app.py

import gradio as gr
import torch
import torch.nn.functional as F
from transformers import AutoTokenizer, AutoModel
import pandas as pd
import sys
import os
import shutil
from pathlib import Path
import chromadb
from chromadb.config import Settings
import uuid
import tempfile

# --- Add scripts to path ---
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "../..")))
from scripts.core.ingestion.ingest import GitCrawler
from scripts.core.ingestion.chunk import RepoChunker

# --- Configuration ---
BASELINE_MODEL = "microsoft/codebert-base"
FINETUNED_MODEL = "shubharuidas/codebert-base-code-embed-mrl-langchain-langgraph"
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
DB_DIR = Path(os.path.abspath("data/chroma_db_comparison"))
DB_DIR.mkdir(parents=True, exist_ok=True)

print(f"Loading models on {DEVICE}...")
print("1. Loading baseline model...")
baseline_tokenizer = AutoTokenizer.from_pretrained(BASELINE_MODEL)
baseline_model = AutoModel.from_pretrained(BASELINE_MODEL)
baseline_model.to(DEVICE)
baseline_model.eval()

print("2. Loading fine-tuned model...")
finetuned_tokenizer = AutoTokenizer.from_pretrained(FINETUNED_MODEL)
finetuned_model = AutoModel.from_pretrained(FINETUNED_MODEL)
finetuned_model.to(DEVICE)
finetuned_model.eval()
print("Both models loaded!")

# --- ChromaDB Setup ---
chroma_client = chromadb.PersistentClient(path=str(DB_DIR))
baseline_collection = chroma_client.get_or_create_collection(name="baseline_rag", metadata={"hnsw:space": "cosine"})
finetuned_collection = chroma_client.get_or_create_collection(name="finetuned_rag", metadata={"hnsw:space": "cosine"})

# --- Embedding Functions ---
def compute_baseline_embeddings(text_list):
    if not text_list: return None
    inputs = baseline_tokenizer(text_list, return_tensors="pt", padding=True, truncation=True, max_length=512).to(DEVICE)
    with torch.no_grad():
        out = baseline_model(**inputs)
        emb = out.last_hidden_state.mean(dim=1)
        return F.normalize(emb, p=2, dim=1)

def compute_finetuned_embeddings(text_list):
    if not text_list: return None
    inputs = finetuned_tokenizer(text_list, return_tensors="pt", padding=True, truncation=True, max_length=512).to(DEVICE)
    with torch.no_grad():
        out = finetuned_model(**inputs)
        emb = out.last_hidden_state.mean(dim=1)
        return F.normalize(emb, p=2, dim=1)

# --- Reset Functions ---
def reset_baseline():
    chroma_client.delete_collection("baseline_rag")
    global baseline_collection
    baseline_collection = chroma_client.get_or_create_collection(name="baseline_rag", metadata={"hnsw:space": "cosine"})
    return "Baseline database reset."

def reset_finetuned():
    chroma_client.delete_collection("finetuned_rag")
    global finetuned_collection
    finetuned_collection = chroma_client.get_or_create_collection(name="finetuned_rag", metadata={"hnsw:space": "cosine"})
    return "Fine-tuned database reset."

# --- Database Inspector Functions ---
def list_baseline_files():
    count = baseline_collection.count()
    if count == 0:
        return [["No data indexed yet", "-", "-"]]
    
    try:
        data = baseline_collection.get(limit=min(count, 1000), include=["metadatas"])
        file_stats = {}
        for meta in data['metadatas']:
            fname = meta.get("file_name", "unknown")
            url = meta.get("url", "unknown")
            if fname not in file_stats:
                file_stats[fname] = {"count": 0, "url": url}
            file_stats[fname]["count"] += 1
        
        results = [[fname, stats["count"], stats["url"]] for fname, stats in file_stats.items()]
        return sorted(results, key=lambda x: x[1], reverse=True)
    except Exception as e:
        return [[f"Error: {str(e)}", "-", "-"]]

