application file

app.py

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+import gradio as gr
+import numpy as np
+import cv2
+import matplotlib.pyplot as plt
+
+import tensorflow as tf
+import tensorflow.keras.backend as K
+from keras.preprocessing import image
+
+from ResUNet import *
+
+from eff import *
+from vit import *
+
+# Define the image transformation
+transform = transforms.Compose([
+    transforms.ToTensor(),
+    transforms.Resize((224, 224)),
+])
+
+examples1 = [
+    f"examples/Eff_ViT/Classification_{i}.jpg" for i in range(0, 4)
+]
+
+def classification(image):
+    input_tensor = transform(image).unsqueeze(0).to(CFG.DEVICE)  # Add batch dimension
+
+    input_batch = input_tensor
+
+    # Perform inference
+    with torch.no_grad():
+        output1 = efficientnet_model(input_batch).to(CFG.DEVICE)
+        output2 = efficientnet_model(input_batch).to(CFG.DEVICE)
+        output3 = vit_model(input_batch).to(CFG.DEVICE)
+
+    # You can now use the 'output' tensor as needed (e.g., get predictions)
+    # print(output)
+    res1 = torch.softmax(output1, dim=1)
+    res2 = torch.softmax(output2, dim=1)
+    res3 = torch.softmax(output3, dim=1)
+
+    probs1 = {class_names[i]: float(res1[0][i]) for i in range(len(class_names))}
+    probs2 = {class_names[i]: float(res2[0][i]) for i in range(len(class_names))}
+    probs3 = {class_names[i]: float(res3[0][i]) for i in range(len(class_names))}
+
+    return probs1, probs2, probs3
+    
+
+classify = gr.Interface(
+    fn=classification,
+    inputs=[
+        gr.Image(label="Image"),
+        # gr.Radio(["EfficientNetB3", "EfficientNetV2", "ViT"], value="ViT")
+    ],
+    outputs=[
+        gr.Label(num_top_classes = 3, label = "EfficientNet-B3"),
+        gr.Label(num_top_classes = 3, label = "EfficientNet-V2"),
+        gr.Label(num_top_classes = 3, label = "ViT"),
+    ],
+    examples=examples1,
+    cache_examples=True
+)
+
+# ---------------------------------------------------------
+
+seg_model = load_model()
+seg_model.load_weights("ResUNet-segModel-weights.hdf5")
+
+
+examples2 = [
+    f"examples/ResUNet/{i}.jpg" for i in range(5)
+]
+
+def detection(img):
+  org_img = img
+
+  img = img *1./255.
+
+  #reshaping
+  img = cv2.resize(img, (256,256))
+
+  # converting img into array
+  img = np.array(img, dtype=np.float64)
+
+  #reshaping the image from 256,256,3 to 1,256,256,3
+  img = np.reshape(img, (1,256,256,3))
+
+
+  #Creating a empty array of shape 1,256,256,1
+  X = np.empty((1,256,256,3))
+
+  # standardising the image
+  img -= img.mean()
+  img /= img.std()
+
+  #converting the shape of image from 256,256,3 to 1,256,256,3
+  X[0,] = img
+
+  #make prediction of mask
+  predict = seg_model.predict(X)
+
+
+  pred = np.array(predict[0]).squeeze().round()
+
+
+  img_ = cv2.resize(org_img, (256,256))
+  img_ = cv2.cvtColor(img_, cv2.COLOR_BGR2RGB)
+  img_[pred==1] = (0,255,150)
+
+  plt.imshow(img_)
+  plt.axis("off")
+  image_path = "plot.png"
+  plt.savefig(image_path)
+
+  return gr.update(value=image_path, visible=True)
+
+
+detect = gr.Interface(
+    fn=detection,
+    inputs=[
+        gr.Image(label="Image")
+    ],
+    outputs=[
+        gr.Image(label="Output")
+    ],
+    examples=examples2,
+    cache_examples=True
+)
+
+# ##########################################
+
+# def data_viewer(label="Pituitary", count=10):
+#   results = []
+
+#   if(label == "Segmentation"):
+#     for i in range((count//2)+1):
+#       results.append(f"Images/{label}/original_image_{i}.png")
+#       results.append(f"Images/{label}/image_with_mask_{i}.png")
+
+#   else:
+
+#     for i in range(count):
+#       results.append(f"Images/{label}/{i}.jpg")
+
+#   return results
+
+
+# view_data = gr.Interface(
+#     fn = data_viewer,
+#     inputs = [
+#         gr.Dropdown(
+#             ["Glioma", "Meningioma", "Pituitary", "Segmentation"], label="Category"
+#         ),
+#         gr.Slider(0, 12, value=4, step=2)
+#     ],
+#     outputs = [
+#         gr.Gallery(columns=2),
+#     ]
+# )
+
+# ##########################
+
+from huggingface_hub import InferenceClient
+
+client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")
+
+def format_prompt(message, history):
+  prompt = "<s>"
+  for user_prompt, bot_response in history:
+    prompt += f"[INST] {user_prompt} [/INST]"
+    prompt += f" {bot_response}</s> "
+  prompt += f"[INST] {message} [/INST]"
+  return prompt
+
+def generate(
+    prompt, history, temperature=0.2, max_new_tokens=1024, top_p=0.95, repetition_penalty=1.0,
+):
+    temperature = float(temperature)
+    if temperature < 1e-2:
+        temperature = 1e-2
+    top_p = float(top_p)
+
+    generate_kwargs = dict(
+        temperature=temperature,
+        max_new_tokens=max_new_tokens,
+        top_p=top_p,
+        repetition_penalty=repetition_penalty,
+        do_sample=True,
+        seed=42,
+    )
+
+    formatted_prompt = format_prompt(prompt, history)
+
+    stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
+    output = ""
+
+    for response in stream:
+        output += response.token.text
+        yield output
+    return output
+
+    
+mychatbot = gr.Chatbot(
+    avatar_images=["Chatbot/user.png", "Chatbot/botm.png"], bubble_full_width=False, show_label=False, show_copy_button=True, likeable=True,)
+
+chatbot = gr.ChatInterface(
+    fn=generate, 
+    chatbot=mychatbot,
+    examples=[
+        "What is Brain Tumor and its types?",
+        "What is a tumor's grade? What does this mean?",
+        "What are some of the treatment options for Brain Tumor?",
+        "What causes brain tumors?",
+        "If I have a brain tumor, can I pass it on to my children?"
+    ],
+)
+
+
+demo = gr.TabbedInterface([classify, detect, chatbot], ["Classification", "Detection", "ChatBot"])
+
+demo.launch()