Update README.md

README.md

@@ -1,446 +1,10 @@
 ---
-title: Text to MongoDB Generator
-emoji: 🚀
+title: CodeGen
+emoji: 💻
 colorFrom: blue
 colorTo: purple
 sdk: gradio
 sdk_version: 5.9.1
 app_file: app.py
 pinned: false
----
-
-# CodeGen: Hybrid RAG Pipeline
-
-[![Python 3.10+](https://img.shields.io/badge/Python-3.10%2B-blue)](https://www.python.org/)
-[![PyTorch](https://img.shields.io/badge/PyTorch-2.5.1-red)](https://pytorch.org/)
-[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
-
-A capstone project (eMasters CS: AI & ML, Group 2) that fine-tunes a small open-source code language model — **Qwen2.5-Coder-0.5B-Instruct** — with **LoRA**, builds a **hybrid semantic + AST/structural retrieval (RAG)** layer on top of it, and evaluates the result against the un-adapted base model and an independently invoked LLM baseline, across text-to-SQL, documentation-generation, program-synthesis, and commit-message-generation tasks.
-
----
-
-## Table of Contents
-
-- [Executive Summary](#-executive-summary)
-- [Key Capabilities](#-key-capabilities)
-- [System Architecture](#-system-architecture)
-- [Pipeline Stages](#-pipeline-stages)
-- [Benchmark Results](#-benchmark-results)
-- [Dataset](#-dataset)
-- [Installation](#-installation)
-- [Quick Start](#-quick-start)
-- [Repository Layout](#-repository-layout)
-- [Configuration](#-configuration)
-- [Requirements](#-requirements)
-- [License](#-license)
-
----
-
-## Executive Summary
-
-CodeGen combines **Retrieval-Augmented Generation (RAG)** and **parameter-efficient LoRA fine-tuning** on top of a compact instruction-tuned code model to support three developer-facing capabilities: natural-language-to-SQL translation, automatic source-code documentation generation, and program/commit-message generation. The primary evaluation dataset is the **Spider** cross-domain text-to-SQL benchmark (1,034 dev-split examples), supplemented by small hand-curated example sets for documentation, program-synthesis, and commit-message generation.
-
-Retrieval embeddings are produced by the small code LM itself (no second embedding model needs to be downloaded), and are combined with an AST/structural fingerprint index so retrieval can be reranked on structure, not just vector similarity.
-
----
-
-## Key Capabilities
-
-| Capability | Description |
-|---|---|
-| **Multi-Task Generation** | Program synthesis, docstring generation, Text-to-SQL translation, commit-message synthesis |
-| **Code-LM Self-Embeddings** | Retrieval embeddings mean-pooled from the base model's own hidden states — no separate embedding model required (an external `BAAI/bge-small-en-v1.5` path is also available and config-switchable) |
-| **Hybrid Retrieval** | FAISS (`IndexFlatIP`) dense semantic search fused with an AST/structural fingerprint reranker (α = 0.7 dense / 0.3 structural) |
-| **LoRA Fine-Tuning** | PEFT LoRA adapter (r=16, α=32) on the attention projections, with a custom training loop (gradient checkpointing, prompt-token label masking, OOM-safe stepping) |
-| **Small-LM vs. LLM Harness** | Pluggable large-LLM baseline generator supporting local, OpenAI-compatible, and Gemini backends |
