Update app.py

app.py

@@ -1,10 +1,7 @@
-# from __future__ import annotations
+from __future__ import annotations
 import gradio as gr
 from huggingface_hub import from_pretrained_fastai
 import os
-from typing import Iterable
-# from gradio.themes.base import Base
-# from gradio.themes.utils import colors, fonts, sizes
 
 os.environ["HF_ENDPOINT"] = "https://huggingface.co"
 
@@ -35,24 +32,6 @@ material_num_name_dict = {
   "others": "Others",
 }
 
-# No plastic bags, wrap, or film (please take back to a local retailer)
-# No Styrofoam
-# No single-use paper (no paper towels, napkins, tissues, or paper plates)
-# No clothing, shoes, or textiles (take gently used items to a donation center)
-# No chain, wire, hoses, or hangers of any kind
-# No lumber, scrap wood, or yard waste
-# No food or liquids
-# No soiled food boxes
-# No electronics
-# No furniture
-
-# Cardboard and boxboard (flatten boxes)
-# Paper: junk mail, magazines, newspaper (no napkins, tissues, paper towels, or paper plates)
-# Aluminum and steel cans (no scrap metal)
-# Plastic bottles, tubs, and jugs (no plastic bags, wrap, film, or Styrofoam)
-# Glass bottles and jars (remove all lids)
-# Poly-coated cartons
-
 plastic_item_num_dict = {
   "CD Disk": ["CD Disk", "No, find a local e-waste collection near you (refer to the info page!!!)"],
   "Straw": ["Straw", "No, dispose as general waste"],
@@ -94,66 +73,6 @@ paper_item_num_dict = {
   "Tissue Paper": ["Tissue Paper", "No, dispose as general waste"]
 }
 
-# class Seafoam(Base):
-#     def __init__(
-#         self,
-#         *,
-#         primary_hue: colors.Color | str = colors.emerald,
-#         secondary_hue: colors.Color | str = colors.teal,
-#         neutral_hue: colors.Color | str = colors.teal,
-#         spacing_size: sizes.Size | str = sizes.spacing_md,
-#         radius_size: sizes.Size | str = sizes.radius_md,
-#         text_size: sizes.Size | str = sizes.text_lg,
-#         font: fonts.Font
-#         | str
-#         | Iterable[fonts.Font | str] = (
-#             fonts.GoogleFont("Quicksand"),
-#             "ui-sans-serif",
-#             "sans-serif",
-#         ),
-#         font_mono: fonts.Font
-#         | str
-#         | Iterable[fonts.Font | str] = (
-#             fonts.GoogleFont("IBM Plex Mono"),
-#             "ui-monospace",
-#             "monospace",
-#         ),
-#     ):
-#         super().__init__(
-#             primary_hue=primary_hue,
-#             secondary_hue=secondary_hue,
-#             neutral_hue=neutral_hue,
-#             spacing_size=spacing_size,
-#             radius_size=radius_size,
-#             text_size=text_size,
-#             font=font,
-#             font_mono=font_mono,
-#         )
-        
-#         super().set(
-#             body_background_fill="linear-gradient(45deg, *primary_200, *primary_200 10px, *primary_50 10px, *primary_50 20px)",
-#             body_background_fill_dark="linear-gradient(45deg, *primary_200, *primary_200 10px, *primary_50 10px, *primary_50 20px)",
-#             stat_background_fill_dark="linear-gradient(to right, *primary_400, *primary_200)",
-#             error_background_fill_dark=f"linear-gradient(to right, {colors.red.c100}, *background_fill_secondary)",
-#             button_primary_background_fill="linear-gradient(90deg, *primary_300, *secondary_400)",
-#             button_primary_background_fill_hover="linear-gradient(90deg, *primary_200, *secondary_300)",
-#             button_primary_background_fill_hover_dark="linear-gradient(90deg, *primary_200, *secondary_300)",
-#             button_primary_text_color="white",
-#             button_primary_background_fill_dark="linear-gradient(90deg, *primary_300, *secondary_400)",
-#             slider_color="*secondary_300",
-#             slider_color_dark="*secondary_300",
-#             block_title_text_weight="600",
-#             block_border_width="3px",
-#             block_shadow="*shadow_drop_lg",
-#             button_shadow="*shadow_drop_lg",
-#             button_large_padding="32px",
-#         )
-#         for k in list(self.__dict__.keys()):
-#             if '_dark' in k:
-#                 setattr(self,k,None)
-
-# seafoam = Seafoam()
-
 def predict_image(inp):
     """
     Performs inference for a given input image and returns the prediction and CAM image.
@@ -201,68 +120,6 @@ def predict_image(inp):
     except:
       raise Exception("Invalid file format! Please only upload .jpg or .png files!")
 
