File size: 6,863 Bytes
d765024 3872cf7 d765024 3872cf7 d765024 3872cf7 d765024 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 |
import gradio as gr
import cv2
import numpy as np
import requests
import g4f
import time
import os
theme = gr.themes.Base(
primary_hue="cyan",
secondary_hue="blue",
neutral_hue="slate",
)
API_KEY = os.environ.get("API_KEY")
BRAIN_TUMOR_API_URL = "https://api-inference.huggingface.co/models/Devarshi/Brain_Tumor_Classification"
BREAST_CANCER_API_URL = "https://api-inference.huggingface.co/models/MUmairAB/Breast_Cancer_Detector"
ALZHEIMER_API_URL = "https://api-inference.huggingface.co/models/AhmadHakami/alzheimer-image-classification-google-vit-base-patch16"
headers = {"Authorization": "Bearer "+ API_KEY+"", 'Content-Type': 'application/json'}
# Create a function to Detect/Classify Alzheimer
def classify_alzheimer(image):
image_data = np.array(image, dtype=np.uint8)
_, buffer = cv2.imencode('.jpg', image_data)
binary_data = buffer.tobytes()
response = requests.post(ALZHEIMER_API_URL, headers=headers, data=binary_data)
result = {item['label']: item['score'] for item in response.json()}
return result
# Create a function to Detect/Classify Breast Cancer
def classify_breast_cancer(image):
image_data = np.array(image, dtype=np.uint8)
_, buffer = cv2.imencode('.jpg', image_data)
binary_data = buffer.tobytes()
response = requests.post(BREAST_CANCER_API_URL, headers=headers, data=binary_data)
result = {item['label']: item['score'] for item in response.json()}
return result
# Create a function to Detect/Classify Brain Tumor
def classify_brain_tumor(image):
image_data = np.array(image, dtype=np.uint8)
_, buffer = cv2.imencode('.jpg', image_data)
binary_data = buffer.tobytes()
response = requests.post(BRAIN_TUMOR_API_URL, headers=headers, data=binary_data)
result = {item['label']: item['score'] for item in response.json()}
return result
# Create the Gradio interface
with gr.Blocks(theme=theme) as Alzheimer:
with gr.Row():
with gr.Column():
gr.Markdown("# Alzheimer Detection and Classification")
gr.Markdown("> Classify the alzheimer into Mild Demented, Very Mild Demented, Moderate Demented and Non Demented.")
image = gr.Image()
output = gr.Label(label='Alzheimer Classification', container=True, scale=2)
with gr.Row():
button = gr.Button(value="Submit", variant="primary")
gr.ClearButton([image, output])
button.click(classify_alzheimer, [image], [output])
def respond(message, history):
bot_message = g4f.ChatCompletion.create(
model="gpt-3.5-turbo",
provider=g4f.Provider.You,
messages=[{"role": "user",
"content": "Your role is Alzheimer Disease Expert. Now I will provide you with the user query. First check if the user query is related to Alzheimer or not. If it is not related to Alzheimer then do not reply the query whereas if related to Alzheimer reply it as usual. Here's the user Query:" + message}],
)
time.sleep(1)
yield str(bot_message)
with gr.Column():
gr.Markdown("# Health Bot for Alzheimer")
gr.Markdown("> **Note:** The information may not be accurate. Please consult a Doctor before considering any actions.")
gr.ChatInterface(respond, autofocus=False).queue()
with gr.Blocks(theme=theme) as BreastCancer:
with gr.Row():
with gr.Column():
gr.Markdown("# Breast Cancer Detection and Classification")
gr.Markdown("> Classify the breast cancer.")
image = gr.Image()
output = gr.Label(label='Breast Cancer Classification', container=True, scale=2)
with gr.Row():
button = gr.Button(value="Classify")
gr.ClearButton([image, output])
button.click(classify_breast_cancer, [image], [output])
def respond(message, history):
bot_message = g4f.ChatCompletion.create(
model="gpt-3.5-turbo",
provider=g4f.Provider.You,
messages=[{"role": "user",
"content": "Your role is Breast Cancer Disease Expert. Now I will provide you with the user query. First check if the user query is related to Breast Cancer or not. If it is not related to Breast Cancer then do not reply the query whereas if related to Breast Cancer reply it as usual. Here's the user Query:" + message}],
)
time.sleep(1)
yield str(bot_message)
with gr.Column():
gr.Markdown("# Health Bot for Breast Cancer")
gr.Markdown("> **Note:** The information may not be accurate. Please consult a Doctor before considering any actions.")
gr.ChatInterface(respond, autofocus=False).queue()
with gr.Blocks(theme=theme) as BrainTumor:
with gr.Row():
with gr.Column():
gr.Markdown("# Brain Tumor Detection and Classification")
gr.Markdown("> Classify the Brain Tumor.")
image = gr.Image()
output = gr.Label(label='Brain Tumor Classification', container=True, scale=2)
with gr.Row():
button = gr.Button(value="Classify")
gr.ClearButton([image, output])
button.click(classify_brain_tumor, [image], [output])
def respond(message, history):
bot_message = g4f.ChatCompletion.create(
model="gpt-3.5-turbo",
provider=g4f.Provider.You,
messages=[{"role": "user",
"content": "Your role is Brain Tumor Disease Expert. Now I will provide you with the user query. First check if the user query is related to Brain Tumor or not. If it is not related to Brain Tumor then do not reply the query whereas if related to Brain Tumor reply it as usual. Here's the user Query:" + message}],
)
time.sleep(1)
yield str(bot_message)
with gr.Column():
gr.Markdown("# Health Bot for Brain Tumor")
gr.Markdown("> **Note:** The information may not be accurate. Please consult a Doctor before considering any actions.")
gr.ChatInterface(respond, autofocus=False, examples=["Explain Brain Tumor."]).queue()
Main = gr.TabbedInterface([Alzheimer, BreastCancer, BrainTumor], ["Alzheimer", "Breast Cancer", "Brain Tumor"],
theme=theme,
css=".gradio-container { background: rgba(255, 255, 255, 0.2) !important; box-shadow: 0 8px 32px 0 rgba( 31, 38, 135, 0.37 ) !important !important; backdrop-filter: blur( 10px ) !important; -webkit-backdrop-filter: blur( 10px ) !important; border-radius: 10px !important; border: 1px solid rgba( 255, 255, 255, 0.18 ) !important;}")
Main.launch()
|