def list_finetuned_files():
    count = finetuned_collection.count()
    if count == 0:
        return [["No data indexed yet", "-", "-"]]
    
    try:
        data = finetuned_collection.get(limit=min(count, 1000), include=["metadatas"])
        file_stats = {}
        for meta in data['metadatas']:
            fname = meta.get("file_name", "unknown")
            url = meta.get("url", "unknown")
            if fname not in file_stats:
                file_stats[fname] = {"count": 0, "url": url}
            file_stats[fname]["count"] += 1
        
        results = [[fname, stats["count"], stats["url"]] for fname, stats in file_stats.items()]
        return sorted(results, key=lambda x: x[1], reverse=True)
    except Exception as e:
        return [[f"Error: {str(e)}", "-", "-"]]

# --- Search Functions ---
def search_baseline(query, top_k=5):
    if baseline_collection.count() == 0: return []
    query_emb = compute_baseline_embeddings([query])
    if query_emb is None: return []
    query_vec = query_emb.cpu().numpy().tolist()[0]
    results = baseline_collection.query(query_embeddings=[query_vec], n_results=min(top_k, baseline_collection.count()), include=["metadatas", "documents", "distances"])
    output = []
    if results['ids']:
        for i in range(len(results['ids'][0])):
            meta = results['metadatas'][0][i]
            code = results['documents'][0][i]
            dist = results['distances'][0][i]
            score = 1 - dist
            output.append([meta.get("file_name", "unknown"), f"{score:.4f}", code[:300] + "..."])
    return output

def search_finetuned(query, top_k=5):
    if finetuned_collection.count() == 0: return []
    query_emb = compute_finetuned_embeddings([query])
    if query_emb is None: return []
    query_vec = query_emb.cpu().numpy().tolist()[0]
    results = finetuned_collection.query(query_embeddings=[query_vec], n_results=min(top_k, finetuned_collection.count()), include=["metadatas", "documents", "distances"])
    output = []
    if results['ids']:
        for i in range(len(results['ids'][0])):
            meta = results['metadatas'][0][i]
            code = results['documents'][0][i]
            dist = results['distances'][0][i]
            score = 1 - dist
            output.append([meta.get("file_name", "unknown"), f"{score:.4f}", code[:300] + "..."])
    return output

def search_comparison(query, top_k=5):
    baseline_results = search_baseline(query, top_k)
    finetuned_results = search_finetuned(query, top_k)
    return baseline_results, finetuned_results

# --- Ingestion Functions ---
def ingest_from_url(repo_url):
    if not repo_url.startswith("http"):
        yield "Invalid URL"
        return
    
    DATA_DIR = Path(os.path.abspath("data/raw_ingest"))
    import stat
    def remove_readonly(func, path, _):
        os.chmod(path, stat.S_IWRITE)
        func(path)
    
    try:
        if DATA_DIR.exists():
            shutil.rmtree(DATA_DIR, onerror=remove_readonly)
        
        yield f"Cloning {repo_url}..."
        crawler = GitCrawler(cache_dir=DATA_DIR)
        repo_path = crawler.clone_repository(repo_url)
        if not repo_path:
            yield "Failed to clone repository."
            return
        
        yield "Listing files..."
        files = crawler.list_files(repo_path, extensions={'.py', '.md', '.json', '.js', '.ts', '.java', '.cpp'})
        if isinstance(files, tuple): files = [f.path for f in files[0]]
        
        total_files = len(files)
        yield f"Found {total_files} files. Chunking..."
        
