added requirements

.history/app_20240617181401.py

@@ -0,0 +1,72 @@
+import os
+import gradio as gr
+from PIL import Image
+import torch
+from transformers import ViTForImageClassification, ViTImageProcessor
+from datasets import load_dataset
+
+# Model and processor configuration
+model_name_or_path = "google/vit-base-patch16-224-in21k"
+processor = ViTImageProcessor.from_pretrained(model_name_or_path)
+
+# Load dataset (adjust dataset_path accordingly)
+dataset_path = "pawlo2013/chest_xray"
+train_dataset = load_dataset(dataset_path, split="train")
+class_names = train_dataset.features["label"].names
+
+# Load ViT model
+model = ViTForImageClassification.from_pretrained(
+    "./models",
+    num_labels=len(class_names),
+    id2label={str(i): label for i, label in enumerate(class_names)},
+    label2id={label: i for i, label in enumerate(class_names)},
+)
+
+# Set model to evaluation mode
+model.eval()
+
+
+# Define the classification function
+def classify_image(img_path):
+    img = Image.open(img_path)
+    processed_input = processor(images=img, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model(**processed_input)
+        logits = outputs.logits
+        probabilities = torch.softmax(logits, dim=1)[0].tolist()
+
+    result = {class_name: prob for class_name, prob in zip(class_names, probabilities)}
+    filename = os.path.basename(img_path).split(".")[0]
+    return {"filename": filename, "probabilities": result}
+
+
+def format_output(output):
+    return f"{output['filename']}", output["probabilities"]
+
+
+# Function to load examples from a folder
+def load_examples_from_folder(folder_path):
+    examples = []
+    for file in os.listdir(folder_path):
+        if file.endswith((".png", ".jpg", ".jpeg")):
+            examples.append(os.path.join(folder_path, file))
+    return examples
+
+
+# Define the path to the examples folder
+examples_folder = "./examples"
+examples = load_examples_from_folder(examples_folder)
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=lambda img: format_output(classify_image(img)),
+    inputs=gr.Image(type="filepath"),
+    outputs=[gr.Textbox(label="True Label (from filename)"), gr.Label()],
+    examples=examples,
+    title="Pneumonia X-Ray 3-Class Classification with Vision Transformer (ViT) using data augmentation",
+    description="Upload an X-ray image to classify it as normal, viral or bacterial pneumonia.",
+)
+
+# Launch the app
+if __name__ == "__main__":
+    iface.launch()

.history/app_20240617181448.py

@@ -0,0 +1,72 @@
+import os
+import gradio as gr
+from PIL import Image
+import torch
+from transformers import ViTForImageClassification, ViTImageProcessor
+from datasets import load_dataset
+
+# Model and processor configuration
+model_name_or_path = "google/vit-base-patch16-224-in21k"
+processor = ViTImageProcessor.from_pretrained(model_name_or_path)
+
+# Load dataset (adjust dataset_path accordingly)
+dataset_path = "pawlo2013/chest_xray"
+train_dataset = load_dataset(dataset_path, split="train")
+class_names = train_dataset.features["label"].names
+
+# Load ViT model
+model = ViTForImageClassification.from_pretrained(
+    "./models",
+    num_labels=len(class_names),
+    id2label={str(i): label for i, label in enumerate(class_names)},
+    label2id={label: i for i, label in enumerate(class_names)},
+)
+
+# Set model to evaluation mode
+model.eval()
+
+
+# Define the classification function
+def classify_image(img_path):
+    img = Image.open(img_path)
+    processed_input = processor(images=img, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model(**processed_input)
+        logits = outputs.logits
+        probabilities = torch.softmax(logits, dim=1)[0].tolist()
+
+    result = {class_name: prob for class_name, prob in zip(class_names, probabilities)}
+    filename = os.path.basename(img_path).split(".")[0]
+    return {"filename": filename, "probabilities": result}
+
+
+def format_output(output):
+    return f"{output['filename']}", output["probabilities"]
+
+
+# Function to load examples from a folder
+def load_examples_from_folder(folder_path):
+    examples = []
+    for file in os.listdir(folder_path):
+        if file.endswith((".png", ".jpg", ".jpeg")):
+            examples.append(os.path.join(folder_path, file))
+    return examples
+
+
+# Define the path to the examples folder
+examples_folder = "./examples"
+examples = load_examples_from_folder(examples_folder)
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=lambda img: format_output(classify_image(img)),
+    inputs=gr.Image(type="filepath"),
+    outputs=[gr.Textbox(label="True Label (from filename)"), gr.Label()],
+    examples=examples,
+    title="Pneumonia X-Ray 3-Class Classification with Vision Transformer (ViT) using data augmentation",
+    description="Upload an X-ray image to classify it as normal, viral or bacterial pneumonia. Checkout the model in more details at https://huggingface.co/pawlo2013/vit-pneumonia-x-ray_3_class",
+)
+
+# Launch the app
+if __name__ == "__main__":
+    iface.launch()

.history/requirements_20240617181322.txt

@@ -0,0 +1 @@
+torch

.history/requirements_20240617181330.txt

@@ -0,0 +1,2 @@
+torch
+transformers

.history/requirements_20240617181331.txt

@@ -0,0 +1,2 @@
+torch
+transformers

.history/requirements_20240617181334.txt

@@ -0,0 +1,3 @@
+torch
+transformers
+datasets

app.py

@@ -63,8 +63,8 @@ iface = gr.Interface(
     inputs=gr.Image(type="filepath"),
     outputs=[gr.Textbox(label="True Label (from filename)"), gr.Label()],
     examples=examples,
-    title="Pneumonia X-Ray 3-Class Classification with Vision Transformer (ViT)",
-    description="Upload an X-ray image to classify it as normal, viral or bacterial pneumonia.",
+    title="Pneumonia X-Ray 3-Class Classification with Vision Transformer (ViT) using data augmentation",
+    description="Upload an X-ray image to classify it as normal, viral or bacterial pneumonia. Checkout the model in more details at https://huggingface.co/pawlo2013/vit-pneumonia-x-ray_3_class",
 )
 
 # Launch the app

requirements.txt

@@ -0,0 +1,3 @@
+torch
+transformers
+datasets