Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+from huggingface_hub import HfApi, ModelFilter
+
+# Get the list of models from the Hugging Face Hub
+api = HfApi()
+models = api.list_models(filter=ModelFilter(tags="text-generation"))
+models_names = [model.modelId for model in models]
+
+# Dictionary to store loaded models and their pipelines
+model_pipelines = {}
+
+# Load a default model initially
+default_model_name = "gia-project/gia2-small-untrained"
+default_generator = pipeline("text-generation", model=default_model_name, trust_remote_code=True)
+model_pipelines[default_model_name] = default_generator
+
+def generate_text(model_name, input_text):
+    # Check if the selected model is already loaded
+    if model_name not in model_pipelines:
+        # Load the model and create a pipeline if it's not already loaded
+        generator = pipeline("text-generation", model=model_name, trust_remote_code=True)
+        model_pipelines[model_name] = generator
+    
+    # Get the pipeline for the selected model and generate text
+    generator = model_pipelines[model_name]
+    generated_text = generator(input_text)[0]['generated_text']
+    return generated_text
+
+# Define the Gradio interface
+iface = gr.Interface(
+    fn=generate_text,  # Function to be called on user input
+    inputs=[
+        gr.inputs.Dropdown(choices=models_names, label="Select Model", default=default_model_name),  # Dropdown to select model
+        gr.inputs.Textbox(lines=5, label="Input Text")  # Textbox for entering text
+    ],
+    outputs=gr.outputs.Textbox(label="Generated Text"),  # Textbox to display the generated text
+    title="GIA Text Generation",  # Title of the interface
+)
+
+# Launch the Gradio interface
+iface.launch()