        chunker = RepoChunker()
        all_chunks = []
        
        for i, file_path in enumerate(files):
            yield f"Chunking: {i+1}/{total_files} ({file_path.name})"
            try:
                meta = {"file_name": file_path.name, "url": repo_url}
                file_chunks = chunker.chunk_file(file_path, repo_metadata=meta)
                all_chunks.extend(file_chunks)
            except Exception as e:
                print(f"Skipping {file_path}: {e}")
        
        if not all_chunks:
            yield "No valid chunks found."
            return
        
        total_chunks = len(all_chunks)
        yield f"Generated {total_chunks} chunks. Embedding (BASELINE)..."
        
        batch_size = 64
        # Index with baseline
        for i in range(0, total_chunks, batch_size):
            batch = all_chunks[i:i+batch_size]
            texts = [c.code for c in batch]
            ids = [str(uuid.uuid4()) for _ in batch]
            metadatas = [{"file_name": Path(c.file_path).name, "url": repo_url} for c in batch]
            
            embeddings = compute_baseline_embeddings(texts)
            if embeddings is not None:
                baseline_collection.add(ids=ids, embeddings=embeddings.cpu().numpy().tolist(), metadatas=metadatas, documents=texts)
            yield f"Baseline: {min(i+batch_size, total_chunks)}/{total_chunks}"
        
        yield f"Embedding (FINE-TUNED)..."
        # Index with fine-tuned
        for i in range(0, total_chunks, batch_size):
            batch = all_chunks[i:i+batch_size]
            texts = [c.code for c in batch]
            ids = [str(uuid.uuid4()) for _ in batch]
            metadatas = [{"file_name": Path(c.file_path).name, "url": repo_url} for c in batch]
            
            embeddings = compute_finetuned_embeddings(texts)
            if embeddings is not None:
                finetuned_collection.add(ids=ids, embeddings=embeddings.cpu().numpy().tolist(), metadatas=metadatas, documents=texts)
            yield f"Fine-tuned: {min(i+batch_size, total_chunks)}/{total_chunks}"
        
        yield f"SUCCESS! Indexed {total_chunks} chunks in both databases."
    except Exception as e:
        import traceback
        traceback.print_exc()
        yield f"Error: {str(e)}"

def ingest_from_files(files):
    if not files or len(files) == 0:
        yield "No files uploaded."
        return
    
    try:
        yield f"Processing {len(files)} file(s)..."
        
        chunker = RepoChunker()
        all_chunks = []
        
        for i, file in enumerate(files):
            yield f"Chunking file {i+1}/{len(files)}: {Path(file.name).name}"
            try:
                # Gradio file upload: file.name contains the temp path
                file_path = Path(file.name)
                meta = {"file_name": file_path.name, "url": "uploaded"}
                file_chunks = chunker.chunk_file(file_path, repo_metadata=meta)
                all_chunks.extend(file_chunks)
            except Exception as e:
                yield f"Error chunking {Path(file.name).name}: {str(e)}"
                import traceback
                traceback.print_exc()

        
        if not all_chunks:
            yield "No valid chunks found."
            return
        
        total_chunks = len(all_chunks)
        yield f"Generated {total_chunks} chunks. Embedding (BASELINE)..."
        
        batch_size = 64
        for i in range(0, total_chunks, batch_size):
            batch = all_chunks[i:i+batch_size]
            texts = [c.code for c in batch]
            ids = [str(uuid.uuid4()) for _ in batch]
            metadatas = [{"file_name": Path(c.file_path).name, "url": "uploaded"} for c in batch]
            
            embeddings = compute_baseline_embeddings(texts)
            if embeddings is not None:
                baseline_collection.add(ids=ids, embeddings=embeddings.cpu().numpy().tolist(), metadatas=metadatas, documents=texts)
            yield f"Baseline: {min(i+batch_size, total_chunks)}/{total_chunks}"
        
        yield f"Embedding (FINE-TUNED)..."
        for i in range(0, total_chunks, batch_size):
            batch = all_chunks[i:i+batch_size]
            texts = [c.code for c in batch]
            ids = [str(uuid.uuid4()) for _ in batch]
            metadatas = [{"file_name": Path(c.file_path).name, "url": "uploaded"} for c in batch]
            
            embeddings = compute_finetuned_embeddings(texts)
            if embeddings is not None:
                finetuned_collection.add(ids=ids, embeddings=embeddings.cpu().numpy().tolist(), metadatas=metadatas, documents=texts)
            yield f"Fine-tuned: {min(i+batch_size, total_chunks)}/{total_chunks}"
        
        yield f"SUCCESS! Indexed {total_chunks} chunks from uploaded files."
    except Exception as e:
        import traceback
        traceback.print_exc()
        yield f"Error: {str(e)}"

