app.py

import chromadb 
import pandas as pd
from sentence_transformers import SentenceTransformer
from langchain.text_splitter import RecursiveCharacterTextSplitter
import json
import openai
from openai import OpenAI
import numpy as np
import requests
import chromadb
from chromadb import Client
from sentence_transformers import SentenceTransformer, util
from langchain_community.embeddings import HuggingFaceEmbeddings
from chromadb import Client
from chromadb import PersistentClient
import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
import os
import requests
import time
import tempfile
from langdetect import detect
import nltk
nltk.download('punkt')
from nltk.tokenize import word_tokenize
from rank_bm25 import BM25Okapi



API_KEY = os.environ.get("OPENROUTER_API_KEY")

# Load the Excel file
df = pd.read_excel("web_documents.xlsx", engine='openpyxl')

# Initialize Chroma Persistent Client
client = chromadb.PersistentClient(path="./db")

# Create (or get) the Chroma collection
collection = client.get_or_create_collection(
    name="rag_web_db_cosine_full_documents",
    metadata={"hnsw:space": "cosine"}
)

# Load the embedding model
embedding_model = SentenceTransformer('sentence-transformers/paraphrase-MiniLM-L6-v2')

# Initialize the text splitter
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1200, chunk_overlap=150)

total_chunks = 0

# Process each row in the DataFrame
for idx, row in df.iterrows():
    content = str(row['Content'])  # Just in case it’s not a string
    metadata_str = str(row['Metadata'])

    # Convert metadata string back to a dictionary (optional: keep it simple if needed)
    metadata = {"metadata": metadata_str}

    # Split content into chunks
    chunks = text_splitter.split_text(content)
    total_chunks += len(chunks)

    # Generate embeddings for each chunk
    chunk_embeddings = embedding_model.encode(chunks)

    # Add each chunk to the Chroma collection
    for i, chunk in enumerate(chunks):
        collection.add(
            documents=[chunk],
            metadatas=[metadata],
            ids=[f"{idx}_chunk_{i}"],
            embeddings=[chunk_embeddings[i]]
        )

# ---------------------- Config ----------------------
SIMILARITY_THRESHOLD = 0.75
client1 = OpenAI(base_url="https://openrouter.ai/api/v1", api_key=API_KEY)  # Replace with your OpenRouter API key

# ---------------------- Models ----------------------
semantic_model = SentenceTransformer("sentence-transformers/all-mpnet-base-v2")

# Load QA Data
with open("qa.json", "r", encoding="utf-8") as f:
    qa_data = json.load(f)

qa_questions = list(qa_data.keys())
qa_answers = list(qa_data.values())
qa_embeddings = semantic_model.encode(qa_questions, convert_to_tensor=True)
#-------------------------bm25---------------------------------

def detect_language(text):
    try:
        lang = detect(text)
        return 'french' if lang.startswith('fr') else 'english'
    except:
        return 'english'  # default fallback

def clean_and_tokenize(text, lang):
    tokens = word_tokenize(text.lower(), language=lang)
    try:
        stop_words = set(stopwords.words(lang))
        return [t for t in tokens if t not in stop_words]
    except:
        return tokens  # fallback if stopwords not found

def rerank_with_bm25(docs, query):
    lang = detect_language(query)
    
    tokenized_docs = [clean_and_tokenize(doc['content'], lang) for doc in docs]
    bm25 = BM25Okapi(tokenized_docs)
    
    tokenized_query = clean_and_tokenize(query, lang)
    scores = bm25.get_scores(tokenized_query)
    
    top_indices = sorted(range(len(scores)), key=lambda i: scores[i], reverse=True)[:3]
    return [docs[i] for i in top_indices]


# ---------------------- History-Aware CAG ----------------------
def retrieve_from_cag(user_query):
    query_embedding = semantic_model.encode(user_query, convert_to_tensor=True)
    cosine_scores = util.cos_sim(query_embedding, qa_embeddings)[0]
    best_idx = int(np.argmax(cosine_scores))
    best_score = float(cosine_scores[best_idx])

    print(f"[CAG] Best score: {best_score:.4f} | Closest question: {qa_questions[best_idx]}")
    if best_score >= SIMILARITY_THRESHOLD:
        return qa_answers[best_idx], best_score  # Only return the answer
    else:
        return None, best_score

# ---------------------- History-Aware RAG ----------------------
def retrieve_from_rag(user_query):
    # Combine history with current query
    #history_context = " ".join([f"User: {msg[0]} Bot: {msg[1]}" for msg in chat_history]) + " "
    #full_query = history_context + user_query
    #full_query= user_query
    print("Searching in RAG with history context...")