-
-with gr.Blocks(title="Trash Classification", css="#custom_header {min-height: 3rem} #custom_title {min-height: 3rem; text-align: center}") as demo:#, theme=seafoam) as demo:
+with gr.Blocks(title="Trash Classification") as demo:
     gr.Markdown("# Check whether your trash is recyclable or not!", elem_id="custom_title")
-    gr.Markdown("Gradio Inference interface for classification of trash and recyclables. To use it, simply upload your image, or click one of the examples to load them. Images uploaded are never saved or indexed. No data or images uploaded are saved or logged.", elem_id="custom_title")
-
-    with gr.Column():
-        with gr.Column():
-            with gr.Group():
-                gr.Markdown("## Inputs", elem_id="custom_header")
-                input_image = gr.Image(label="Input Image")
-                input_image.scale(height=240)
-                btn = gr.Button(value="Submit")
-                btn.scale(full_width=True)
-        with gr.Column():
-            with gr.Group():
-                gr.Markdown("## Outputs", elem_id="custom_header")
-                recycling_qn = gr.outputs.Textbox(label="Is this recyclable?")
-                recycling_advice = gr.outputs.Textbox(label="It is not recyclable when:")
-                with gr.Row():
-                    material_probs = gr.outputs.Label(label="Material Prediction")
-                    item_probs = gr.outputs.Label(label="Item Prediction")
-                
-    gr.Examples(
-        examples=examples,
-        inputs=input_image,
-        fn=predict_image,
-        cache_examples=False,
-    )
-
-    btn.click(predict_image, inputs=[input_image],
-              outputs=[material_probs, item_probs, recycling_qn, recycling_advice])
-
-# css = """
-# #warning {background-color: #FFCCCB}
-# #custom_header {min-height: 3rem} #custom_title {min-height: 3rem; text-align: center}
-# .feedback textarea {font-size: 24px !important}
-# """
-
-# with gr.Blocks(title="Green Genius", css=css, theme=gr.themes.Monochrome()
-# ) as demo:
-  
-#     with gr.Column():
-#         with gr.Column():
-#                 input_image = gr.Image(label="Input Image")
-#                 input_image.style(height=240)
-#                 btn = gr.Button(value="Submit")
-#                 btn.style(full_width=True)
-#         with gr.Column():
-#               gr.Markdown("# We think this is?",elem_id="custom_header")
-#               with gr.Row():
-#                 item_probs = gr.outputs.Label(label=" ",num_top_classes=1)
-#                 material_probs = gr.outputs.Label(label=" ",num_top_classes=1)
-#               gr.Markdown("# Can it Recycle?", elem_id="custom_header")
-#               recycling_qn = gr.outputs.Label(label=" ",)
-#               gr.Markdown("# This Cant be Recycled when: ",elem_id="custom_header")
-#               recycling_advice = gr.outputs.Label(label=" ",)
-              
-                
-                
-
-#     btn.click(predict_image, inputs=[input_image],
-#               outputs=[material_probs, item_probs, recycling_qn, recycling_advice])
-
-if __name__ == "__main__":
-    demo.launch(show_error=True)
+    gr.Markdown("Gradio Inference interface for classification of trash and recyclables. To use it, simply upload your image, or click one of the examples to load them. Images uploaded are never saved or indexed. No data