# --- Analysis & Evaluation Functions ---
def analyze_embeddings_baseline():
    count = baseline_collection.count()
    if count < 5:
        return "Not enough data (Need > 5 chunks).", None
    
    try:
        limit = min(count, 2000)
        data = baseline_collection.get(limit=limit, include=["embeddings", "metadatas"])
        
        X = torch.tensor(data['embeddings'])
        X_mean = torch.mean(X, 0)
        X_centered = X - X_mean
        U, S, V = torch.pca_lowrank(X_centered, q=2)
        projected = torch.matmul(X_centered, V[:, :2]).numpy()
        
        indices = torch.randint(0, len(X), (min(100, len(X)),))
        sample = X[indices]
        sim_matrix = torch.mm(sample, sample.t())
        mask = ~torch.eye(len(sample), dtype=bool)
        avg_sim = sim_matrix[mask].mean().item()
        diversity_score = 1.0 - avg_sim
        
        metrics = (
            f"BASELINE MODEL\n"
            f"Total Chunks: {count}\n"
            f"Analyzed: {len(X)}\n"
            f"Diversity Score: {diversity_score:.4f}\n"
            f"Avg Similarity: {avg_sim:.4f}"
        )
        
        plot_df = pd.DataFrame({
            "x": projected[:, 0],
            "y": projected[:, 1],
            "topic": [m.get("file_name", "unknown") for m in data['metadatas']]
        })
        
        import matplotlib.pyplot as plt
        import io
        from PIL import Image
        
        # Create matplotlib figure with proper spacing
        fig, ax = plt.subplots(figsize=(10, 8))
        fig.subplots_adjust(top=0.92)  # Add space for title
        
        # Plot each file with different color
        unique_topics = plot_df["topic"].unique()
        for topic in unique_topics:
            mask = plot_df["topic"] == topic
            ax.scatter(plot_df[mask]["x"], plot_df[mask]["y"], label=topic, alpha=0.6, s=50)
        
        ax.set_xlabel("PC1")
        ax.set_ylabel("PC2")
        ax.set_title("Baseline Semantic Space (PCA)", fontsize=14, pad=20)
        ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left', fontsize=8)
        ax.grid(True, alpha=0.3)
        plt.tight_layout()
        
        # Convert to image for Gradio
        buf = io.BytesIO()
        plt.savefig(buf, format='png', dpi=100, bbox_inches='tight')
        buf.seek(0)
        img = Image.open(buf)
        plt.close()
        
        return metrics, img
    except Exception as e:
        import traceback
        traceback.print_exc()
        return f"Error: {e}", None

def analyze_embeddings_finetuned():
    count = finetuned_collection.count()
    if count < 5:
        return "Not enough data (Need > 5 chunks).", None
    
    try:
        limit = min(count, 2000)
        data = finetuned_collection.get(limit=limit, include=["embeddings", "metadatas"])
        
