Delete app.py

app.py

@@ -1,224 +0,0 @@
-import streamlit as st
-import numpy as np
-from PIL import Image
-import torch
-import torch.nn as nn
-from transformers import SegformerForSemanticSegmentation
-from transformers import AutoTokenizer
-from transformers import AutoImageProcessor
-from huggingface_hub import hf_hub_url, cached_download
-from tensorflow.keras.applications import EfficientNetV2B0
-from keras.layers import GlobalAveragePooling2D, Dense
-from keras.models import Model
-from tensorflow.keras.optimizers import Adam
-from tensorflow.keras.preprocessing.image import load_img, img_to_array
-
-# Load SegFormer model
-model_id_seg = "nvidia/mit-b0"
-image_processor = AutoImageProcessor.from_pretrained(model_id_seg, size=(128, 128))
-#id2label = {0: "na", 1:"anillo", 2:"nervio"}
-#label2id = { v:k for k, v in id2label.items()}
-#model_seg = AutoModelForSemanticSegmentation.from_pretrained(model_id_seg, id2label=id2label, label2id=label2id)
-
-# Load SegFormer model with trained weights
-repo_id_seg = "ferferefer/segformer"
-#filename_seg = "model.ckpt"
-#model_file_seg = cached_download(hf_hub_url(repo_id_seg, filename_seg))
-
-model_seg = SegformerForSemanticSegmentation.from_pretrained(repo_id_seg)
-
-# Function to preprocess and obtain predictions from SegFormer model
-def obtener_predicciones(model, sample_batch):
-    processed_batch = image_processor(sample_batch, return_tensors="pt")
-    pixel_values = processed_batch.pixel_values
-    outputs = model(pixel_values=pixel_values)
-    logits = outputs.logits
-
-    upsampled_logits = nn.functional.interpolate(
-        logits,
-        size=sample_batch[0].size[::-1],
-        mode="bilinear",
-        align_corners=False,
-    )
-    pred_seg = upsampled_logits.argmax(dim=1)
-    return pred_seg
-
-# Function to calculate centroids of the segmented image
-def calcular_centro_imagen(masks):
-    centroid_list = []
-    imagenes_transformadas = []
-
-    for mask in  masks:
-
-      image = np.transpose(np.argwhere(mask.cpu()==1))
-
-
-      x = [p[0] for p in image]
-      y = [p[1] for p in image]
-      centroid = (sum(x) / len(image), sum(y) / len(image))
-      centroid_list.append(centroid)
-      imagenes_transformadas.append(image)
-
-    return centroid_list,imagenes_transformadas
-
-# Function to crop the segmented image based on centroids
-def recortar_imagen(centroids, mascara_final,images):
-  lista_img_recortadas = []
-
-  for counter, image in enumerate (images):
-    max_distance = 0
-
-    for x, y in mascara_final[counter]:
-
-      distance = np.sqrt((x - centroids[counter][0]) ** 2 + (y - centroids[counter][1]) ** 2)
-
-      if distance > max_distance:
-
-        max_distance = distance
-
-    centroid_uno = int(centroids[counter][1].item())
-
-    centroid_cero = int(centroids[counter][0].item())
-
-    max_distance = int(max_distance.item())
-
-    image = image.cpu().numpy()
-
-    #image = np.transpose(image, (1, 2, 0))
-
-
-    #image = np.clip(image, 0, 1, dtype=np.float32)
-
-    a = centroid_cero - int(max_distance * 2)
-
-    b = centroid_cero + int(max_distance * 2)
-
-    c = centroid_uno - int(max_distance * 2)
-
-    d = centroid_uno + int(max_distance * 2)
-
-    height, width, _ = image.shape
-    pad_size = max_distance * 2
-
-    if a < 0:
-      crop_img = image[
-        0:centroid_cero + int(max_distance * 2),
-        centroid_uno - int(max_distance * 2):centroid_uno + int(max_distance * 2)]
-
-      pad_top = max(0, pad_size - centroid_cero)
-      pad_bottom = max(0, pad_size + centroid_cero - height)
-      pad_left = max(0, pad_size - centroid_uno)
-      pad_right = max(0, pad_size + centroid_uno - width)
-
-      padded_img = np.pad(crop_img, ((pad_top, pad_bottom), (pad_left, pad_right), (0, 0)), mode='constant', constant_values=0)
-      padded_img = torch.from_numpy(padded_img)
-      lista_img_recortadas.append(padded_img)
-    elif b > height:
-      crop_img = image[
-        centroid_cero - int(max_distance*2):height,
-        centroid_uno - int(max_distance * 2):centroid_uno + int(max_distance * 2)]
-
-      pad_top = max(0, pad_size - centroid_cero)
-      pad_bottom = max(0, pad_size + centroid_cero - height)
