Delete app.py

app.py

@@ -1,210 +0,0 @@
-import gradio as gr
-import torch
-import time
-from pathlib import Path
-import sys
-import os
-import logging
-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_mongo_generator import TextToMongoGenerator
-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"
-
-# Add project root and src to path
-project_root = Path(__file__).parent.resolve()
-sys.path.append(str(project_root))
-sys.path.append(str(project_root / "src"))
-
-# ==========================================
-# 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)
-
-base_generator = TextToMongoGenerator(base_pipeline, config)
-finetuned_generator = TextToMongoGenerator(finetuned_pipeline, config)
-
-# ==========================================
-# 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
-# ==========================================
-def generate_mongo_query(question: str, model_choice: str, use_rag: bool, top_k: int):
-    if not question.strip():
-        return "Please enter a valid question.", "Latency: N/A"
-
-    start_time = time.time()
-
-    # RAG Generation
-    if use_rag and rag_pipeline is not None:
-        generated_query, context_metadata = rag_pipeline.generate_with_rag(question, top_k=int(top_k))
-        latency = (time.time() - start_time) * 1000
-
-        # Format context for display
-        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}"
-
-    # Standard Generation (Base or Fine-Tuned)
-    else:
-        if model_choice == "Fine-Tuned (LoRA)":
-            result = finetuned_generator.generate_single(question)
-        else:
-            result = base_generator.generate_single(question)
-
-        latency = result['latency_ms']
-        info_text = f"⏱️ Latency: {latency:.2f} ms\nModel: {model_choice}"
-        return result['generated_query'], info_text
-
-
-# ==========================================
-# 5. Build Gradio UI
-# ==========================================
-with gr.Blocks(title="Text-to-MongoDB Generator", theme=gr.themes.Soft()) as demo:
-    gr.Markdown(
-        """
-        # 🚀 Text-to-MongoDB Query Generator
-        Convert natural language questions into executable MongoDB queries using **Base**, **Fine-Tuned**, or **RAG-enhanced** models.
-        """
-    )
-
-    with gr.Row():
-        # Left Column: Inputs
-        with gr.Column(scale=3):
-            question_input = gr.Textbox(
-                label="Natural Language Question",
-                placeholder="e.g., How many singers do we have?",
-                lines=2,
-                info="Type your question about the database here."
-            )
-
-            with gr.Row():
-                model_choice = gr.Radio(
-                    choices=["Base Model", "Fine-Tuned (LoRA)"],
-                    value="Fine-Tuned (LoRA)",
-                    label="Model Selection"
-                )
-                use_rag = gr.Checkbox(
-                    label="🧠 Use RAG (Retrieval-Augmented Generation)",
-                    value=False,
-                    info="Requires pre-built indices"
-                )
-
-            with gr.Accordion("⚙️ RAG Settings", open=False):
-                top_k_slider = gr.Slider(
-                    minimum=1, maximum=10, value=3, step=1,
-                    label="Top-K Retrieved Contexts",
-                    info="Number of similar examples to retrieve from the vector database."
-                )
-
-            generate_btn = gr.Button("Generate MongoDB Query", variant="primary", size="lg")
-
-        # Right Column: Outputs
-        with gr.Column(scale=2):
-            query_output = gr.Code(
-                label="Generated MongoDB Query",
-                language="javascript",
-                lines=6,
-                interactive=False
-            )
-            info_output = gr.Textbox(
-                label="Generation Info & Context",
-                lines=8,
-                interactive=False,
-                max_lines=15
-            )
-
-    gr.Markdown("---")
-    gr.Markdown("### 💡 Example Questions")
-    gr.Examples(
-        examples=[
-            ["How many singers do we have?"],
-            ["What is the average age of all singers from France?"],
-            ["Show the name and release year of the song by the youngest singer."],
-            ["Find the number of pets whose weight is heavier than 10."],
-            ["What are the different countries with singers above age 20?"],
-            ["List the name and date of each battle."],
-        ],
-        inputs=[question_input],
-        cache_examples=False
-    )
-
-    # Link button to function
-    generate_btn.click(
-        fn=generate_mongo_query,
-        inputs=[question_input, model_choice, use_rag, top_k_slider],
-        outputs=[query_output, info_output]
-    )
-
-# ==========================================
-# 6. Launch the App
-# ==========================================
-if __name__ == "__main__":
-    # Launch locally on port 7860
-    demo.launch(
-        server_name="0.0.0.0",  # Makes it accessible on your local network
-        server_port=7860,
-        share=False,  # Set to True if you want a public temporary URL
-        show_error=True
-    )