app.py

import gradio as gr
from sentence_transformers import SentenceTransformer
import torch

# 1. Load the text database
with open("dogs.txt", "r", encoding="utf-8") as file:
    dogs_text = file.read()

cleaned_text = dogs_text.strip()
chunks = cleaned_text.split("\n")
cleaned_chunks = [chunk.strip() for chunk in chunks if chunk.strip()]

# 2. Load model and embed text chunks
model = SentenceTransformer('all-MiniLM-L6-v2')
chunk_embeddings = model.encode(cleaned_chunks, convert_to_tensor=True)

# 3. Define retrieval function
def get_top_chunks(query, top_k=3):
    query_embedding = model.encode(query, convert_to_tensor=True)
    query_embedding_normalized = query_embedding / query_embedding.norm()

    chunk_embeddings_normalized = chunk_embeddings / chunk_embeddings.norm(dim=1, keepdim=True)

    similarities = torch.matmul(chunk_embeddings_normalized, query_embedding_normalized)
    top_indices = torch.topk(similarities, k=top_k).indices

    top_chunks = [cleaned_chunks[i] for i in top_indices]

    return top_chunks

# 4. Define chatbot response function
def responses(message, history):
    top_chunks = get_top_chunks(message)
    context = "\n".join(top_chunks)
    
    # Simple template generator
    answer = f"Based on what I found:\n{context}\n\nHope this helps answer your question!"
    
    return answer

# 5. Gradio chat interface
demo = gr.ChatInterface(responses, title="Dogs RAG Chatbot")
demo.launch()