Update app.py

app.py

@@ -1,31 +1,9 @@
-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.error(f"Error loading model: {e}. Please verify the model name or your internet connection.")
     st.stop()
 
 # Input Section
@@ -47,42 +25,20 @@ if st.button("Generate Output"):
             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":
+# Handling Code Optimization:
+if 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).")
+        if user_code.strip() == "":
+            st.warning("Please paste valid Python code to optimize.")
+        else:
+            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 while optimizing code: {e}")