-| **Comprehensive Evaluation** | BLEU, ROUGE-1/L, token-F1, CodeBERTScore, SQL Exact Match, SQL Execution Accuracy, RAG gain-over-baseline, top-K sweep |
-
----
-
-## System Architecture
-
-```
-┌─────────────────────────────────────────────────────────────────┐
-│ 1. Configuration & Data                                         │
-│  • Load config.yaml (paths, model, LoRA, retrieval, eval)       │
-│  • Convert & normalize Spider (dev.json + tables.json + SQLite) │
-│  • Build retrieval corpus (1,034 question/SQL pairs)            │
-└──────────────────────────┬──────────────────────────────────────┘
-                           │
-┌──────────────────────────▼──────────────────────────────────────┐
-│ 2. Model Loading & LoRA Fine-Tuning                             │
-│  • Load Qwen2.5-Coder-0.5B-Instruct (tokenizer + base weights)  │
-│  • Attach LoRA adapter, train on illustrative task examples     │
-│  • Save / reload adapter checkpoint                             │
-└──────────────────────────┬──────────────────────────────────────┘
-                           │
-┌──────────────────────────▼──────────────────────────────────────┐
-│ 3. Generation Tasks                                             │
-│  • Text-to-SQL   • Documentation   • Program Synthesis          │
-│  • Commit Messages, evaluated for base model vs. LoRA model     │
-└──────────────────────────┬──────────────────────────────────────┘
-                           │
-┌──────────────────────────▼──────────────────────────────────────┐
-│ 4. Hybrid Indexing & RAG                                        │
-│  • CodeLMEmbedder → FAISS semantic index (IndexFlatIP)          │
-│  • ASTIndexer → structural fingerprints (Python AST / SQL regex)│
-│  • HybridRetriever → dense + structural blended top-K search    │
-│  • RAGPipeline → context injection + generation                 │
-└──────────────────────────┬──────────────────────────────────────┘
-                           │
-┌──────────────────────────▼──────────────────────────────────────┐
-│ 5. Evaluation & Comparison                                      │
-│  • Base vs. LoRA vs. RAG vs. LLM-baseline (ModelComparator)     │
-│  • RAG gain-over-baseline & top-K sweep (RAGEvaluator)          │
-│  • Charts (matplotlib) + JSON metrics persisted to output/      │
-└─────────────────────────────────────────────────────────────────┘
-```
-
----
-
-## Pipeline Stages
-
-The pipeline is run through a set of Jupyter notebooks rather than a single CLI entry point:
-
-| Notebook | Stage |
-|---|---|
-| `baseline_model.ipynb` | Loads the **un-adapted base model**, runs text-to-SQL and documentation generation, saves metrics to `outputs/generated/baseline_model_results.json` |
-| `finetuned_model.ipynb` | Loads/trains the **LoRA adapter**, re-runs the same two tasks, saves metrics to `outputs/generated/finetuned_model_results.json` |
-| `spider_eval.ipynb` | Spider-specific SQL execution evaluation |
-| `eval.ipynb` | RAG-gain measurement, top-K sweep, small-LM-vs-LLM comparison |
-| `main.ipynb` | Complete End-to-End pipeline |
-
-Supporting production code lives in `src/` (see [Repository Layout](#-repository-layout)) and is imported by the notebooks — it is not currently exposed as a standalone CLI script.
