Create app.py

app.py

@@ -0,0 +1,42 @@
+import numpy as np
+import requests
+import gradio as gr
+from transformers import pipeline
+
+translator = pipeline("translation", model="Helsinki-NLP/opus-mt-en-de")
+
+def blipofasinki(input_img): 
+    b64_string = gr.processing_utils.encode_url_or_file_to_base64(input_img)
+    #blip-Nucleus
+    responsen = requests.post(url='https://hf.space/embed/Salesforce/BLIP/+/api/predict/', json={"data": [ b64_string,"Image Captioning","None",str('Nucleus sampling')]})
+    jresn = responsen.json()
+    capn = jresn["data"][0]
+    offset = len(str("caption:"))
+    capn = capn[offset:]
+    trans_capn = translator(capn)
+    tcn = trans_capn[0]['translation_text']
+    #blip-beam
+    responseb = requests.post(url='https://hf.space/embed/Salesforce/BLIP/+/api/predict/', json={"data": [ b64_string,"Image Captioning","None",str('Beam search')]})
+    jresb = responseb.json()
+    capb = jresb["data"][0]
+    capb = capb[offset:]
+    trans_capb = translator(capb)
+    tcb = trans_capb[0]['translation_text']
+    #ofa
+    responseo = requests.post(url='https://hf.space/embed/OFA-Sys/OFA-Image_Caption/+/api/predict/', json={"data": [b64_string]})
+    jreso = responseo.json()
+    capo = jreso["data"][0]
+    trans_capo = translator(capo)
+    tco = trans_capo[0]['translation_text']
+    return [tcn, tcb, tco]
+    
+
+description = "A direct comparison in image captioning between BLIP and OFA (in German translated with Helsinki)."
+
+input_ = [gr.inputs.Image(type='filepath', label="Input Image")]
+
+output_ = [gr.outputs.Textbox(label="BLIP Nucleus sampling output"),gr.outputs.Textbox(label="BLIP Beam search output"), gr.outputs.Textbox(label="OFA output")]
+
+iface = gr.Interface(blipofasinki, input_, output_, description=description)
+
+iface.launch(debug=True,show_error=True)