app.py

!curl https://ollama.ai/install.sh | sh                                                                            
import os

import subprocess

import time

import gradio as gr

import numpy as np

import tensorflow as tf

from tensorflow.keras.preprocessing.image import load_img, img_to_array

import h5py                                                                                                                              
# Set the environment variable for the Ollama model

OLLAMA_MODEL = 'dolphin-mistral:v2.8'

os.environ['OLLAMA_MODEL'] = OLLAMA_MODEL



# Start Ollama as a background process

command = "nohup ollama serve &"

process = subprocess.Popen(command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)

time.sleep(5)  # Wait for the server to start

 # Function to interact with the Ollama model

def query_model(input_text):

    response = subprocess.run(

        ["ollama", "run", OLLAMA_MODEL, input_text],

        stdout=subprocess.PIPE,

        stderr=subprocess.PIPE,

        text=True

    )

    return response.stdout if response.returncode == 0 else response.stderr                           
# Load the breast cancer detection model

incept_model = tf.keras.models.load_model('best_model_2.h5')

fixed_image_url = "breast-cancer-awareness-month-1200x834.jpg"



# Example images and their descriptions

examples = [

    ["malignant.png", "Malignant X-ray image."],

    ["normal.png", "X-ray image indicating normal."],

    ["benign.png", "X-ray image showing no signs of benign."]

]

IMAGE_SHAPE = (224, 224)

classes = ['benign', 'malignant', 'normal']      
# Function to prepare the image for prediction

def prepare_image(file):

    img = load_img(file, target_size=IMAGE_SHAPE)

    img_array = img_to_array(img)

    img_array = np.expand_dims(img_array, axis=0)

    return tf.keras.applications.efficientnet.preprocess_input(img_array)



# Prediction function for breast cancer detection

def predict(file):

    if file is None:

        return "Please upload an image.", fixed_image_url



    img = prepare_image(file)

    res = incept_model.predict(img)

    pred_index = np.argmax(res)

    pred = classes[pred_index]



    # Specific advice for each prediction

    if pred == 'malignant':

        advice = "As a healthcare professional, I recommend immediate further evaluation. Malignant findings can indicate the presence of cancer. Please consult a specialist."

    elif pred == 'benign':

        advice = "The results show benign characteristics, which is a positive outcome. This means there are no cancerous cells. However, it’s essential to have regular follow-ups with your healthcare provider to ensure that there are no changes over time."

    else:  # pred == 'normal'

        advice = "The results appear normal. Continue with regular check-ups and maintain a healthy lifestyle."



    return advice, fixed_image_url                                                                                              
# Function to provide project information

def show_info():

    return (

        "<h3 style='text-align: center;'> 🎗️ Welcome to Our Breast Cancer  System 🎗️ </h3>\n\n"

        "Breast cancer is one of the most common causes of death among women worldwide.\n\n "

        "Early detection plays a crucial role in reducing mortality rates.\n\n "

        "This project includes two main components:\n\n"

        "- **Ultrasound Image Classification**: \n\n We classify breast ultrasound images into three categories: normal, benign, and malignant. \n\n"

        "The dataset consists of 780 ultrasound images collected in 2018 from 600 female patients, aged 25 to 75. \n\n "

        "  Each image is in PNG format with an average size of 500x500 pixels.\n\n\n"

        "- **Breast Cancer Information Chatbot**: \n\n Our chatbot is designed to provide reliable information and answer questions about breast cancer, helping users to understand the disease better.\n\n\n"

        "For additional assistance, you can interact with our chatbot or upload images for classification."

    )



# Create the Gradio interface for both functionalities

chatbot_interface = gr.Interface(

    fn=query_model,

    inputs=gr.Textbox(label="Enter your question about breast cancer:", placeholder="e.g., What are the symptoms of breast cancer?", lines=2),

    outputs=gr.Textbox(label="Response:", placeholder="Your answer will appear here..."),

    title="Breast Cancer Chatbot 🎗️",

    description="Ask your questions related to breast cancer. Our chatbot provides information and guidance based on your inquiries.",

)



breast_cancer_interface = gr.Interface(

    fn=predict,

    inputs=gr.Image(type="filepath", label="Upload an Image"),

    outputs=[

        gr.Textbox(label="Prediction"),

        gr.Image(label="Your Partner in Breast Health Awareness 🎗️", value=fixed_image_url)

    ],

    title="Breast Cancer Detection",

    description="Predicting Your Breast Health: Is it Benign, Malignant, or Normal?",

    examples=examples,

)



# Create the information display as a separate Markdown element

info_markdown = gr.Markdown(show_info())



# Combine interfaces into a themed Blocks app

with gr.Blocks(theme=gr.themes.Default(primary_hue=gr.themes.colors.pink, secondary_hue=gr.themes.colors.neutral)) as demo:

    combined_interface = gr.TabbedInterface(

        [info_markdown,chatbot_interface, breast_cancer_interface],

        ["Project Information","Breast Cancer Chatbot", "Breast Cancer Detection"]

    )



# Launch the combined interface

if __name__ == "__main__":

    demo.launch(debug=True)