    query_embedding = embedding_model.encode(user_query)
    results = collection.query(query_embeddings=[query_embedding], n_results=5)  # Get top 5 first

    if not results or not results.get('documents'):
        return None

    # Build docs list
    documents = []
    for i, content in enumerate(results['documents'][0]):
        metadata = results['metadatas'][0][i]
        documents.append({
            "content": content.strip(),
            "metadata": metadata
            
        })
        print(metadata)

    #  Rerank with BM25
    top_docs = rerank_with_bm25(documents, user_query)

    print("BM25-selected top 3 documents:", top_docs)
    return top_docs

# ---------------------- Generation function (OpenRouter) ----------------------
def generate_via_openrouter(context, query, chat_history=None):
    print("\n--- Generating via OpenRouter ---")
    print("Context received:", context)

   
    prompt = f"""<s>[INST]
You are a Moodle expert assistant.
Instructions:
- Always respond in the same language as the question.
- Use only the provided documents below to answer.
- If the answer is not in the documents, simply say: "I don't know." / "Je ne sais pas."
- Cite only the sources you use, indicated at the end of each document like (Source: https://example.com).



Documents:
{context}

Question: {query}
Answer:
[/INST]
"""
    try:
        response = client1.chat.completions.create(
           # model="mistralai/mistral-7b-instruct:free",
            model="mistralai/mistral-small-3.1-24b-instruct:free",
            messages=[{"role": "user", "content": prompt}]
        )
        return response.choices[0].message.content.strip()
    except Exception as e:
        print(f"Erreur lors de la génération : {e}")
        return "Erreur lors de la génération."


# ---------------------- Main Chatbot ----------------------
def chatbot(query, chat_history):
    print("\n==== New Query ====")
    print("User Query:", query)

    # Try to retrieve from CAG (cache)
    answer, score = retrieve_from_cag(query)
    if answer:
        print("Answer retrieved from CAG cache.")
      
        return answer

    # If not found, retrieve from RAG
    docs = retrieve_from_rag(query)
    if docs:
        context_blocks = []
        for doc in docs:
            content = doc.get("content", "").strip()
            metadata = doc.get("metadata") or {}
            source = "Source inconnue"

            if isinstance(metadata, dict):
                source_field = metadata.get("metadata", "")
                if isinstance(source_field, str) and source_field.startswith("source:"):
                    source = source_field.replace("source:", "").strip()

            context_blocks.append(f"{content}\n(Source: {source})")

        context = "\n\n".join(context_blocks)

        # Choose the generation backend (OpenRouter)
        response = generate_via_openrouter(context, query)
       # chat_history.append((query, response))  # Append the new question-answer pair to history
        return response

    else:
        print("No relevant documents found.")
       # chat_history.append((query, "Je ne sais pas."))
        return "Je ne sais pas."

# ---------------------- Gradio App ----------------------
def save_chat_to_file(chat_history):
    timestamp = time.strftime("%Y%m%d-%H%M%S")
    filename = f"chat_history_{timestamp}.json"

    # Create a temporary file
    temp_dir = tempfile.gettempdir()
    file_path = os.path.join(temp_dir, filename)

    # Write the chat history into the file
    with open(file_path, "w", encoding="utf-8") as f:
        json.dump(chat_history, f, ensure_ascii=False, indent=2)

    return file_path

def ask(user_message, chat_history):
    if not user_message:
        return chat_history , chat_history, ""

    response = chatbot(user_message, chat_history)
    chat_history.append((user_message, response))
    return chat_history , chat_history, ""

# Initialize chat history with a welcome messageinitial_message = (None, "Hello, how can I help you with Moodle?")
initial_message = (None, "Hello, how can I help you with Moodle?")

with gr.Blocks(theme=gr.themes.Soft()) as demo:
    chat_history = gr.State([initial_message])

    chatbot_ui = gr.Chatbot(value=[initial_message])
    question = gr.Textbox(placeholder="Ask me anything about Moodle...", show_label=False)
    clear_button = gr.Button("Clear")
    save_button = gr.Button("Save Chat")

    question.submit(ask, [question, chat_history], [chatbot_ui, chat_history, question])
    clear_button.click(lambda: ([initial_message], [initial_message], ""), None, [chatbot_ui, chat_history, question], queue=False)
    save_button.click(save_chat_to_file, [chat_history], gr.File(label="Download your chat history"))

demo.queue()
demo.launch(share=False)