-
-### Task-Specific Generators (`src/generators/`)
-
-| Task | Module | Benchmark used | Primary Metrics |
-|---|---|---|---|
-| **Text-to-SQL** | `text_to_sql.py`, `sql_generator.py` | Spider (100-sample subset of the 1,034-question dev split) | Exact Match, Execution Accuracy, BLEU, CodeBERTScore |
-| **Documentation Generation** | `doc_gen.py` | 3 hand-authored illustrative examples | BLEU, ROUGE, CodeBERTScore, F1 |
-| **Program Synthesis** | `program_generator.py` | 3 hand-authored illustrative examples | Qualitative + shared metric suite |
-| **Commit Message Generation** | `commit_gen.py` | 3 hand-authored illustrative examples | Qualitative + shared metric suite |
-| **LLM Baseline** | `llm_baseline.py` | Same prompts as program synthesis | Comparative scoring against the small LM |
-
-### Hybrid Indexing & Retrieval (`src/embeddings/`)
-
-| Component | Technology | Purpose |
-|---|---|---|
-| `code_lm_embedder.py` | Mean-pooled hidden states of the base model itself | Retrieval embeddings without a second model download |
-| `embedding.py` | `BAAI/bge-small-en-v1.5` (external, config-switchable) | Alternative embedding backend |
-| `indexing.py` | FAISS `IndexFlatIP` | Dense semantic vector store (cosine similarity via normalized inner product) |
-| `ast_indexing.py` | Python `ast` module (Python code) / regex-based structural extractor (SQL) | Structural fingerprints — function/class names, or referenced tables/aggregates/clauses |
-| `hybrid_retriever.py` | Blended reranker | Over-fetches `k × 4` from FAISS, reranks with `α * dense + (1-α) * structural` |
-
-### Evaluation & Persistence (`src/evaluation/`)
-
-| Component | Function |
-|---|---|
-| `metrics.py` | BLEU, ROUGE, token-F1, CodeBERTScore, execution accuracy primitives |
-| `evaluation.py` | Task-level evaluation engine |
-| `sql_eval.py` | SQL normalization and Spider-style execution comparison |
-| `comparator.py` | Base vs. LoRA vs. RAG side-by-side comparison |
-| `rag_evaluation.py` | RAG gain-over-baseline measurement and top-K sweep |
-| `visualization.py` | Matplotlib bar-chart generation, saved to `output/plots/` |
-
----
-
-## Benchmark Results
-
-Numbers below are taken directly from `outputs/generated/baseline_model_results.json` and `finetuned_model_results.json` (100-sample Spider text-to-SQL subset, 3-sample documentation-generation set).
-
-### Baseline (un-adapted) vs. LoRA Fine-Tuned
-
-| Task | Metric | Base Model | LoRA Fine-Tuned |
-|---|---|:---:|:---:|
-| **Text-to-SQL** | BLEU | 0.2329 | **0.2467** |
-| | CodeBERTScore | 0.9740 | **0.9759** |
-| | F1 | 0.4642 | **0.4879** |
-| | ROUGE-1 | 0.4378 | **0.4602** |
-| | ROUGE-L | 0.4274 | **0.4503** |
-| | Exact Match | 7% | **8%** |
-| | Execution Accuracy | 11% | **14%** |
-| **Documentation Generation** | BLEU | 0.0241 | **0.0528** |
-| | CodeBERTScore | 0.9250 | **0.9447** |
-| | F1 | 0.1181 | **0.2022** |
-| | ROUGE-1 | 0.0831 | **0.1615** |
-| | ROUGE-L | 0.0719 | **0.1494** |
-
-LoRA fine-tuning (trained on only 9 illustrative prompt/completion examples — 2.16M trainable params, 0.44% of the model) improves every metric on both tasks. Gains are proportionally larger on documentation generation.
-
-### RAG, Top-K Sweep, and LLM Baseline (5-question SQL slice)
-
-- On the small 5-question evaluation slice used for this stage, the **RAG-augmented pipeline underperformed the no-RAG LoRA baseline** on every metric — a genuine, reproducible negative result, not a placeholder. See [Known Limitations](#-known-limitations--future-work) for why.
-- A **top-K sweep** over K ∈ {1, 2, 3, 5, 8} shows the gain is least negative around **K = 2–3** and degrades further at K = 8.
-- The **LLM-baseline comparison** fell back to the same local Qwen2.5-Coder-0.5B model in this run because no external API credential was resolved at runtime — see the security note below and Known Limitations.
-- For reference, the rule-based Spider baseline scores **12.28% exact match / 33.66% execution accuracy** over the full 1,034-question dev set.
-
-Plots for all of the above are saved under `output/plots/` (`scores_baseline_text_to_sql_vs_doc_generation.png`, `scores_finetuned_text_to_sql_vs_doc_generation.png`, `scores_model_comparison_checkpoint2.png`, `scores_model_comparison_full.png`, `topk_gain_sweep.png`).
