app.py

import os
import gradio as gr
import faiss
import pickle
from PyPDF2 import PdfReader
from sentence_transformers import SentenceTransformer
from huggingface_hub import InferenceClient, HfApi
import pdfplumber

# Hugging Face Space persistence
HF_REPO_ID = "MoslemBot/kajibuku"  # e.g., "username/your-space-name"
HF_API_TOKEN = os.getenv("HF_TOKEN")
api = HfApi()

def upload_to_hub(local_path, remote_path):
    api.upload_file(
        path_or_fileobj=local_path,
        path_in_repo=remote_path,
        repo_id=HF_REPO_ID,
        repo_type="space",
        token=HF_API_TOKEN
    )
    print(f"✅ Uploaded to Hub: {remote_path}")

# Initialize embedder and LLM client
embedder = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
llm = InferenceClient(token=os.getenv("HF_TOKEN"))

DATA_DIR = "data"
os.makedirs(DATA_DIR, exist_ok=True)

# Save uploaded PDF and index its content
def save_pdf(file, title):
    folder = os.path.join(DATA_DIR, title.strip())
    if os.path.exists(folder):
        return f"'{title}' already exists. Use a different title."

    os.makedirs(folder, exist_ok=True)

    # Extract text
    # reader = PdfReader(file.name)
    # full_text = "\n".join(p.extract_text() for p in reader.pages if p.extract_text())

    with pdfplumber.open(file.name) as pdf:
        full_text = ""
        for page in pdf.pages:
            full_text += page.extract_text() + "\n"
        
    print(full_text)

    # Chunk text
    chunks = [full_text[i:i+500] for i in range(0, len(full_text), 500)]

    # Embed and index
    embeddings = embedder.encode(chunks)
    
    print("Embeddings shape:", embeddings.shape)
    if len(embeddings.shape) != 2:
        raise ValueError(f"Expected 2D embeddings, got shape {embeddings.shape}")
        
    index = faiss.IndexFlatL2(embeddings.shape[1])
    index.add(embeddings)

    # Save index and chunks locally
    index_path = os.path.join(folder, "index.faiss")
    chunks_path = os.path.join(folder, "chunks.pkl")
    faiss.write_index(index, index_path)
    with open(chunks_path, "wb") as f:
        pickle.dump(chunks, f)

    # Upload to hub
    upload_to_hub(index_path, f"data/{title}/index.faiss")
    upload_to_hub(chunks_path, f"data/{title}/chunks.pkl")

    return f"✅ Saved and indexed '{title}', and uploaded to Hub. Please reload (refresh) the page."

# Return all available PDF titles
def list_titles():
    print(f"Listing in: {DATA_DIR} → {os.listdir(DATA_DIR)}")
    return [d for d in os.listdir(DATA_DIR) if os.path.isdir(os.path.join(DATA_DIR, d))]

# Ask question using selected PDFs as context
def ask_question(message, history, selected_titles):
    if not selected_titles:
        return "❗ Please select at least one PDF."

    combined_answer = ""
    for title in selected_titles:
        folder = os.path.join(DATA_DIR, title)
        try:
            index = faiss.read_index(os.path.join(folder, "index.faiss"))
            with open(os.path.join(folder, "chunks.pkl"), "rb") as f:
                chunks = pickle.load(f)

            q_embed = embedder.encode([message])
            D, I = index.search(q_embed, k=3)
            context = "\n".join([chunks[i] for i in I[0]])

            #prompt = f"Context:\n{context}\n\nQuestion: {message}\nAnswer:"
            #print(prompt)
            response = llm.chat_completion(
                messages=[
                    {"role": "system", "content": "You are a helpful assistant. Answer based only on the given context."},
                    {"role": "user", "content": f"Context:\n{context}\n\nQuestion: {message}"}
                ],
                model="deepseek-ai/DeepSeek-R1-0528",
                max_tokens=2048,
            )
            
            response = response.choices[0].message["content"]
  
            #response = llm.text_generation(prompt, max_new_tokens=200)
            #print(response)
            combined_answer += f"**{title}**:\n{response.strip()}\n\n"
        except Exception as e:
            combined_answer += f"⚠️ Error with {title}: {str(e)}\n\n"

    return combined_answer.strip()

# Gradio UI
with gr.Blocks() as demo:
    with gr.Tab("📤 Upload PDF"):
        file = gr.File(label="PDF File", file_types=[".pdf"])
        title = gr.Textbox(label="Title for PDF")
        upload_btn = gr.Button("Upload and Index")
        upload_status = gr.Textbox(label="Status")
        upload_btn.click(fn=save_pdf, inputs=[file, title], outputs=upload_status)

    with gr.Tab("💬 Chat with PDFs"):
        pdf_selector = gr.CheckboxGroup(label="Select PDFs", choices=list_titles())
        refresh_btn = gr.Button("🔄 Refresh PDF List")
        refresh_btn.click(fn=list_titles, outputs=pdf_selector)
        chat = gr.ChatInterface(fn=ask_question, additional_inputs=[pdf_selector])

demo.launch()