Create app.py

Initial commit

app.py

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+import os
+import warnings
+import gradio as gr
+from transformers import pipeline
+from transformers.utils import logging
+
+# Ignore warning that are not application related
+logging.set_verbosity_error()
+warnings.filterwarnings("ignore",category=FutureWarning)
+warnings.filterwarnings("ignore", message="Using the model-agnostic default `max_length`")
+
+# Load model
+image_captioning = pipeline("image-to-text", model="Salesforce/blip-image-captioning-base")
+
+# Perform inference
+def launch(input):
+    out= image_captioning(input)
+    return out[0]['generated_text']
+
+# Create the gradio interface
+int_face = gr.Interface(
+    launch,
+    inputs=gr.Image(type="pil"),
+    outputs="text"
+)
+
+# Run the gradio interface
+int_face.launch()