-
-
-## Dataset
-
-This project uses the **[Spider](https://yale-lily.github.io/spider)** dataset — a large-scale, cross-domain text-to-SQL benchmark — as its primary evaluation dataset.
-
-> **Note:** The Spider dataset is **not included in this repository**. GitHub's file/repo upload limits make it impractical to commit the raw dataset (question/SQL pairs, table schemas, and per-database SQLite files), so it must be downloaded separately before running the pipeline.
-
-### Download
-
-1. Download the dataset from Google Drive: **[Spider dataset (Google Drive)](https://drive.google.com/file/d/1403EGqzIDoHMdQF4c9Bkyl7dZLZ5Wt6J/view)**
-   - Alternatively, see the [official Spider dataset page](https://yale-lily.github.io/spider) for the source release and licensing terms.
-2. Unzip the download and place its contents under `data/raw/Spider/` at the repository root, so that `dev.json`, `tables.json`, and the per-database SQLite files sit alongside each other, e.g.:
-
-   ```
-   data/raw/Spider/
-   ├── dev.json
-   ├── tables.json
-   └── database/
-       ├── concert_singer/
-       │   └── concert_singer.sqlite
-       └── ...
-   ```
-
-3. Run `src/scripts/convert_spider_to_jsonl.py` to convert the raw files into the normalized `data/processed/spider_eval.jsonl` and retrieval corpus used by the notebooks (see [Quick Start](#-quick-start)).
-
-`data/raw/`, `data/processed/`, and `data/indices/` are excluded via `.gitignore` for the same reason — they're generated/downloaded locally rather than versioned.
-
----
-
-## Installation
-
-### Prerequisites
-
-```bash
-Python 3.10 or higher
-A CUDA-capable GPU is optional — the code auto-detects CUDA > MPS (Apple Silicon) > CPU
-Recommended: 16 GB RAM, 8 GB+ VRAM if using GPU
-```
-
-### Step 1: Clone Repository
-
-```bash
-git clone https://github.com/Rithusravya/Emasters_Group-2_CapstoneProject.git
-cd Emasters_Group-2_CapstoneProject
-```
-
-### Step 2: Create Virtual Environment
-
-```bash
-python -m venv venv
-source venv/bin/activate      # macOS/Linux
-# OR
-venv\Scripts\activate         # Windows
-```
-
-### Step 3: Install Dependencies
-
-```bash
-pip install --upgrade pip
-pip install -r requirements.txt
-```
-
-### Step 4: Verify Installation
-
-```bash
-python -c "import torch; print(f'CUDA Available: {torch.cuda.is_available()}')"
-```
-
----
-
-## Quick Start
-
-This project is driven through notebooks, not a CLI. Typical order:
-
-1. **Download & prepare data** — download the Spider dataset as described in [Dataset](#-dataset) and place it under `data/raw/Spider/`. Then run `src/scripts/convert_spider_to_jsonl.py` to convert the raw Spider files into `data/processed/spider_eval.jsonl` and a retrieval corpus; `src/scripts/prepare_processed_data.py` builds the MongoDB-style variant.
-2. **Baseline evaluation** — open `baseline_model.ipynb` and run all cells to evaluate the un-adapted base model on text-to-SQL and documentation generation.
-3. **LoRA fine-tuning + evaluation** — open `finetuned_model.ipynb` to train (or reload) the LoRA adapter at `models/checkpoints/lora_finetuned/` and re-run the same evaluations.
-4. **RAG / top-K / LLM comparison** — open `eval.ipynb` to build the FAISS + AST indices, measure RAG gain, sweep top-K, and compare against the LLM baseline.
-5. **Spider execution evaluation** — open `spider_eval.ipynb` for SQL-execution-based scoring against the SQLite databases.
