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import gradio as gr
import torch
import numpy as np
from PIL import Image
import os
import json
import base64
from io import BytesIO
import requests
from typing import Dict, List, Any, Optional
from transformers.pipelines import pipeline
# Initialize the model
try:
model = pipeline("image-feature-extraction", model="nomic-ai/nomic-embed-vision-v1.5", trust_remote_code=True)
model_loaded = True
except Exception as e:
print(f"Error loading model: {str(e)}")
model = None
model_loaded = False
# Function to generate embeddings from an image
def generate_embedding(image):
if image is None:
return {"error": "No image provided"}, "No image provided"
if not model_loaded:
return {"error": "Model not loaded properly"}, "Error: Model not loaded properly"
# Convert to PIL Image if needed
if not isinstance(image, Image.Image):
try:
image = Image.fromarray(image)
except Exception as e:
print(f"Error converting image: {str(e)}")
return {"error": f"Invalid image format: {str(e)}"}, f"Error: Invalid image format"
try:
# Check if model is loaded before calling it
if model is None:
return {"error": "Model not loaded properly"}, "Error: Model not loaded properly"
# Generate embedding using the transformers pipeline
result = model(image)
# Process the result based on its type
embedding_list = None
# Handle different possible output types
if isinstance(result, torch.Tensor):
embedding_list = result.detach().cpu().numpy().flatten().tolist()
elif isinstance(result, np.ndarray):
embedding_list = result.flatten().tolist()
elif isinstance(result, list):
# If it's a list of tensors or arrays
if result and isinstance(result[0], (torch.Tensor, np.ndarray)):
embedding_list = result[0].flatten().tolist() if hasattr(result[0], 'flatten') else result[0]
else:
embedding_list = result
else:
# Try to convert to a list as a last resort
try:
if result is not None:
embedding_list = list(result)
else:
print("Result is None")
return {"error": "Failed to generate embedding"}, "Failed to generate embedding"
except:
print(f"Couldn't convert result of type {type(result)} to list")
return {"error": "Failed to process embedding"}, "Failed to process embedding"
# Ensure we have a valid embedding list
if embedding_list is None:
return {"error": "Failed to generate embedding"}, "Failed to generate embedding"
# Calculate embedding dimension
embedding_dim = len(embedding_list)
return {
"embedding": embedding_list,
"dimension": embedding_dim
}, f"Dimension: {embedding_dim}"
except Exception as e:
print(f"Error generating embedding: {str(e)}")
return {"error": f"Error generating embedding: {str(e)}"}, f"Error: {str(e)}"
# Create a Gradio app
app = gr.Interface(
fn=generate_embedding,
inputs=gr.Image(type="pil", label="Input Image"),
outputs=[
gr.JSON(label="Embedding Output"),
gr.Textbox(label="Embedding Dimension")
],
title="Nomic Vision Embedding Model (nomic-ai/nomic-embed-vision-v1.5)",
description="Upload an image to generate embeddings using the Nomic Vision model.",
allow_flagging="never"
)
# Launch the app
if __name__ == "__main__":
# For Huggingface Spaces, we need to specify the server name and port
app.launch(server_name="0.0.0.0", server_port=7860, mcp_server=True) |