        X = torch.tensor(data['embeddings'])
        X_mean = torch.mean(X, 0)
        X_centered = X - X_mean
        U, S, V = torch.pca_lowrank(X_centered, q=2)
        projected = torch.matmul(X_centered, V[:, :2]).numpy()
        
        indices = torch.randint(0, len(X), (min(100, len(X)),))
        sample = X[indices]
        sim_matrix = torch.mm(sample, sample.t())
        mask = ~torch.eye(len(sample), dtype=bool)
        avg_sim = sim_matrix[mask].mean().item()
        diversity_score = 1.0 - avg_sim
        
        metrics = (
            f"FINE-TUNED MODEL\n"
            f"Total Chunks: {count}\n"
            f"Analyzed: {len(X)}\n"
            f"Diversity Score: {diversity_score:.4f}\n"
            f"Avg Similarity: {avg_sim:.4f}"
        )
        
        plot_df = pd.DataFrame({
            "x": projected[:, 0],
            "y": projected[:, 1],
            "topic": [m.get("file_name", "unknown") for m in data['metadatas']]
        })
        
        import matplotlib.pyplot as plt
        import io
        from PIL import Image
        
        # Create matplotlib figure with proper spacing
        fig, ax = plt.subplots(figsize=(10, 8))
        fig.subplots_adjust(top=0.92)  # Add space for title
        
        # Plot each file with different color
        unique_topics = plot_df["topic"].unique()
        for topic in unique_topics:
            mask = plot_df["topic"] == topic
            ax.scatter(plot_df[mask]["x"], plot_df[mask]["y"], label=topic, alpha=0.6, s=50)
        
        ax.set_xlabel("PC1")
        ax.set_ylabel("PC2")
        ax.set_title("Fine-tuned Semantic Space (PCA)", fontsize=14, pad=20)
        ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left', fontsize=8)
        ax.grid(True, alpha=0.3)
        plt.tight_layout()
        
        # Convert to image for Gradio
        buf = io.BytesIO()
        plt.savefig(buf, format='png', dpi=100, bbox_inches='tight')
        buf.seek(0)
        img = Image.open(buf)
        plt.close()
        
        return metrics, img
    except Exception as e:
        import traceback
        traceback.print_exc()
        return f"Error: {e}", None

def evaluate_retrieval_baseline(sample_limit):
    count = baseline_collection.count()
    if count < 10: return "Not enough data for evaluation (Need > 10 chunks)."
    
    try:
        fetch_limit = min(count, 2000)
        data = baseline_collection.get(limit=fetch_limit, include=["documents"])
        
        import random
        actual_sample_size = min(sample_limit, len(data['ids']))
        sample_indices = random.sample(range(len(data['ids'])), actual_sample_size)
        
        hits_at_1 = 0
        hits_at_5 = 0
        mrr_sum = 0
        
        yield f"BASELINE: Evaluating {actual_sample_size} chunks..."
        
        for i, idx in enumerate(sample_indices):
            target_id = data['ids'][idx]
            code = data['documents'][idx]
            query = "\n".join(code.split("\n")[:3])
            query_emb = compute_baseline_embeddings([query]).cpu().numpy().tolist()[0]
            results = baseline_collection.query(query_embeddings=[query_emb], n_results=10)
            found_ids = results['ids'][0]
            if target_id in found_ids:
                rank = found_ids.index(target_id) + 1
                mrr_sum += 1.0 / rank
                if rank == 1: hits_at_1 += 1
                if rank <= 5: hits_at_5 += 1
            if i % 10 == 0:
                yield f"Baseline: {i}/{actual_sample_size}..."
        
        recall_1 = hits_at_1 / actual_sample_size
        recall_5 = hits_at_5 / actual_sample_size
        mrr = mrr_sum / actual_sample_size
        
        report = (
            f"BASELINE EVALUATION ({actual_sample_size} chunks)\n"
            f"{'='*40}\n"
            f"Recall@1: {recall_1:.4f}\n"
            f"Recall@5: {recall_5:.4f}\n"
            f"MRR:      {mrr:.4f}"
        )
        yield report
    except Exception as e:
        import traceback
        traceback.print_exc()
        yield f"Error: {e}"

def evaluate_retrieval_finetuned(sample_limit):
    count = finetuned_collection.count()
    if count < 10: return "Not enough data for evaluation (Need > 10 chunks)."
    