-
-Programmatic usage of the core pipeline, e.g. for text-to-SQL generation:
-
-```python
-from src.config import load_config
-from src.models.load_model import ModelLoader
-from src.generators.program_generator import GenerationPipeline
-from src.generators.text_to_sql import TextToSQLGenerator
-
-config = load_config()  # reads configs/config.yaml
-loader = ModelLoader(config)
-models, tokenizer = loader.load_models(lora_path=config.lora.output_dir)
-
-pipeline = GenerationPipeline(models["lora"] or models["base"], tokenizer, config.generation)
-sql_gen = TextToSQLGenerator(pipeline)
-
-result = sql_gen.generate_queries(
-    question="How many singers are there?",
-    schema="CREATE TABLE singer (singer_id INT, name TEXT, age INT)",
-)
-print(result["sql_query"])
-```
-
----
-
-## Repository Layout
-
-```
-Emasters_Group-2_CapstoneProject/
-├── configs/
-│   └── config.yaml                    # Project, model, LoRA, retrieval, eval & output paths
-├── data/
-│   ├── raw/                           # Raw Spider files (dev.json, tables.json, SQLite DBs)
-│   ├── processed/                     # Normalized JSONL (spider_eval, retrieval_corpus, etc.)
-│   └── indices/                       # Saved FAISS index, AST store, FAISS metadata
-├── models/
-│   └── checkpoints/lora_finetuned/    # Saved LoRA adapter + tokenizer files
-├── src/
-│   ├── config.py                      # YAML → dataclass config loader
-│   ├── data/
-│   │   └── data_loader.py             # Generic JSON/JSONL dataset loader (Spider, etc.)
-│   ├── models/
-│   │   └── load_model.py              # Base model + LoRA loading, training loop, base-vs-LoRA generation
-│   ├── generators/
-│   │   ├── program_generator.py       # Core GenerationPipeline + prompt builder
-│   │   ├── text_to_sql.py             # Text-to-SQL generator
-│   │   ├── sql_generator.py           # SQL prompt/parsing helpers
-│   │   ├── doc_gen.py                 # Documentation generator
-│   │   ├── commit_gen.py              # Commit-message generator
-│   │   └── llm_baseline.py            # Pluggable local/OpenAI/Gemini LLM baseline
-│   ├── embeddings/
-│   │   ├── code_lm_embedder.py        # Embeddings from the small code LM's own hidden states
-│   │   ├── embedding.py               # External embedding-model backend (BGE)
-│   │   ├── indexing.py                # FAISS semantic index manager
-│   │   ├── ast_indexing.py            # AST / SQL structural fingerprint indexer
-│   │   └── hybrid_retriever.py        # Dense + structural blended retrieval
-│   ├── rag/
-│   │   └── rag_pipeline.py            # Context retrieval, prompt building, RAG generation
-│   ├── evaluation/
-│   │   ├── metrics.py                 # BLEU, ROUGE, F1, CodeBERTScore, execution accuracy
-│   │   ├── evaluation.py              # Task-level evaluation engine
-│   │   ├── sql_eval.py                # Spider SQL normalization & execution comparison
-│   │   ├── comparator.py              # Base vs. LoRA vs. RAG comparison
-│   │   ├── rag_evaluation.py          # RAG gain-over-baseline + top-K sweep
-│   │   └── visualization.py           # Chart generation
-│   └── scripts/
-│       ├── convert_spider_to_jsonl.py # Raw Spider → normalized JSONL
-│       └── prepare_processed_data.py  # Build derived/MongoDB-style datasets
-├── outputs/generated/                 # Per-task generated samples & timestamped run results
-├── output/plots/                      # Saved comparison charts
