File size: 9,335 Bytes
86d6d13 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 | import spaces # must be imported before torch/other CUDA-touching modules (ZeroGPU requirement)
import time
from pathlib import Path
import sys
import os
import logging
# ==========================================
# 0. Fix sys.path BEFORE importing anything from src/
# ==========================================
project_root = Path(__file__).parent.resolve()
sys.path.insert(0, str(project_root))
sys.path.insert(0, str(project_root / "src"))
import gradio as gr
import torch
from config import load_config
from src.models.load_model import ModelLoader
from src.generators.program_generator import GenerationPipeline
from src.generators.text_to_mongo_generator import TextToMongoGenerator
from src.generators.doc_generator import DocGenerator
from src.vectorDB.index_manager import IndexManager
from src.rag.rag_pipeline import RAGPipeline
# Configure logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)
# Environment variables to suppress warnings
os.environ["TOKENIZERS_PARALLELISM"] = "false"
os.environ["PYTORCH_MPS_HIGH_WATERMARK_RATIO"] = "0.0"
os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
# ==========================================
# 1. Load Configuration & Models
# ==========================================
logger.info("Loading configuration...")
config = load_config("configs/config.yaml")
logger.info("Initializing Model Loader...")
model_loader = ModelLoader(config)
tokenizer = model_loader.load_tokenizer()
base_model = model_loader.load_base_model()
# Try to load the Fine-Tuned (LoRA) model if it exists
lora_path = Path(config.lora.output_dir)
if lora_path.exists() and (lora_path / "adapter_config.json").exists():
logger.info(f"✅ Found LoRA adapter at {lora_path}. Loading Fine-Tuned model...")
finetuned_model = model_loader.autodetect_saved_model(str(lora_path))
else:
logger.warning("⚠️ No LoRA adapter found. Fine-Tuned model will fallback to Base model.")
finetuned_model = base_model
# ==========================================
# 2. Setup Generation Pipelines
# ==========================================
logger.info("Setting up generation pipelines...")
base_pipeline = GenerationPipeline(base_model, tokenizer, config.generation)
finetuned_pipeline = GenerationPipeline(finetuned_model, tokenizer, config.generation)
# Text-to-MongoDB Generators
base_generator = TextToMongoGenerator(base_pipeline, config)
finetuned_generator = TextToMongoGenerator(finetuned_pipeline, config)
# Documentation Generators
base_doc_generator = DocGenerator(base_pipeline)
finetuned_doc_generator = DocGenerator(finetuned_pipeline)
# ==========================================
# 3. Setup RAG Pipeline (Optional)
# ==========================================
rag_pipeline = None
try:
index_manager = IndexManager(
embedding_model=config.embedding.external_model,
language=config.retrieval.structural_language,
semantic_weight=1.0 - config.retrieval.structural_weight_alpha,
ast_weight=config.retrieval.structural_weight_alpha,
save_dir="data/indices"
)
if index_manager._indices_exist():
index_manager.load_indices()
rag_pipeline = RAGPipeline(
retriever=index_manager,
generator=finetuned_pipeline,
config=config.generation
)
logger.info("✅ RAG Pipeline initialized successfully.")
else:
logger.warning("⚠️ RAG indices not found in 'data/indices'. RAG feature will be disabled.")