    try:
        fetch_limit = min(count, 2000)
        data = finetuned_collection.get(limit=fetch_limit, include=["documents"])
        
        import random
        actual_sample_size = min(sample_limit, len(data['ids']))
        sample_indices = random.sample(range(len(data['ids'])), actual_sample_size)
        
        hits_at_1 = 0
        hits_at_5 = 0
        mrr_sum = 0
        
        yield f"FINE-TUNED: Evaluating {actual_sample_size} chunks..."
        
        for i, idx in enumerate(sample_indices):
            target_id = data['ids'][idx]
            code = data['documents'][idx]
            query = "\n".join(code.split("\n")[:3])
            query_emb = compute_finetuned_embeddings([query]).cpu().numpy().tolist()[0]
            results = finetuned_collection.query(query_embeddings=[query_emb], n_results=10)
            found_ids = results['ids'][0]
            if target_id in found_ids:
                rank = found_ids.index(target_id) + 1
                mrr_sum += 1.0 / rank
                if rank == 1: hits_at_1 += 1
                if rank <= 5: hits_at_5 += 1
            if i % 10 == 0:
                yield f"Fine-tuned: {i}/{actual_sample_size}..."
        
        recall_1 = hits_at_1 / actual_sample_size
        recall_5 = hits_at_5 / actual_sample_size
        mrr = mrr_sum / actual_sample_size
        
        report = (
            f"FINE-TUNED EVALUATION ({actual_sample_size} chunks)\n"
            f"{'='*40}\n"
            f"Recall@1: {recall_1:.4f}\n"
            f"Recall@5: {recall_5:.4f}\n"
            f"MRR:      {mrr:.4f}"
        )
        yield report
    except Exception as e:
        import traceback
        traceback.print_exc()
        yield f"Error: {e}"

# --- UI ---
theme = gr.themes.Soft(primary_hue="slate", neutral_hue="slate", spacing_size="sm", radius_size="md").set(body_background_fill="*neutral_50", block_background_fill="white", block_border_width="1px", block_title_text_weight="600")

css = """
h1 { text-align: center; font-family: 'Inter', sans-serif; margin-bottom: 1rem; color: #1e293b; }
.gradio-container { max-width: 1400px !important; margin: auto; }
.comparison-header { font-size: 1.1rem; font-weight: 600; color: #334155; text-align: center; padding: 0.5rem; }
"""

with gr.Blocks(theme=theme, css=css, title="CodeMode - Baseline vs Fine-tuned") as demo:
    gr.Markdown("# CodeMode: Baseline vs Fine-tuned Model Comparison")
    gr.Markdown("Compare retrieval performance between **microsoft/codebert-base** (baseline) and **MRL-enhanced fine-tuned** model")
    
    with gr.Tabs():
        # TAB 1: INGEST
        with gr.Tab("1. Ingest Code"):
            with gr.Tabs():
                with gr.Tab("GitHub Repository"):
                    repo_input = gr.Textbox(label="GitHub URL", placeholder="https://github.com/pallets/flask")
                    ingest_url_btn = gr.Button("Ingest from URL", variant="primary")
                    url_status = gr.Textbox(label="Status")
                    ingest_url_btn.click(ingest_from_url, inputs=repo_input, outputs=url_status)
                
                with gr.Tab("Upload Python Files"):
                    file_upload = gr.File(label="Upload .py files", file_types=[".py"], file_count="multiple")
                    ingest_files_btn = gr.Button("Ingest Uploaded Files", variant="primary")
                    upload_status = gr.Textbox(label="Status")
                    ingest_files_btn.click(ingest_from_files, inputs=file_upload, outputs=upload_status)
            
            with gr.Row():
                reset_baseline_btn = gr.Button("Reset Baseline DB", variant="stop")
                reset_finetuned_btn = gr.Button("Reset Fine-tuned DB", variant="stop")
                reset_status = gr.Textbox(label="Reset Status")
            
