Upload 3 files

README.md

@@ -1,19 +1,19 @@
 ---
 title: Code Explainer 💡
 emoji: 🧠
-colorFrom: indigo
-colorTo: purple
+colorFrom: green
+colorTo: blue
 sdk: streamlit
 sdk_version: "1.35.0"
 app_file: streamlit_app.py
 pinned: false
 ---
 
-# 🧠 Code Explainer (Codet5 Version – Prompt Fixed)
+# 🧠 Code Explainer (CodeBERT Version)
 
-This app uses `Salesforce/codet5-base` to explain Python code snippets in plain English using task prompts.
+This app uses `mrm8488/codebert-base-finetuned-stackoverflow` to analyze and classify Python code.
 
-## ✅ How to Use
-1. Paste Python code in the box
+## How to Use
+1. Paste Python code
 2. Click "Explain"
-3. Get step-by-step plain English explanation
+3. Get an AI-generated summary or label

streamlit_app.py

@@ -1,11 +1,10 @@
 import streamlit as st
 from model.model_utils import load_model, generate_explanation
-from prompt_utils import build_prompt
 
 st.set_page_config(page_title="Code Explainer", layout="centered")
 
-st.title("🧠 Code Explainer")
-st.write("Paste your Python code below and get a plain English explanation:")
+st.title("🧠 Code Explainer (CodeBERT)")
+st.write("Paste your Python code below and get a natural language explanation:")
 
 code_input = st.text_area("Paste Python Code", height=200)
 
@@ -13,15 +12,8 @@ if st.button("Explain"):
     if code_input.strip():
         with st.spinner("Generating explanation..."):
             tokenizer, model, device = load_model()
-            prompt = build_prompt(code_input)
-            st.subheader("🔍 Prompt Sent to Model")
-            st.code(prompt)
-
-            explanation = generate_explanation(prompt, tokenizer, model, device)
-            st.subheader("📝 Raw Output from Model")
-            st.code(explanation)
-
-            st.subheader("✅ Final Explanation")
+            explanation = generate_explanation(code_input, tokenizer, model, device)
+            st.subheader("✅ Explanation")
             st.write(explanation)
     else:
         st.warning("Please paste some code to explain.")