Create app.py

app.py

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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()