app.py

import gradio as gr
import chromadb
from transformers import AutoTokenizer, AutoModel
import faiss
import numpy as np
import torch

# Load the pre-trained model and tokenizer
model_name = "sentence-transformers/all-MiniLM-L6-v2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)

# Initialize Chroma client
client = chromadb.Client()

# Create a Chroma collection
collection = client.create_collection(name="tree_images")

# Example data (you can replace this with your actual content or dataset)
content = ["Tree 1: Decorated with lights", "Tree 2: Undecorated", "Tree 3: Decorated with ornaments"]

# Function to generate embeddings using the pre-trained model
def generate_embeddings(texts):
    embeddings = []
    for text in texts:
        inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
        with torch.no_grad():
            output = model(**inputs)
            embeddings.append(output.last_hidden_state.mean(dim=1).squeeze().numpy())
    return embeddings

# Generate embeddings for the content
embeddings = generate_embeddings(content)

# Add the embeddings to Chroma
for idx, text in enumerate(content):
    collection.add(embedding=embeddings[idx], document=text, metadatas={"id": idx})

# Build FAISS index for efficient retrieval
embeddings_np = np.array(embeddings).astype('float32')
faiss_index = faiss.IndexFlatL2(embeddings_np.shape[1])
faiss_index.add(embeddings_np)

# Define the search function for Gradio interface
def search(query):
    # Generate embedding for the query
    query_embedding = generate_embeddings([query])[0].reshape(1, -1)
    
    # FAISS-based search
    distances, indices = faiss_index.search(query_embedding, 3)
    faiss_results = [content[i] for i in indices[0]]
    
    # Chroma-based search
    chroma_results = collection.query(query_embeddings=query_embedding, n_results=3)["documents"]
    
    # Return results
    return "FAISS Results: " + ", ".join(faiss_results) + "\nChroma Results: " + ", ".join(chroma_results)

# Create the Gradio interface
interface = gr.Interface(fn=search, inputs="text", outputs="text")

# Launch the Gradio interface
interface.launch()