Update app.py

app.py

@@ -1,49 +1,216 @@
-from flask import Flask, render_template, request
-import tensorflow as tf
-import numpy as np
-from keras.models import load_model
-from tensorflow.keras.preprocessing.image import load_img, img_to_array
-from io import BytesIO
-import base64
-
-app = Flask(__name__)
-
-# Load the model
-incept_model = load_model('best_model_2.h5')
-IMAGE_SHAPE = (224, 224)
-classes = ['benign', 'malignant', 'normal']
-
-# Function to prepare the image
-def prepare_image(file):
-    img = load_img(BytesIO(file.read()), 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)
-
-@app.route('/')
-def home():
-    return render_template('index.html')
-
-@app.route('/predict', methods=['POST'])
-def predict():
-    if 'file' not in request.files:
-        return redirect(request.url)
-    file = request.files['file']
-    if file.filename == '':
-        return redirect(request.url)
-
-    # Prepare the image for prediction
-    img = prepare_image(file)
-    res = incept_model.predict(img)
-    pred = classes[np.argmax(res)]
-
-    # Encode image to display in the result page
-    file.seek(0)  # Reset file pointer to the beginning
-    img_bytes = file.read()
-    img_base64 = base64.b64encode(img_bytes).decode('utf-8')
-    img_data = f"data:image/jpeg;base64,{img_base64}"
-
-    return render_template('result.html', prediction=pred, image_data=img_data)
-
-if __name__ == '__main__':
-    app.run(debug=True)
+!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('/content/sample_data/breast cancer/best_model_2.h5')
+
+fixed_image_url = "/content/sample_data/breast cancer/static/breast-cancer-awareness-month-1200x834.jpg"
+
+
+
+# Example images and their descriptions
+
+examples = [
+
+    ["/content/sample_data/breast cancer/malignant.png", "Malignant X-ray image."],
+
+    ["/content/sample_data/breast cancer/normal.png", "X-ray image indicating normal."],
+
+    ["/content/sample_data/breast cancer/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)