app.py

import gradio as gr
import os
from typing import List, Dict
import numpy as np
from datasets import load_dataset
from langchain.text_splitter import (
    RecursiveCharacterTextSplitter,
    CharacterTextSplitter,
    TokenTextSplitter
)
from langchain_community.vectorstores import FAISS, Chroma
from langchain_community.document_loaders import PyPDFLoader
from langchain.chains import ConversationalRetrievalChain
from langchain_community.embeddings import HuggingFaceEmbeddings 
from langchain_community.llms import HuggingFaceEndpoint
from langchain.memory import ConversationBufferMemory
from sentence_transformers import SentenceTransformer, util
import torch
from ragas import evaluate
from ragas.metrics import (
    ContextRecall,
    AnswerRelevancy,
    Faithfulness,
    ContextPrecision
)
import pandas as pd

# Constants and configurations
CHUNK_SIZES = {
    "small": {"recursive": 512, "fixed": 512, "token": 256},
    "medium": {"recursive": 1024, "fixed": 1024, "token": 512}
}

class RAGEvaluator:
    def __init__(self):
        self.datasets = {
            "squad": "squad_v2",
            "msmarco": "ms_marco"
        }
        self.current_dataset = None
        self.test_samples = []
        
    def load_dataset(self, dataset_name: str, num_samples: int = 50):
        if dataset_name == "squad":
            dataset = load_dataset("squad_v2", split="validation")
            samples = dataset.select(range(num_samples))
            self.test_samples = [
                {
                    "question": sample["question"],
                    "ground_truth": sample["answers"]["text"][0] if sample["answers"]["text"] else "",
                    "context": sample["context"]
                }
                for sample in samples
                if sample["answers"]["text"]  # Filter out samples without answers
            ]
        elif dataset_name == "msmarco":
            dataset = load_dataset("ms_marco", "v2.1", split="train")
            samples = dataset.select(range(num_samples))
            self.test_samples = [
                {
                    "question": sample["query"],
                    "ground_truth": sample["answers"][0] if sample["answers"] else "",
                    "context": sample["passages"]["passage_text"][0]
                }
                for sample in samples
                if sample["answers"]  # Filter out samples without answers
            ]
        self.current_dataset = dataset_name
        return self.test_samples
    
    def evaluate_configuration(self, 
                             vector_db,
                             qa_chain,
                             splitting_strategy: str,
                             chunk_size: str) -> Dict:
        if not self.test_samples:
            return {"error": "No dataset loaded"}
            
        results = []
        for sample in self.test_samples:
            response = qa_chain.invoke({
                "question": sample["question"],
                "chat_history": []
            })
            
            results.append({
                "question": sample["question"],
                "answer": response["answer"],
                "contexts": [doc.page_content for doc in response["source_documents"]],
                "ground_truths": [sample["ground_truth"]]
            })
        
        # Convert to RAGAS dataset format
        eval_dataset = Dataset.from_list(results)
        
        # Calculate RAGAS metrics
        metrics = [
            ContextRecall(),
            AnswerRelevancy(),
            Faithfulness(),
            ContextPrecision()
        ]
        
        scores = evaluate(
            eval_dataset,
            metrics=metrics
        )
        
        return {
            "configuration": f"{splitting_strategy}_{chunk_size}",
            "context_recall": float(scores['context_recall']),
            "answer_relevancy": float(scores['answer_relevancy']),
            "faithfulness": float(scores['faithfulness']),
            "context_precision": float(scores['context_precision']),
            "average_score": float(np.mean([
                scores['context_recall'],
                scores['answer_relevancy'],
                scores['faithfulness'],
                scores['context_precision']
            ]))
        }

def demo():
    evaluator = RAGEvaluator()
    
    with gr.Blocks(theme=gr.themes.Default(primary_hue="red", secondary_hue="pink", neutral_hue="sky")) as demo:
        vector_db = gr.State()
        qa_chain = gr.State()
        
        gr.HTML("<center><h1>Enhanced RAG PDF Chatbot with Evaluation</h1></center>")
        
        with gr.Tabs():
            # Custom PDF Tab
            with gr.Tab("Custom PDF Chat"):
                # Your existing UI components here
                with gr.Row():
                    with gr.Column(scale=86):
                        gr.Markdown("<b>Step 1 - Configure and Initialize RAG Pipeline</b>")
                        with gr.Row():
                            document = gr.Files(height=300, file_count="multiple", file_types=["pdf"], interactive=True, label="Upload PDF documents")
                        
