Create app.py

app.py

@@ -0,0 +1,84 @@
+import gradio as gr
+import shap
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+if torch.cuda.is_available():
+    device = "cuda"
+else:
+    device = "cpu"
+
+MODEL_NAME = "gpt2"
+
+model = AutoModelForCausalLM.from_pretrained(MODEL_NAME)
+
+# set model decoder to true
+model.config.is_decoder = True
+# set text-generation params under task_specific_params
+model.config.task_specific_params["text-generation"] = {
+    "do_sample": True,
+    "max_length": 50,
+    "temperature": 0.7,
+    "top_k": 50,
+    "no_repeat_ngram_size": 2,
+}
+model = model.to(device)
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+explainer = shap.Explainer(model, tokenizer)
+
+
+def start_experiment():
+    """Returns an APIExperiment object that is thread safe
+    and can be used to log inferences to a single Experiment
+    """
+    try:
+        api = comet_ml.API()
+        workspace = api.get_default_workspace()
+        project_name = comet_ml.config.get_config()["comet.project_name"]
+
+        experiment = comet_ml.APIExperiment(
+            workspace=workspace, project_name=project_name
+        )
+        experiment.log_other("Created from", "gradio-inference")
+
+        message = f"Started Experiment: [{experiment.name}]({experiment.url})"
+
+        return (experiment, message)
+
+    except Exception as e:
+        return None, None
+
+
+def predict(text, state, message):
+    experiment = state
+
+    shap_values = explainer([text])
+    plot = shap.plots.text(shap_values, display=False)
+
+    if experiment is not None:
+        experiment.log_other("message", message)
+        experiment.log_html(plot)
+
+    return plot
+
+
+with gr.Blocks() as demo:
+    start_experiment_btn = gr.Button("Start New Experiment")
+    experiment_status = gr.Markdown()
+
+    # Log a message to the Experiment to provide more context
+    experiment_message = gr.Textbox(label="Experiment Message")
+    experiment = gr.State()
+
+    input_text = gr.Textbox(label="Input Text", lines=5, interactive=True)
+    submit_btn = gr.Button("Submit")
+
+    output = gr.HTML()
+
+    start_experiment_btn.click(
+        start_experiment, outputs=[experiment, experiment_status]
+    )
+    submit_btn.click(
+        predict, inputs=[input_text, experiment, experiment_message], outputs=[output]
+    )