Upload 3 files

app.py

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+import streamlit as st
+import os
+from dotenv import load_dotenv
+import tensorflow as tf
+from tensorflow.keras.preprocessing import image
+import numpy as np
+import tempfile
+from reportlab.pdfgen import canvas
+from vertexai.language_models import ChatModel
+import vertexai
+
+# Load environment variables from .env
+load_dotenv()
+
+# Set Google Cloud credentials from JSON key file
+json_key_file = "gen-lang-client-0651086807-7a7c02723fa8.json"  # Replace with your actual JSON key file name
+
+# If the JSON key file is in the current working directory, set its path
+os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = json_key_file
+
+# Initialize Vertex AI
+project = "gen-lang-client-0651086807"  # Replace with your Vertex AI project ID
+location = "us-central1"  # Replace with your Vertex AI location
+vertexai.init(project=project, location=location)
+
+# Load the Vertex AI ChatModel
+chat_model = ChatModel.from_pretrained("chat-bison@002")
+
+# Class labels for brain tumor types
+class_labels = ['glioma', 'meningioma', 'notumor', 'pituitary']
+
+# Function to preprocess image for the model
+def preprocess_image(img):
+    img = image.load_img(img, target_size=(150, 150))
+    img_array = image.img_to_array(img)
+    img_array = np.expand_dims(img_array, axis=0)
+    img_array /= 255.0  # Normalize to [0, 1]
+    return img_array
+
+# Function to load the CNN model
+@st.cache(allow_output_mutation=True)
+def load_model():
+    model_path = 'brain_tumor_classifier.h5'  # Replace with your actual model path
+    model = tf.keras.models.load_model(model_path)
+    return model
+
+# Function to predict brain tumor type and generate medical advice
+def predict_and_generate_text(uploaded_file):
+    try:
+        if uploaded_file is None:
+            raise ValueError("No image file uploaded.")
+
+        # Load the model
+        model = load_model()
+
+        # Preprocess uploaded image
+        processed_img = preprocess_image(uploaded_file)
+
+        # Predict using the model
+        prediction = model.predict(processed_img)
+        predicted_class_index = np.argmax(prediction)
+        predicted_class = class_labels[predicted_class_index]
+
+        # Debugging: Print prediction details
+        st.write(f"Predicted class: {predicted_class}")
+        st.write(f"Prediction probabilities: {prediction}")
+
+        # Generate medical advice only for specific tumor classes
+        if predicted_class in ['glioma', 'meningioma', 'pituitary']:
+            # Initialize Vertex AI ChatModel
+            chat_session = chat_model.start_chat(context="")
+
+            # Constructing the prompt based on the predicted class
+            prompt = f"The patient has been diagnosed with a {predicted_class} brain tumor. Provide detailed medical advice including:"
+
+            if predicted_class == 'glioma':
+                prompt += (
+                    " 1. Medical Condition: You have a glioma located in the frontal lobe.\n"
+                    " 2. Medication Recommendations: Suggest suitable medications including chemotherapy drugs like Temozolomide, steroids to reduce brain swelling.\n"
+                    " 3. Treatment Guidance: Recommend necessary treatments such as surgery to remove the tumor, followed by radiation therapy.\n"
+                    " 4. Lifestyle Changes: Suggest lifestyle modifications to manage symptoms or improve overall health, such as regular exercise and a balanced diet.\n"
+                    " 5. Monitoring and Specialist Referrals: Recommend ongoing monitoring by neurologists and oncologists for further evaluation and treatment."
+                )
+
+            elif predicted_class == 'meningioma':
+                prompt += (
+                    " 1. Medical Condition: You have a meningioma, typically located in the meninges surrounding the brain.\n"
+                    " 2. Medication Recommendations: Suggest medications to control symptoms, possibly including anticonvulsants and corticosteroids.\n"
+                    " 3. Treatment Guidance: Recommend surgical removal of the tumor, with radiation therapy considered if complete removal isn't feasible.\n"
+                    " 4. Lifestyle Changes: Advise on lifestyle adjustments such as stress reduction techniques and adequate sleep.\n"
+                    " 5. Monitoring and Specialist Referrals: Recommend regular follow-up with neurosurgeons and neurologists for monitoring and management."
+                )
