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import gradio as gr
from gradio_huggingfacehub_search import HuggingfaceHubSearch
from transformers import pipeline, Pipeline
from transformers.pipelines import PipelineException
from huggingface_hub.utils import ModelNotFoundError
import logging
# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
# Initialize Hugging Face Hub search component
search_in = HuggingfaceHubSearch(api_key="hf_YourAPITokenHere", submit_on_select=True)
# Function to load the selected model and create a pipeline
def load_model(model_id):
try:
logger.info(f"Loading model: {model_id}")
model_pipeline = pipeline(model=model_id)
logger.info("Model loaded successfully.")
return model_pipeline
except ModelNotFoundError:
logger.error(f"Model '{model_id}' not found.")
return None
except PipelineException as e:
logger.error(f"Error creating pipeline: {e}")
return None
except Exception as e:
logger.error(f"Unexpected error: {e}")
return None
# Function to process input data using the loaded pipeline
def process_input(model_pipeline, input_data):
try:
logger.info("Processing input data.")
output = model_pipeline(input_data)
logger.info("Processing complete.")
return output
except Exception as e:
logger.error(f"Error during processing: {e}")
return None
# Gradio interface setup
def create_interface():
with gr.Blocks() as demo:
gr.Markdown("# Transformers Pipeline Playground")
model_id = gr.Textbox(label="Enter Model ID from Hugging Face Hub")
input_data = gr.Textbox(label="Input Data")
output_data = gr.Textbox(label="Output Data")
load_button = gr.Button("Load Model")
process_button = gr.Button("Process Input")
# Load model on button click
def on_load_click():
model_pipeline = load_model(model_id.value)
if model_pipeline:
process_button.click(
fn=lambda: process_input(model_pipeline, input_data.value),
inputs=[],
outputs=output_data,
)
else:
output_data.value = "Failed to load model."
load_button.click(on_load_click, inputs=[], outputs=[])
return demo
# Run the Gradio interface
if __name__ == "__main__":
demo = create_interface()
demo.launch() |