app.py

import gradio as gr
import openai
import os
import nltk
import shutil
import numpy as np
import torch
from datasets import load_dataset
from langchain.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import Chroma
from langchain.schema import Document
from sentence_transformers import SentenceTransformer
from sklearn.metrics import mean_squared_error, roc_auc_score
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity

# ✅ Load Pretrained Model
model_name = "bert-base-uncased"
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
embedding_model = HuggingFaceEmbeddings(model_name=model_name)
embedding_model.client.to(device)

# ✅ Set OpenAI API Key (Replace with your own)
openai.api_key = os.getenv("OPENAI_API_KEY")  

# ✅ Download NLTK Dependencies
nltk.download('punkt')

# ✅ Load RunGalileo Datasets
ragbench = {}
for dataset in ['covidqa', 'cuad', 'delucionqa', 'emanual', 'expertqa', 'finqa', 'hagrid', 'hotpotqa', 'msmarco', 'pubmedqa', 'tatqa', 'techqa']:
    ragbench[dataset] = load_dataset("rungalileo/ragbench", dataset)
print("Datasets Loaded ✅")

# ✅ Function to Chunk Documents
def chunk_documents_semantic(documents, max_chunk_size=500):
    chunks = []
    for doc in documents:
        sentences = nltk.sent_tokenize(doc)
        current_chunk = ""
        for sentence in sentences:
            if len(current_chunk) + len(sentence) <= max_chunk_size:
                current_chunk += sentence + " "
            else:
                chunks.append(current_chunk.strip())
                current_chunk = sentence + " "
        if current_chunk:
            chunks.append(current_chunk.strip())
    return chunks

# ✅ Chunk the Entire Dataset
chunked_ragbench = {}
for dataset_name in ragbench.keys():
    for split in ragbench[dataset_name].keys():
        original_documents_full = ragbench[dataset_name][split]['documents']
        chunked_documents_full = chunk_documents_semantic(original_documents_full)
        chunked_ragbench[split] = chunked_documents_full
print("Chunking Completed ✅")

# ✅ Setup ChromaDB
persist_directory = "chroma_db_directory"
if os.path.exists(persist_directory):
    shutil.rmtree(persist_directory)

documents = [Document(page_content=chunk) for chunk in chunked_documents_full]
vectordb = Chroma.from_documents(
    documents=documents,
    embedding=embedding_model,
    persist_directory=persist_directory
)
vectordb.persist()

# ✅ Retrieve Documents
def retrieve_documents(question, k=5):
    docs = vectordb.similarity_search(question, k=k)
    if not docs:
        return ["⚠️ No relevant documents found. Try a different query."]
    return [doc.page_content for doc in docs]

# ✅ Generate AI Response
def generate_response(question, context):
    if not context or "No relevant documents found." in context:
        return "No relevant context available. Try a different query."

    full_prompt = f"Context: {context}\n\nQuestion: {question}"

    try:
        client = openai.OpenAI()
        response = client.chat.completions.create(
            model="gpt-4",
            messages=[
                {"role": "system", "content": "You are an AI assistant that answers user queries based on the given context."},
                {"role": "user", "content": full_prompt}
            ],
            max_tokens=300,
            temperature=0.7
        )
        return response.choices[0].message.content.strip()
    except Exception as e:
        return f"Error generating response: {str(e)}"

# ✅ Compute Context Relevance, Utilization, Completeness, Adherence
def compute_cosine_similarity(text1, text2):
    vectorizer = TfidfVectorizer()
    vectors = vectorizer.fit_transform([text1, text2])
    return cosine_similarity(vectors[0], vectors[1])[0][0]

def context_relevance(question, relevant_documents):
    combined_docs = " ".join(relevant_documents)
    return compute_cosine_similarity(question, combined_docs)

def context_utilization(response, relevant_documents):
    combined_docs = " ".join(relevant_documents)
    return compute_cosine_similarity(response, combined_docs)

def completeness(response, ground_truth_answer):
    return compute_cosine_similarity(response, ground_truth_answer)

def adherence(response, relevant_documents):
    combined_docs = " ".join(relevant_documents)
    response_tokens = set(response.split())
    relevant_tokens = set(combined_docs.split())
    supported_tokens = response_tokens.intersection(relevant_tokens)
    return len(supported_tokens) / len(response_tokens)

def compute_rmse(predicted_values, ground_truth_values):
    return np.sqrt(mean_squared_error(ground_truth_values, predicted_values))

# ✅ Full RAG Pipeline
def rag_pipeline(question):
    retrieved_docs = retrieve_documents(question, k=5)
    context = " ".join(retrieved_docs)
    response = generate_response(question, context)

    # Compute Evaluation Metrics
    ground_truth_answer = "Sample ground truth answer from dataset"
    predicted_metrics = {
        "context_relevance": context_relevance(question, retrieved_docs),
        "context_utilization": context_utilization(response, retrieved_docs),
        "completeness": completeness(response, ground_truth_answer),
        "adherence": adherence(response, retrieved_docs)
    }
    
    return response, "\n\n".join(retrieved_docs), predicted_metrics

# ✅ Gradio UI Interface
iface = gr.Interface(
    fn=rag_pipeline,
    inputs=gr.Textbox(label="Enter your question"),
    outputs=[
        gr.Textbox(label="Generated Response"),
        gr.Textbox(label="Retrieved Documents"),
        gr.JSON(label="Evaluation Metrics")
    ],
    title="RAG-Based QA System for RunGalileo",
    description="Enter a question and retrieve relevant documents with AI-generated response & evaluation metrics."
)

# ✅ Launch the Gradio App
if __name__ == "__main__":
    iface.launch()