app.py

import streamlit as st
from transformers import pipeline

# App Title
st.set_page_config(page_title="ML Assistant with Replit LLM", layout="wide")
st.title("🤖 ML Assistant with Replit LLM")
st.write("Interact with the Replit LLM for machine learning workflows and AI-driven coding assistance.")

# Sidebar Configuration
st.sidebar.title("Configuration")
api_key = st.sidebar.text_input("Replit LLM API Key", type="password")
model_name = st.sidebar.text_input("Hugging Face Model Name", "Canstralian/RabbitRedux")
task_type = st.sidebar.selectbox(
    "Choose a Task",
    ["Text Generation", "Pseudocode to Python", "ML Debugging", "Code Optimization"]
)

# Ensure API Key is Provided
if not api_key:
    st.warning("Please provide your Replit LLM API Key in the sidebar to continue.")
    st.stop()

# Initialize Replit LLM Pipeline
try:
    nlp_pipeline = pipeline("text2text-generation", model=model_name)
    st.success("Model loaded successfully!")
except Exception as e:
    st.error(f"Error loading model: {e}")
    st.stop()

# Input Section
st.subheader("Input Your Query")
user_input = st.text_area("Enter your query or task description", height=150)

# Submit Button
if st.button("Generate Output"):
    if user_input.strip() == "":
        st.warning("Please enter a valid input.")
    else:
        with st.spinner("Processing..."):
            try:
                # Generate response using Replit LLM
                output = nlp_pipeline(user_input)
                response = output[0]["generated_text"]
                st.subheader("AI Response")
                st.write(response)
            except Exception as e:
                st.error(f"An error occurred: {e}")

# Additional ML Features
st.subheader("Advanced Machine Learning Assistance")

if task_type == "Text Generation":
    st.info("Use the input box to generate text-based output.")
elif task_type == "Pseudocode to Python":
    st.info("Provide pseudocode, and the Replit LLM will attempt to generate Python code.")
    example = st.button("Show Example")
    if example:
        st.code("""
# Pseudocode
FOR each item IN list:
    IF item > threshold:
        PRINT "Above Threshold"

# Expected Python Output
for item in my_list:
    if item > threshold:
        print("Above Threshold")
        """)
elif task_type == "ML Debugging":
    st.info("Describe your ML pipeline error for debugging suggestions.")
elif task_type == "Code Optimization":
    st.info("Paste your Python code for optimization recommendations.")
    user_code = st.text_area("Paste your Python code", height=200)
    if st.button("Optimize Code"):
        with st.spinner("Analyzing and optimizing..."):
            try:
                optimization_prompt = f"Optimize the following Python code:\n\n{user_code}"
                output = nlp_pipeline(optimization_prompt)
                optimized_code = output[0]["generated_text"]
                st.subheader("Optimized Code")
                st.code(optimized_code)
            except Exception as e:
                st.error(f"An error occurred: {e}")

# Footer
st.write("---")
st.write("Powered by [Replit LLM](https://replit.com) and [Hugging Face](https://huggingface.co).")