app.py

import gradio as gr
from docx import Document
from sentence_transformers import SentenceTransformer
from transformers import AutoTokenizer, AutoModelForCausalLM
import faiss
import torch
import numpy as np

# Load .docx file
def load_docx_text(path):
    doc = Document(path)
    return "\n".join([p.text for p in doc.paragraphs if p.text.strip() != ""])

# Make sure this filename matches the uploaded file
text_data = load_docx_text("8_laws.docx")

# Chunk text
def chunk_text(text, chunk_size=300, overlap=50):
    words = text.split()
    return [" ".join(words[i:i+chunk_size]) for i in range(0, len(words), chunk_size - overlap)]

doc_chunks = chunk_text(text_data)

# Embed text
embedder = SentenceTransformer("all-MiniLM-L6-v2")
doc_embeddings = embedder.encode(doc_chunks)

# Build FAISS index
index = faiss.IndexFlatL2(doc_embeddings.shape[1])
index.add(np.array(doc_embeddings))

# Load TinyLLaMA (CPU safe)
tokenizer = AutoTokenizer.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v1.0")
model = AutoModelForCausalLM.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v1.0")

# RAG logic
def retrieve_context(query, k=3):
    query_vec = embedder.encode([query])
    _, indices = index.search(np.array(query_vec), k)
    return [doc_chunks[i] for i in indices[0]]

def generate_answer(question):
    try:
        context = "\n".join(retrieve_context(question))
        prompt = f"""Use the context below to answer the question.

Context:
{context}

Question:
{question}

Answer:"""

        print("🧠 Prompt:\n", prompt)

        inputs = tokenizer(prompt, return_tensors="pt")
        output = model.generate(**inputs, max_new_tokens=150)
        answer = tokenizer.decode(output[0], skip_special_tokens=True)
        return answer
    except Exception as e:
        print("❌ ERROR:", str(e))
        return f"An error occurred: {e}"

# Gradio interface
demo = gr.Interface(
    fn=generate_answer,
    inputs=gr.Textbox(lines=2, placeholder="Ask a question..."),
    outputs="text",
    title="📘 TinyLLaMA DOCX RAG",
    description="Ask a question about the 8 laws of health"
)

demo.launch()