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import modal
from src.gradio_interface import demo
# Print debug information
print("Importing Modal and setting up the app...")
# Define the Modal app
app = modal.App(name="example-app")
# Define a custom image with Python and some dependencies
print("Building custom image...")
image = (
modal.Image.debian_slim(python_version="3.11") # Base image
.pip_install(
"numpy",
"pandas",
"diffusers",
"transformers",
"torch",
"accelerate",
"gradio",
"safetensors",
"pillow",
) # Install Python packages
.run_commands("echo 'Image build complete!'") # Run a shell command
)
# Define a function to run inside the container
@app.function(image=image)
def main():
# Debug: Print a message when the function starts
print("Starting main function inside the container...")
# Import libraries and print their versions
import numpy as np
import pandas as pd
import torch
import diffusers
import transformers
import gradio as gr
from PIL import Image as PILImage
print("Hello from Modal!")
print("NumPy version:", np.__version__)
print("Pandas version:", pd.__version__)
print("PyTorch version:", torch.__version__)
print("Diffusers version:", diffusers.__version__) # Corrected: Use the library's __version__
print("Transformers version:", transformers.__version__) # Corrected: Use the library's __version__
print("Gradio version:", gr.__version__)
print("Pillow version:", PILImage.__version__)
# Create a simple DataFrame
df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
print("DataFrame:\n", df)
# Test PyTorch
tensor = torch.tensor([1, 2, 3])
print("PyTorch tensor:", tensor)
# Test Diffusers (load a simple pipeline)
print("Loading Diffusers pipeline...")
pipe = diffusers.DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
print("Diffusers pipeline loaded successfully!")
# Test Gradio (create a simple interface)
def greet(name):
return f"Hello {name}!"
print("Creating Gradio interface...")
iface = gr.Interface(fn=greet, inputs="text", outputs="text")
print("Gradio interface created successfully!")
# Debug: Print a message when the function ends
print("Main function execution complete!")
# Launch gradio-interface
demo.launch()
# Run the function locally (for testing)
if __name__ == "__main__":
print("Running the function locally...")
main.local() |