Create app.py

app.py

@@ -0,0 +1,36 @@
+import os
+import gradio as gr
+
+def get_openai_key():
+    return os.getenv("OPENAI_API_KEY","")
+
+openai_api_key = gr.inputs.Textbox(value=get_openai_key(),label="OpenAI API Key",type="password")
+image_input = gr.inputs.Image(type='filepath', label="Input Image")
+
+title_with_logo = \
+    f'Understanding Image with Text'
+
+
+interface = gr.Interface(
+    inputs=[openai_api_key,
+            image_input,        
+            gr.CheckboxGroup(
+            label="Options",
+            choices=["Image Generation", "Semantic Segment"],
+            ),
+            ],
+    outputs=gr.outputs.HTML(),
+    title=title_with_logo,
+    description=extra_title +"""
+    Image.txt. This code support image to text transformation. Then the generated text can do retrieval, question answering et al to conduct zero-shot.
+    \n Github: https://github.com/showlab/Image2Paragraph
+    \n Twitter: https://twitter.com/awinyimgprocess/status/1646225454599372800?s=46&t=HvOe9T2n35iFuCHP5aIHpQ
+    \n For online demo, we use smallest model to speed up. For better result, look for github for using large models.
+    \n Ttext2image model is controlnet, which used canny edge as reference.
+    \n To speed up, we generate image with small size 384, run the code local for high-quality sample.
+    """
+)
+
+# Launch the interface
+interface.launch()
+