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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).")
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