-      pad_left = max(0, pad_size - centroid_uno)
-      pad_right = max(0, pad_size + centroid_uno - width)
-
-      padded_img = np.pad(crop_img, ((pad_top, pad_bottom), (pad_left, pad_right), (0, 0)), mode='constant', constant_values=0)
-      padded_img = torch.from_numpy(padded_img)
-      lista_img_recortadas.append(padded_img)
-    elif c < 0:
-      crop_img = image[
-      centroid_cero-int(max_distance * 2):centroid_cero + int(max_distance * 2),
-        0:centroid_uno + int(max_distance * 2)]
-
-      pad_top = max(0, pad_size - centroid_cero)
-      pad_bottom = max(0, pad_size + centroid_cero - height)
-      pad_left = max(0, pad_size - centroid_uno)
-      pad_right = max(0, pad_size + centroid_uno - width)
-
-      padded_img = np.pad(crop_img, ((pad_top, pad_bottom), (pad_left, pad_right), (0, 0)), mode='constant', constant_values=0)
-      padded_img = torch.from_numpy(padded_img)
-
-      lista_img_recortadas.append(padded_img)
-    elif d > width:
-      crop_img = image[
-        centroid_cero - int(max_distance *2):centroid_cero + int(max_distance * 2),
-        centroid_uno - int(max_distance * 2):width]
-
-      pad_top = max(0, pad_size - centroid_cero)
-      pad_bottom = max(0, pad_size + centroid_cero - height)
-      pad_left = max(0, pad_size - centroid_uno)
-      pad_right = max(0, pad_size + centroid_uno - width)
-
-      padded_img = np.pad(crop_img, ((pad_top, pad_bottom), (pad_left, pad_right), (0, 0)), mode='constant', constant_values=0)
-      padded_img = torch.from_numpy(padded_img)
-      lista_img_recortadas.append(padded_img)
-
-    else:
-      crop_img = image[
-          centroid_cero - int(max_distance * 2):centroid_cero + int(max_distance * 2),
-          centroid_uno - int(max_distance * 2):centroid_uno + int(max_distance * 2)
-      ]
-
-      crop_img = torch.from_numpy(crop_img)
-
-
-      lista_img_recortadas.append(crop_img)
-
-  return lista_img_recortadas
-
-# Load EfficientNetV2 model
-img_shape = (224, 224, 3)
-model_efficientnet = EfficientNetV2B0(include_top=False, input_shape=img_shape)
-flat_1 = GlobalAveragePooling2D()(model_efficientnet.output)
-capa_3 = Dense(1, activation='sigmoid')(flat_1)
-model_efficientnet = Model(inputs=model_efficientnet.inputs, outputs=capa_3)
-model_efficientnet.compile(optimizer=Adam(learning_rate=1e-4), loss="BinaryCrossentropy", metrics=["accuracy"])
-
-# Load weights for EfficientNetV2 model
-repo_id = "ferferefer/PAPILA"
-filename = "EfficientNetV2B0_checkpoint.h5"
-model_file = cached_download(hf_hub_url(repo_id, filename))
-model_efficientnet.load_weights(model_file)
-
-# Streamlit app
-st.title('Glaucoma PAPILA Image Classifier')
-
-
-# Main Streamlit app logic
-uploaded_image = st.file_uploader('Upload image', type=['jpg', 'jpeg', 'png'])
-
-if uploaded_image is not None:
- 
-
-    # Obtain predictions from SegFormer model
-    predictions_papila = obtener_predicciones(model_seg, uploaded_image)
-
-    centroids,imagenes_transformadas = calcular_centro_imagen(predictions_papila)
-    imagen_final_recortada = recortar_imagen(centroids, imagenes_transformadas,uploaded_image)
-    imagen_final_recortada= Image.fromarray(imagen_final_recortada[0].numpy())
-
-    # Display cropped image
-    st.image(imagen_final_recortada[0], use_column_width=True)
-
-    # Button to trigger prediction
-    if st.button('PREDICT'):
-        predict = load_img(imagen_final_recortada[0], target_size=img_shape)
-        predict_modified = img_to_array(predict)
-        predict_modified = np.expand_dims(predict_modified, axis=0)
-        result = model_efficientnet.predict(predict_modified)
-        if result < 0.5:
-            probability = 1 - result[0][0]
-            st.write(f"Healthy with {probability*100:.2f}%")
-        else:
-            probability = result[0][0]
-            st.write(f"Glaucoma with {probability*100:.2f}%")
-        
-        image1 = img_to_array(imagen_final_recortada[0])
-        image1 = np.array(imagen_final_recortada[0])
-        image1 = imagen_final_recortada[0]/255
-    
-    
-
-    st.image(imagen_final_recortada[0], caption='Uploaded Image', use_column_width=True, clamp=True)
-