            reset_baseline_btn.click(reset_baseline, inputs=[], outputs=reset_status)
            reset_finetuned_btn.click(reset_finetuned, inputs=[], outputs=reset_status)
            
            gr.Markdown("---")
            gr.Markdown("### Database Inspector")
            gr.Markdown("View indexed files in each collection")
            
            with gr.Row():
                with gr.Column():
                    gr.Markdown("#### Baseline Collection")
                    inspect_baseline_btn = gr.Button("Inspect Baseline DB", variant="secondary")
                    baseline_files_df = gr.Dataframe(
                        headers=["File Name", "Chunks", "Source URL"],
                        datatype=["str", "number", "str"],
                        interactive=False,
                        value=[["No data yet", "-", "-"]]
                    )
                    inspect_baseline_btn.click(list_baseline_files, inputs=[], outputs=baseline_files_df)
                
                with gr.Column():
                    gr.Markdown("#### Fine-tuned Collection")
                    inspect_finetuned_btn = gr.Button("Inspect Fine-tuned DB", variant="secondary")
                    finetuned_files_df = gr.Dataframe(
                        headers=["File Name", "Chunks", "Source URL"],
                        datatype=["str", "number", "str"],
                        interactive=False,
                        value=[["No data yet", "-", "-"]]
                    )
                    inspect_finetuned_btn.click(list_finetuned_files, inputs=[], outputs=finetuned_files_df)
        
        # TAB 2: COMPARISON SEARCH
        with gr.Tab("2. Comparison Search (Note: Semantic search is sensitive to query phrasing)"):
            gr.Markdown("### Side-by-Side Retrieval Comparison")
            search_query = gr.Textbox(label="Search Query", placeholder="e.g., 'Flask route decorator'")
            compare_btn = gr.Button("Compare Models", variant="primary")
            
            with gr.Row():
                with gr.Column():
                    gr.Markdown("<div class='comparison-header'>BASELINE (CodeBERT)</div>", elem_classes="comparison-header")
                    baseline_results = gr.Dataframe(headers=["File", "Score", "Code Snippet"], datatype=["str", "str", "str"], interactive=False, wrap=True)
                
                with gr.Column():
                    gr.Markdown("<div class='comparison-header'>FINE-TUNED (MRL-Enhanced)</div>", elem_classes="comparison-header")
                    finetuned_results = gr.Dataframe(headers=["File", "Score", "Code Snippet"], datatype=["str", "str", "str"], interactive=False, wrap=True)
            
            compare_btn.click(search_comparison, inputs=search_query, outputs=[baseline_results, finetuned_results])
        
        
        # TAB 3: CODE SIMILARITY SEARCH
        with gr.Tab("3. Code Similarity Search"):
            gr.Markdown("### Find Similar Code Snippets")
            gr.Markdown("Paste a code snippet to find similar code in the database")
            
            with gr.Row():
                with gr.Column():
                    code_input = gr.Code(label="Paste Code Snippet", language="python", lines=10)
                    similarity_btn = gr.Button("Find Similar Code", variant="primary")
                
                with gr.Column():
                    gr.Markdown("#### Search Settings")
                    top_k_slider = gr.Slider(minimum=1, maximum=20, value=5, step=1, label="Number of Results")
                    model_choice = gr.Radio(["Baseline", "Fine-tuned", "Both"], value="Both", label="Model to Use")
            
            gr.Markdown("### Results")
            
            with gr.Row():
                with gr.Column():
                    gr.Markdown("#### Baseline Results")
                    baseline_code_results = gr.Dataframe(
                        headers=["File", "Similarity", "Code Snippet"],
                        datatype=["str", "str", "str"],
                        interactive=False,
                        wrap=True,
                        value=[["No search yet", "-", "-"]]
                    )
                