except Exception as e:
logger.error(f"⚠️ Could not initialize RAG pipeline: {e}")
# ==========================================
# 4. Gradio Generation Logic
# ==========================================
@spaces.GPU(duration=60)
def generate_output(task, input_text, model_choice, use_rag, top_k):
if not input_text.strip():
return "Please enter a valid input.", "Latency: N/A"
start_time = time.time()
if task == "Text-to-MongoDB":
if use_rag and rag_pipeline is not None:
generated_query, context_metadata = rag_pipeline.generate_with_rag(input_text, top_k=int(top_k))
latency = (time.time() - start_time) * 1000
context_str = "Retrieved Context:\n" + "-" * 30 + "\n"
for i, ctx in enumerate(context_metadata[:int(top_k)]):
q = ctx.get('question', 'N/A')
mql = ctx.get('generated_mongodb_query', ctx.get('mongodb_query', 'N/A'))
context_str += f"[{i + 1}] Q: {q}\n MQL: {mql}\n\n"
return generated_query, f"⏱️ Latency: {latency:.2f} ms\n\n{context_str}"
else:
if model_choice == "Fine-Tuned (LoRA)":
result = finetuned_generator.generate_single(input_text)
else:
result = base_generator.generate_single(input_text)
latency = result['latency_ms']
info_text = f"⏱️ Latency: {latency:.2f} ms\nModel: {model_choice}\nTask: Text-to-MongoDB"
return result['generated_query'], info_text
elif task == "Documentation Generation":
if model_choice == "Fine-Tuned (LoRA)":
generated_doc = finetuned_doc_generator.generate_docstring(input_text)
else:
generated_doc = base_doc_generator.generate_docstring(input_text)
latency = (time.time() - start_time) * 1000
info_text = f"⏱️ Latency: {latency:.2f} ms\nModel: {model_choice}\nTask: Documentation Generation"
return generated_doc, info_text
def update_ui(task):
"""Dynamically updates input placeholders and output syntax highlighting based on the selected task."""
if task == "Text-to-MongoDB":
return (
gr.update(placeholder="e.g., How many singers do we have?", label="Natural Language Question"),
gr.update(language="javascript")
)
else:
return (
gr.update(placeholder="e.g., def add(a, b):\n return a + b", label="Code Snippet"),
gr.update(language="python")
)
# ==========================================
# 5. Build Gradio UI
# ==========================================
with gr.Blocks(title="CodeGen", theme=gr.themes.Soft()) as demo:
gr.Markdown(
"""
# 🚀 CodeGen
Generate **MongoDB queries** or **Code Documentation** using Base, Fine-Tuned, or RAG-enhanced models.
"""
)
with gr.Row():
# Left Column: Controls
with gr.Column(scale=1):
task_selector = gr.Radio(
choices=["Text-to-MongoDB", "Documentation Generation"],
value="Text-to-MongoDB",
label="Select Task"
)
model_choice = gr.Radio(
choices=["Base Model", "Fine-Tuned (LoRA)"],
value="Fine-Tuned (LoRA)",
label="Model Selection"
)
with gr.Accordion("⚙️ Advanced Settings", open=False):
use_rag = gr.Checkbox(
label="🧠 Use RAG (Text-to-MongoDB only)",
value=False,
info="Requires pre-built indices"
)
top_k_slider = gr.Slider(
minimum=1, maximum=10, value=3, step=1,
label="Top-K Retrieved Contexts"
)
generate_btn = gr.Button("Generate", variant="primary", size="lg")
# Right Column: Inputs & Outputs
with gr.Column(scale=2):
input_box = gr.Textbox(
label="Natural Language Question",
placeholder="e.g., How many singers do we have?",
lines=6,
info="Type your question or paste your code snippet here."
)
output_box = gr.Code(
label="Generated Output",
language="javascript",
lines=10,
interactive=False
)
info_output = gr.Textbox(
label="Generation Info",
lines=4,
interactive=False
)
gr.Markdown("---")
gr.Markdown("### 💡 Example Prompts")
gr.Examples(
examples=[
["How many singers do we have?", "Text-to-MongoDB"],
["What is the average age of all singers from France?", "Text-to-MongoDB"],
["def calculate_area(radius):\n return 3.14159 * radius ** 2", "Documentation Generation"],
["import os\ndef list_files(directory):\n return os.listdir(directory)", "Documentation Generation"],
],
inputs=[input_box, task_selector],
cache_examples=False
)
# Link events
task_selector.change(fn=update_ui, inputs=task_selector, outputs=[input_box, output_box])
generate_btn.click(
fn=generate_output,
inputs=[task_selector, input_box, model_choice, use_rag, top_k_slider],
outputs=[output_box, info_output]
)
# ==========================================
# 6. Launch the App
# ==========================================
if __name__ == "__main__":
demo.launch(
server_name="0.0.0.0",
server_port=7860,
share=False,
show_error=True
) |