Upload 4 files

app.py

@@ -1,4 +1,4 @@
-import gradio as gr
+import streamlit as st
 from PIL import Image
 import cv2 as cv
 
@@ -303,10 +303,8 @@ def visualize(model, data, dims):
         # t_img = transforms.Resize((dims[0], dims[1]))(t_img)
         img = Image.fromarray(np.uint8(fake_imgs[i]))
         img = cv.resize(fake_imgs[i], dsize=(dims[1], dims[0]), interpolation=cv.INTER_CUBIC)
-        return img
         # st.text(f"Size of fake image {fake_imgs[i].shape} \n Type of image = {type(fake_imgs[i])}")
-        # st.image(img, caption="Output image", use_column_width='auto', clamp=True)
-        
+        st.image(img, caption="Output image", use_column_width='auto', clamp=True)
         
 def log_results(loss_meter_dict):
     for loss_name, loss_meter in loss_meter_dict.items():
@@ -354,27 +352,14 @@ def make_dataloaders2(batch_size=16, n_workers=4, pin_memory=True, **kwargs): #
                             pin_memory=pin_memory)
     return dataloader
 
-def main_func(filepath):
-    im = Image.open(filepath)
-    size_text=f"Size of uploaded image {im.shape}"
-    # st.text(body=f"Size of uploaded image {im.shape}")
+file_up = st.file_uploader("Upload an jpg image", type="jpg")
+if file_up is not None: 
+    im = Image.open(file_up)
+    st.text(body=f"Size of uploaded image {im.shape}")
     a = im.shape
-    # st.image(im, caption="Uploaded Image.", use_column_width='auto')
+    st.image(im, caption="Uploaded Image.", use_column_width='auto')
     test_dl = make_dataloaders2(img_list=[im])
     for data in test_dl:
         model.setup_input(data)
         model.optimize()
-        img=visualize(model, data, a)
-    return (size_text,img)
-
-title = "PicSum"
-description = "Gradio demo for PicSum project. You can give an image as input on the left side and then click on the submit button. The generated text, summary, important sentences and fill in the gaps would be generated on the right side."
-gr.Interface(
-    extract, 
-    [gr.inputs.Image(type="filepath", label="Input"),gr.inputs.CheckboxGroup(choices, type="value", default=['Generate text'], label='Options') ], 
-    [gr.outputs.Textbox(label="Generated Text"),"image"],
-    title=title,
-    description=description,
-    # examples=[['a.png', ['Generate text']], ['b.png', ['Generate text','Summary','Important Sentences']], ]
-).launch(enable_queue=True)
-    
+        visualize(model, data, a)

requirements.txt

@@ -1,15 +1,22 @@
-gradio
+streamlit
+Pillow
 glob2
 numpy
 pathlib
 tqdm
-matplotlib
-matplotlib-venn
+matplotlib==3.2.2
+matplotlib-venn==0.11.7
 scikit-image
-torch==1.7
+torch
 torchvision
 torchsummary
 fastai==2.4
+fastcore==1.3.29
+fastdownload==0.0.7
+fastdtw==0.3.4
+fastjsonschema==2.16.2
+fastprogress==1.0.3
+fastrlock==0.8.1
 opencv-contrib-python==4.6.0.66
 opencv-python==4.6.0.66
 opencv-python-headless==4.6.0.66