app.py carol

app.py

@@ -0,0 +1,32 @@
+from transformers import pipeline
+import gradio as gr
+import base64
+import io
+
+# calling pipeline to get_completion
+get_completion = pipeline("image-to-text",model="carolcarneiro/keras-dummy-sequential-demo") #"sashakunitsyn/vlrm-blip2-opt-2.7b") #"Salesforce/blip2-opt-6.7b") # "Salesforce/blip-image-captioning-large" "nlpconnect/vit-gpt2-image-captioning"
+
+def summarize(input):
+    output = get_completion(input)
+    return output[0]['generated_text']
+
+def image_to_base64_str(pil_image):
+    byte_arr = io.BytesIO()
+    pil_image.save(byte_arr, format='PNG')
+    byte_arr = byte_arr.getvalue()
+    return str(base64.b64encode(byte_arr).decode('utf-8'))
+
+def captioner(image):
+    #base64_image = image_to_base64_str(image)
+    result = get_completion(image)
+    return result[0]['generated_text']
+
+gr.close_all()
+demo = gr.Interface(fn=captioner,
+                    inputs=[gr.Image(label="Upload image", type="pil")],
+                    outputs=[gr.Textbox(label="Caption")],
+                    title="Image Captioning Application",
+                    description="Caption the image you'd like to upload",
+                    allow_flagging="never")
+
+demo.launch()