python code")

    if prompt:
        st.session_state.messages.append({"role": "user", "content": prompt})
        with st.chat_message("user"):
            st.markdown(prompt)

        with st.chat_message("assistant"):
            with st.spinner("Thinking..."):
                response = pipeline.predict(prompt)
                cleaned_response = response.replace(
                    "You are a senior Python developer. Provide clear, correct, well-commented code.", ""
                ).strip()
                formatted_response = f"```python\n{cleaned_response}\n```"
                st.markdown(formatted_response)

        st.session_state.messages.append({"role": "assistant", "content": formatted_response})

else:
    st.error("The prediction pipeline could not be loaded. Please check the logs.")