Create app.py

app.py

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+# import packages
+import gradio as gr
+import copy
+from llama_cpp import Llama
+from huggingface_hub import hf_hub_download
+import chromadb
+from chromadb.utils.embedding_functions import OpenCLIPEmbeddingFunction
+from chromadb.utils.data_loaders import ImageLoader
+from chromadb.config import Settings
+from datasets import load_dataset
+import numpy as np
+from tqdm import tqdm
+import shutil
+import os
+from chromadb.utils import embedding_functions
+import gradio as gr
+from PIL import Image
+import requests
+from io import BytesIO
+from transformers import pipeline
+from bark import SAMPLE_RATE, generate_audio, preload_models
+
+# Initialize the Llama model
+llm = Llama(
+    ## original model
+    # model_path=hf_hub_download(
+    #     repo_id="microsoft/Phi-3-mini-4k-instruct-gguf",
+    #     filename="Phi-3-mini-4k-instruct-q4.gguf",
+    # ),
+    ## compressed model
+    model_path=hf_hub_download(
+        repo_id="TheBloke/CapybaraHermes-2.5-Mistral-7B-GGUF",
+        filename="capybarahermes-2.5-mistral-7b.Q2_K.gguf",
+    ),
+    n_ctx=2048,
+    n_gpu_layers=50,  # Adjust based on your VRAM
+)
+
+# use of clip model for embedding
+client = chromadb.PersistentClient(path="DB")
+
+embedding_function = OpenCLIPEmbeddingFunction()
+image_loader = ImageLoader() # must be if you reads from URIs
+
+# initialize separate collection for image and text data
+collection_images = client.create_collection(
+    name='collection_images',
+    embedding_function=embedding_function,
+    data_loader=image_loader)
+
+collection_text = client.create_collection(
+    name='collection_text',
+    embedding_function=embedding_function,
+    )
+
+# Get the uris to the images
+IMAGE_FOLDER = 'Moin_Von_Bremen/images'
+
+
+image_uris = sorted([os.path.join(IMAGE_FOLDER, image_name) for image_name in os.listdir(IMAGE_FOLDER) if not image_name.endswith('.txt')])
+ids = [str(i) for i in range(len(image_uris))]
+
+collection_images.add(ids=ids, uris=image_uris)