                with gr.Column():
                    gr.Markdown("#### Fine-tuned Results")
                    finetuned_code_results = gr.Dataframe(
                        headers=["File", "Similarity", "Code Snippet"],
                        datatype=["str", "str", "str"],
                        interactive=False,
                        wrap=True,
                        value=[["No search yet", "-", "-"]]
                    )
            
            def search_similar_code(code_snippet, top_k, model_choice):
                if not code_snippet or len(code_snippet.strip()) == 0:
                    empty = [["Enter code to search", "-", "-"]]
                    return empty, empty
                
                baseline_res = []
                finetuned_res = []
                
                if model_choice in ["Baseline", "Both"]:
                    baseline_res = search_baseline(code_snippet, top_k)
                    if not baseline_res:
                        baseline_res = [["No results found", "-", "-"]]
                
                if model_choice in ["Fine-tuned", "Both"]:
                    finetuned_res = search_finetuned(code_snippet, top_k)
                    if not finetuned_res:
                        finetuned_res = [["No results found", "-", "-"]]
                
                if model_choice == "Baseline":
                    finetuned_res = [["Not searched", "-", "-"]]
                elif model_choice == "Fine-tuned":
                    baseline_res = [["Not searched", "-", "-"]]
                
                return baseline_res, finetuned_res
            
            similarity_btn.click(
                search_similar_code,
                inputs=[code_input, top_k_slider, model_choice],
                outputs=[baseline_code_results, finetuned_code_results]
            )
        
        # TAB 4: DEPLOYMENT MONITORING
        with gr.Tab("4. Deployment Monitoring"):
            gr.Markdown("### Embedding Quality Analysis")
            gr.Markdown("Analyze the semantic space distribution and diversity of embeddings")
            
            with gr.Row():
                with gr.Column():
                    gr.Markdown("#### Baseline Model")
                    analyze_baseline_btn = gr.Button("Analyze Baseline Embeddings", variant="secondary")
                    baseline_metrics = gr.Textbox(label="Baseline Metrics")
                    baseline_plot = gr.Image()
                    analyze_baseline_btn.click(analyze_embeddings_baseline, inputs=[], outputs=[baseline_metrics, baseline_plot])
                
                with gr.Column():
                    gr.Markdown("#### Fine-tuned Model")
                    analyze_finetuned_btn = gr.Button("Analyze Fine-tuned Embeddings", variant="secondary")
                    finetuned_metrics = gr.Textbox(label="Fine-tuned Metrics")
                    finetuned_plot = gr.Image()
                    analyze_finetuned_btn.click(analyze_embeddings_finetuned, inputs=[], outputs=[finetuned_metrics, finetuned_plot])
            
            gr.Markdown("---")
            gr.Markdown("### Retrieval Performance Evaluation")
            gr.Markdown("Evaluate retrieval accuracy using synthetic queries (query = first 3 lines of code)")
            
            eval_size = gr.Slider(minimum=10, maximum=500, value=50, step=10, label="Sample Size (Chunks to Evaluate)")
            
            with gr.Row():
                with gr.Column():
                    gr.Markdown("#### Baseline Evaluation")
                    eval_baseline_btn = gr.Button("Run Baseline Evaluation", variant="primary")
                    baseline_eval_output = gr.Textbox(label="Baseline Results")
                    eval_baseline_btn.click(evaluate_retrieval_baseline, inputs=[eval_size], outputs=baseline_eval_output)
                
                with gr.Column():
                    gr.Markdown("#### Fine-tuned Evaluation")
                    eval_finetuned_btn = gr.Button("Run Fine-tuned Evaluation", variant="primary")
                    finetuned_eval_output = gr.Textbox(label="Fine-tuned Results")
                    eval_finetuned_btn.click(evaluate_retrieval_finetuned, inputs=[eval_size], outputs=finetuned_eval_output)

if __name__ == "__main__":
    demo.queue().launch(server_name="0.0.0.0", server_port=7860, share=False)