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*")