feat: reorganize app

README.md

@@ -4,8 +4,8 @@ emoji: 🥑
 colorFrom: yellow
 colorTo: green
 sdk: streamlit
-app_file: app/app.py
-pinned: false
+app_file: app/streamlit/app.py
+pinned: True
 ---
 
 # DALL·E Mini

app/gradio/app_gradio_ngrok.py

@@ -1,89 +0,0 @@
-#!/usr/bin/env python
-# coding: utf-8
-
-import requests
-from PIL import Image
-import numpy as np
-import matplotlib.pyplot as plt
-from io import BytesIO
-import base64
-import os
-
-import gradio as gr
-
-from dalle_mini.helpers import captioned_strip
-
-
-backend_url = os.environ["BACKEND_SERVER"]
-
-
-class ServiceError(Exception):
-    def __init__(self, status_code):
-        self.status_code = status_code
-
-def get_images_from_ngrok(prompt):
-    r = requests.post(
-        backend_url, 
-        json={"prompt": prompt}
-    )
-    if r.status_code == 200:
-        images = r.json()["images"]
-        images = [Image.open(BytesIO(base64.b64decode(img))) for img in images]
-        return images
-    else:
-        raise ServiceError(r.status_code)
-        
-def run_inference(prompt):
-    try:
-        images = get_images_from_ngrok(prompt)
-        predictions = captioned_strip(images)
-        output_title = f"""
-        <p style="font-size:22px; font-style:bold">Best predictions</p>
-        <p>We asked our model to generate 128 candidates for your prompt:</p>
-
-        <pre>
-
-        <b>{prompt}</b>
-        </pre>
-        <p>We then used a pre-trained <a href="https://huggingface.co/openai/clip-vit-base-patch32">CLIP model</a> to score them according to the
-        similarity of the text and the image representations.</p>
-
-        <p>This is the result:</p>
-        """
-        
-        output_description = """
-        <p>Read our <a style="color:blue;" href="https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-mini--Vmlldzo4NjIxODA">full report</a> for more details on how this works.<p>
-        <p style='text-align: center'>Created with <a style="color:blue;" href="https://github.com/borisdayma/dalle-mini">DALL·E mini</a></p>
-        """
-
-    except ServiceError:
-        output_title = f"""
-        Sorry, there was an error retrieving the images. Please, try again later or <a href="mailto:[email protected]">contact us here</a>.
-        """
-        predictions = None
-        output_description = ""
-        
-    return (output_title, predictions, output_description)
-    
-outputs = [
-    gr.outputs.HTML(label=""),      # To be used as title
-    gr.outputs.Image(label=''),
-    gr.outputs.HTML(label=""),      # Additional text that appears in the screenshot
-]
-
-description = """
-Welcome to DALL·E-mini, a text-to-image generation model.
-"""
-gr.Interface(run_inference, 
-    inputs=[gr.inputs.Textbox(label='Prompt')],
-    outputs=outputs, 
-    title='DALL·E mini',
-    description=description,
-    article="<p style='text-align: center'> DALLE·mini by Boris Dayma et al. | <a href='https://github.com/borisdayma/dalle-mini'>GitHub</a></p>",
-    layout='vertical',
-    theme='huggingface',
-    examples=[['an armchair in the shape of an avocado'], ['snowy mountains by the sea']],
-    allow_flagging=False,
-    live=False,
-    # server_name="0.0.0.0",      # Bind to all interfaces
-).launch()

app/{app.py → streamlit/app.py}

@@ -1,7 +1,7 @@
 #!/usr/bin/env python
 # coding: utf-8
 
-from dalle_mini.backend import ServiceError, get_images_from_backend
+from .backend import ServiceError, get_images_from_backend
 import streamlit as st
 
 st.sidebar.markdown(
@@ -50,7 +50,7 @@ if prompt != "":
         <div class="st-b7">
         <div class="css-whx05o e13vu3m50">
         <div data-testid="stMarkdownContainer" class="css-1ekf893 e16nr0p30">
-                <img src="https://raw.githubusercontent.com/borisdayma/dalle-mini/main/app/img/loading.gif" width="30"/>
+                <img src="https://raw.githubusercontent.com/borisdayma/dalle-mini/main/app/streamlit/img/loading.gif" width="30"/>
                 Generating predictions for: <b>{prompt}</b>
         </div>
         </div>