-├── backup/                            # Earlier snapshot of evaluation/rag/embeddings modules
-├── baseline_model.ipynb               # Base model evaluation
-├── finetuned_model.ipynb              # LoRA fine-tuning + evaluation
-├── spider_eval.ipynb                  # Spider execution-based SQL evaluation
-├── eval.ipynb                         # RAG gain / top-K sweep / LLM comparison
-├── main.ipynb                         # Colab entry point
-├── requirements.txt
-└── README.md
-```
-
----
-
-## Configuration (`configs/config.yaml`)
-
-Key fields actually read by `src/config.py`:
-
-```yaml
-model_name: "Qwen/Qwen2.5-Coder-0.5B-Instruct"
-lora_adapter_path: "models/checkpoints/lora_finetuned"
-embedding_model: "BAAI/bge-small-en-v1.5"   # used only when embedding.source == "external"
-
-embedding:
-  source: "code_lm"           # "code_lm" (default, self-embeddings) or "external"
-
-retrieval:
-  hybrid: true
-  structural_weight_alpha: 0.7
-  structural_language: "sql"
-  topk_sweep: [1, 2, 3, 5, 8]
-
-llm_baseline:
-  backend: "local"            # "local" or an external provider ("openai" / "gemini")
-  local_model: "Qwen/Qwen2.5-Coder-0.5B-Instruct"
-  api_model: "gpt-5.4-mini"
-  max_new_tokens: 256
-  temperature: 0.2
-
-lora:
-  r: 16
-  alpha: 32
-  dropout: 0.05
-  target_modules: ["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"]
-  batch_size: 4
-  learning_rate: 0.0003
-  epochs: 5
-  output_dir: "models/checkpoints/lora_finetuned"
-
-indexing:
-  top_k: 3
-  vector_dim: 1024            # must match the embedding model's output dimension
-
-outputs:
-  logs_dir: "output/logs"
-  metrics_path: "output/metrics/evaluation_metrics.json"
-  plots_dir: "output/plots"
-```
-
----
-
-## Requirements
-
-| Package | Version | Purpose |
-|---|---|---|
-| `torch` | 2.5.1 | Core deep learning framework |
-| `transformers` | 4.46.3 | Pretrained model + tokenizer loading (Qwen2.5-Coder) |
-| `peft` | 0.13.2 | LoRA fine-tuning |
-| `datasets` | 3.1.0 | Dataset utilities |
-| `faiss-cpu` | 1.9.0 | Dense vector similarity search |
-| `tree-sitter` / `tree-sitter-python` | 0.23.2 / 0.23.6 | (Available for AST-based parsing) |
-| `codebleu` | 0.7.0 | CodeBLEU metric |
-| `codebert-score` | 0.3.13 | CodeBERTScore semantic similarity metric |
-| `evaluate` | 0.4.3 | Hugging Face evaluation metrics |
-| `rouge-score` | 0.1.2 | ROUGE metrics |
-| `nltk` | 3.9.1 | Text preprocessing / BLEU support |
-| `PyYAML` | 6.0.2 | Config parsing |
-| `matplotlib` / `seaborn` | 3.10.0 / 0.13.2 | Plotting |
-| `numpy` | 2.1.3 | Numerical computing |
-
-See `requirements.txt` for the full pinned list.
-
----
-
-## Contributing
-
-1. **Fork** the repository
-2. **Create** a feature branch (`git checkout -b feature/amazing-feature`)
-3. **Commit** your changes (`git commit -m 'Add amazing feature'`)
-4. **Push** to the branch (`git push origin feature/amazing-feature`)
-5. **Open** a Pull Request
-
----
-
-## License
-
-This project is licensed under the **MIT License** — see the `LICENSE` file for details.
-
----
-
-## Citation
-
-```bibtex
-@software{codegen_capstone_2026,
-  title  = {CodeGen: Small Code-LM Fine-Tuning and Hybrid RAG Pipeline},
-  author = {Emasters Group 2},
-  year   = {2026},
-  url    = {https://github.com/Rithusravya/Emasters_Group-2_CapstoneProject}
-}
-```
-
----
-
-<div align="center">
-
-**Built by IIIT-H eMasters Group 2**
-
-[⬆ Back to Top](#-codegen-small-code-lm-fine-tuning--hybrid-rag-pipeline)
-
-</div>
+---