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import streamlit as st
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import os
# Initialize Hugging Face model and tokenizer
MODEL_NAME = "microsoft/codebert-base"
# Load the pre-trained CodeBERT model for understanding code
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME)
# Helper function to analyze code
def analyze_code(code):
# Split the code into manageable chunks
max_length = 512
lines = code.split("\n")
chunks = ["\n".join(lines[i:i+max_length]) for i in range(0, len(lines), max_length)]
results = []
for chunk in chunks:
tokenized_code = tokenizer(chunk, return_tensors="pt", truncation=True, max_length=max_length)
outputs = model(**tokenized_code)
logits = outputs.logits
results.append(logits.argmax(dim=1).item())
return results
# Function to simulate quality and bug detection
# Note: Replace with a real analysis pipeline.
def check_code_quality_and_bugs(code):
suggestions = []
# Example: Check for readability (placeholder logic)
if len(code.split("\n")) < 5:
suggestions.append("Code seems too short, ensure functionality is implemented correctly.")
# Example: Check for comments (placeholder logic)
if "#" not in code:
suggestions.append("Consider adding comments to improve code clarity.")
# Example: Check for style guide adherence (placeholder logic)
if "import" in code and "os" not in code:
suggestions.append("Unused imports detected; consider removing them.")
# Example: Detect code smells (placeholder logic)
if "try" in code and "except" not in code:
suggestions.append("`try` block without `except` may lead to unhandled exceptions.")
return suggestions
# Streamlit app UI
st.title("Code Quality and Bug Detection Tool")
st.markdown("Analyze your code for syntax issues, quality, and potential bugs.")
# File uploader
uploaded_file = st.file_uploader("Upload a Python code file", type=["py"])
# Code snippet input
code_snippet = st.text_area("Or paste your code snippet below:")
if st.button("Analyze Code"):
if uploaded_file is not None:
code = uploaded_file.read().decode("utf-8")
elif code_snippet.strip():
code = code_snippet
else:
st.error("Please upload a file or paste code to analyze.")
st.stop()
# Perform code analysis
st.subheader("Analysis Results")
st.write("**Code Quality and Bug Suggestions:**")
suggestions = check_code_quality_and_bugs(code)
if suggestions:
for i, suggestion in enumerate(suggestions, 1):
st.write(f"{i}. {suggestion}")
else:
st.write("No issues detected. Your code looks good!")
# Simulated CodeBERT analysis (placeholder)
st.write("**Model Analysis:**")
model_results = analyze_code(code)
for idx, result in enumerate(model_results, 1):
st.write(f"Chunk {idx} classification result: {result}")
st.markdown("---")
st.markdown("*Powered by Hugging Face and Streamlit*")
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