Update app.py

app.py

@@ -5,56 +5,15 @@ from tensorflow.keras.preprocessing import image
 import numpy as np
 import cv2
 import PIL.Image
-import os
-from dotenv import load_dotenv
 import traceback
-import base64
 from io import BytesIO
 
-# Load environment variables
-load_dotenv()
-
-# Remove Google Generative AI imports and configuration
-# We will use a local model for generating explanations
-
-# Import the transformers library for text generation
-from transformers import pipeline
-
-# Initialize the text generation pipeline (using a smaller model for demonstration)
-# Replace 'google/flan-t5-base' with 'meta-llama/Llama-2-7b-chat-hf' if you have the resources
-generator = pipeline('text2text-generation', model='google/flan-t5-base')
-
-def generate_explanation(saliency_map_image, model_prediction, confidence):
-    # Convert the saliency map image array to a base64-encoded string
-    buffered = BytesIO()
-    img = PIL.Image.fromarray(saliency_map_image)
-    img.save(buffered, format="PNG")
-    img_str = base64.b64encode(buffered.getvalue()).decode('utf-8')
-
-    # Prepare the prompt (Note: models like FLAN-T5 cannot process images directly)
-    prompt = f"""You are an expert neurologist. You are tasked with explaining a saliency map of a brain tumor MRI scan.
-The saliency map was generated by a deep learning model that was trained to classify brain tumors as either
-glioma, meningioma, pituitary, or no tumor.
-
-The saliency map highlights the regions of the image that the machine learning model is focusing on to make the predictions.
-
-The deep learning model predicted the image to be of class '{model_prediction}' with a confidence of {confidence * 100:.2f}%.
-
-
-In your response:
-- Explain what regions of the brain the model is focusing on, based on the saliency map.
-- Explain possible reasons why the model made the prediction it did.
-- Do not mention phrases like 'The saliency map highlights the regions the model is focusing on, which are in light cyan.'
-- Keep your explanation to 5 sentences max.
-
-Your response will go directly in the report to the doctor and patient, so do not add extra phrases like 'Sure!' or ask any questions at the end.
-Let's think step by step about this.
-"""
-
-    # Generate the explanation using the text generation pipeline
-    response = generator(prompt, max_length=500)
-    explanation = response[0]['generated_text']
-
+# Function to generate a simple explanation based on the saliency map and prediction
+def generate_explanation(model_prediction, confidence):
+    explanation = (
+        f"The model predicts the tumor type is '{model_prediction}' with a confidence of {confidence * 100:.2f}%. "
+        "This prediction is based on the highlighted regions of the MRI scan which contributed most to the decision."
+    )
     return explanation
 
 def generate_saliency_map(model, img_array, class_index, img_size):
@@ -150,10 +109,9 @@ def classify_brain_tumor(image_file, model_choice):
 
         # Generate the saliency map
         saliency_map = generate_saliency_map(model, img_array, class_index, img_size)
-        # No longer saving the saliency map to disk
 
         # Generate the explanation
-        explanation = generate_explanation(saliency_map, result, confidence)
+        explanation = generate_explanation(result, confidence)
 
         # Prepare probabilities for all classes
         probabilities = prediction[0]