                        with gr.Row():
                            splitting_strategy = gr.Radio(
                                ["recursive", "fixed", "token"],
                                label="Text Splitting Strategy",
                                value="recursive"
                            )
                            db_choice = gr.Dropdown(
                                ["faiss", "chroma"],
                                label="Vector Database",
                                value="faiss"
                            )
                            chunk_size = gr.Radio(
                                ["small", "medium"],
                                label="Chunk Size",
                                value="medium"
                            )
                        
                        # Rest of your existing UI components...

            # Evaluation Tab
            with gr.Tab("RAG Evaluation"):
                with gr.Row():
                    dataset_choice = gr.Dropdown(
                        choices=list(evaluator.datasets.keys()),
                        label="Select Evaluation Dataset",
                        value="squad"
                    )
                    load_dataset_btn = gr.Button("Load Dataset")
                
                with gr.Row():
                    dataset_info = gr.JSON(label="Dataset Information")
                
                with gr.Row():
                    eval_splitting_strategy = gr.Radio(
                        ["recursive", "fixed", "token"],
                        label="Text Splitting Strategy",
                        value="recursive"
                    )
                    eval_chunk_size = gr.Radio(
                        ["small", "medium"],
                        label="Chunk Size",
                        value="medium"
                    )
                
                with gr.Row():
                    evaluate_btn = gr.Button("Run Evaluation")
                    evaluation_results = gr.DataFrame(label="Evaluation Results")

        # Event handlers
        def load_dataset_handler(dataset_name):
            samples = evaluator.load_dataset(dataset_name)
            return {
                "dataset": dataset_name,
                "num_samples": len(samples),
                "sample_questions": [s["question"] for s in samples[:3]]
            }
        
        def run_evaluation(dataset_choice, splitting_strategy, chunk_size, vector_db, qa_chain):
            if not evaluator.current_dataset:
                return pd.DataFrame()
                
            results = evaluator.evaluate_configuration(
                vector_db=vector_db,
                qa_chain=qa_chain,
                splitting_strategy=splitting_strategy,
                chunk_size=chunk_size
            )
            
            # Convert results to DataFrame
            df = pd.DataFrame([results])
            return df
        
        # Connect event handlers
        load_dataset_btn.click(
            load_dataset_handler,
            inputs=[dataset_choice],
            outputs=[dataset_info]
        )
        
        evaluate_btn.click(
            run_evaluation,
            inputs=[
                dataset_choice,
                eval_splitting_strategy,
                eval_chunk_size,
                vector_db,
                qa_chain
            ],
            outputs=[evaluation_results]
        )
        
        qachain_btn.click(
            initialize_llmchain,  # Fixed function name here
            inputs=[llm_btn, slider_temperature, slider_maxtokens, slider_topk, vector_db],
            outputs=[qa_chain, llm_progress]
        ).then(
            lambda: [None, "", 0, "", 0, "", 0],
            inputs=None,
            outputs=[chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page],
            queue=False
        )
        
        msg.submit(conversation,
            inputs=[qa_chain, msg, chatbot],
            outputs=[qa_chain, msg, chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page],
            queue=False
        )
        
        submit_btn.click(conversation,
            inputs=[qa_chain, msg, chatbot],
            outputs=[qa_chain, msg, chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page],
            queue=False
        )
        
        clear_btn.click(
            lambda: [None, "", 0, "", 0, "", 0],
            inputs=None,
            outputs=[chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page],
            queue=False
        )
    demo.queue().launch(debug=True)

if __name__ == "__main__":
    demo()