+
+            elif predicted_class == 'pituitary':
+                prompt += (
+                    " 1. Medical Condition: You have a pituitary tumor affecting the pituitary gland.\n"
+                    " 2. Medication Recommendations: Suggest hormone replacement therapy to manage hormone imbalances caused by the tumor.\n"
+                    " 3. Treatment Guidance: Recommend surgical intervention to remove the tumor, or radiation therapy depending on the tumor's size and location.\n"
+                    " 4. Lifestyle Changes: Recommend dietary adjustments and regular physical activity to support overall health.\n"
+                    " 5. Monitoring and Specialist Referrals: Refer to endocrinologists and neurosurgeons for specialized care and ongoing monitoring."
+                )
+
+            # Additional prompts for all tumor types
+            prompt += (
+                " 6. Emotional Support: Emphasize the importance of emotional support and counseling throughout the treatment process.\n"
+                " 7. Patient Education: Educate the patient about the tumor type, its potential complications, and treatment options.\n"
+                " 8. Family Involvement: Encourage involvement of family members in decision-making and support.\n"
+                " 9. Research and Clinical Trials: Discuss opportunities for participation in clinical trials for new treatments.\n"
+                "10. Rehabilitation: Outline potential rehabilitation programs post-treatment to aid recovery and improve quality of life."
+            )
+
+            response = chat_session.send_message(prompt)
+            return response.text
+        else:
+            return "No specific medical advice available for tumors classified as 'notumor'."
+
+    except ValueError as ve:
+        st.error(f"Error: {ve}")
+    except Exception as e:
+        st.error(f"An error occurred during prediction: {e}")
+
+# Function to generate PDF report
+def generate_pdf(description):
+    if description is None:
+        return None
+
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as tmpfile:
+        c = canvas.Canvas(tmpfile.name)
+        c.setFont("Helvetica", 12)
+        text_lines = description.split('\n')  # Ensure description is not None before splitting
+        y = 750
+        for line in text_lines:
+            c.drawString(100, y, line)
+            y -= 20
+        c.save()
+        tmpfile.close()
+        return tmpfile.name
+
+# Streamlit app
+def main():
+    st.title('Brain Tumor Classifier and Treatment Advisor')
+    st.markdown('Upload a brain MRI image for analysis.')
+
+    uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "png", "jpeg"])  # Allow jpg, png, and jpeg files
+    if uploaded_file is not None:
+        st.image(uploaded_file, caption='Uploaded MRI Image', use_column_width=True)
+        if st.button('Predict and Recommend'):
+            try:
+                with st.spinner('Analyzing the image...'):
+                    output_text = predict_and_generate_text(uploaded_file)
+                st.success("Medical Advice:")
+                st.write(output_text)
+
+                # Generate and download PDF report
+                pdf_filename = generate_pdf(output_text)
+                with open(pdf_filename, "rb") as pdf_file:
+                    st.download_button(label="Download PDF", data=pdf_file, file_name="medical_advice.pdf", mime="application/pdf")
+
+            except Exception as e:
+                st.error(f"An error occurred: {e}")
+
+if __name__ == "__main__":
+    main()

brain_tumor_classifier.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:28ab14dd82d2d60586378868305ed17a358388a0426219d6c620e7d3f6b3d350
+size 72870296

gen-lang-client-0651086807-7a7c02723fa8.json

@@ -0,0 +1,13 @@
+{
+  "type": "service_account",
+  "project_id": "gen-lang-client-0651086807",
+  "private_key_id": "7a7c02723fa878dce92e58ce097bf5ed7c82bb21",
+  "private_key": "-----BEGIN PRIVATE KEY-----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\nTL9YYij4O4mu3PRyHUGe+xjA\n-----END PRIVATE KEY-----\n",
+  "client_email": "sadik-27@gen-lang-client-0651086807.iam.gserviceaccount.com",
+  "client_id": "100824630135105586057",
+  "auth_uri": "https://accounts.google.com/o/oauth2/auth",
+  "token_uri": "https://oauth2.googleapis.com/token",
+  "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
+  "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/sadik-27%40gen-lang-client-0651086807.iam.gserviceaccount.com",
+  "universe_domain": "googleapis.com"
+}