Update core/utils.py

core/utils.py

@@ -1,58 +1,71 @@
-# app/core/utils.py
-import numpy as np
-import tensorflow as tf
-import io
-from PIL import Image
-from .config import GEMINI_MODEL
-
-
-def preprocess_image_tf(image_bytes):
-    img = Image.open(io.BytesIO(image_bytes)).convert("RGB")
-    img = img.resize((224, 224))
-    img_array = tf.keras.preprocessing.image.img_to_array(img) # type: ignore
-    return np.expand_dims(img_array / 255.0, axis=0)
-
-
-def preprocess_image_pil(image_bytes):
-    img = Image.open(io.BytesIO(image_bytes)).convert("RGB")
-    return img
-
-
-async def generate_gemini_insights(label, confidences, mode="general"):
-    confidence = confidences[label] * 100
-
-    if mode == "tumor":
-        prompt = f"""
-        You are a medical assistant. Generate a tumor analysis report for diagnosis **{label}** with confidence {confidence:.1f}%.
-        Include:
-        - Description
-        - Common MRI findings
-        - Recommended medical steps
-        - Lifestyle/care suggestions
-        Avoid markdown or HTML.
-        """
-    elif mode == "alzheimers":
-        prompt = f"""
-        Generate Alzheimer’s medical report for type '{label}' with {confidence:.1f}% confidence.
-        Explain:
-        - Symptoms
-        - MRI observations
-        - Next steps
-        - Care strategies
-        Format as plain text only.
-        """
-    elif mode == "pneumonia":
-        prompt = f"""
-        Explain in layman terms the medical condition '{label}' with {confidence:.1f}% confidence.
-        Focus on:
-        - What it is
-        - Symptoms
-        - What the patient should do
-        Format in markdown.
-        """
-    else:
-        prompt = f"Explain medical condition '{label}' with {confidence:.1f}% confidence."
-
-    response = await GEMINI_MODEL.generate_content_async(prompt)
-    return response.text
-
+# app/core/utils.py
+import numpy as np
+import tensorflow as tf
+import io
+from PIL import Image
+from .config import GEMINI_MODEL
+
+
+def preprocess_image_tf(image_bytes):
+    img = Image.open(io.BytesIO(image_bytes)).convert("RGB")
+    img = img.resize((224, 224))
+    img_array = tf.keras.preprocessing.image.img_to_array(img) # type: ignore
+    return np.expand_dims(img_array / 255.0, axis=0)
+
+
+def preprocess_image_pil(image_bytes):
+    img = Image.open(io.BytesIO(image_bytes)).convert("RGB")
+    return img
+
+
+async def generate_gemini_insights(label, confidences, mode="general"):
+    confidence = confidences[label] * 100
+
+    if mode == "tumor":
+        prompt = f"""
+        You are a medical assistant. Generate a tumor analysis report for diagnosis **{label}** with confidence {confidence:.1f}%.
+        Include:
+        - Description
+        - Common MRI findings
+        - Recommended medical steps
+        - Lifestyle/care suggestions
+        Avoid markdown or HTML.
+        """
+    elif mode == "alzheimers":
+        prompt = f"""
+        Generate Alzheimer’s medical report for type '{label}' with {confidence:.1f}% confidence.
+        Explain:
+        - Symptoms
+        - MRI observations
+        - Next steps
+        - Care strategies
+        
+        Format the output strictly using Markdown. Output only the following sections and nothing else. No introduction, no headings, no extra markdown syntax beyond what is shown below. Use this exact format:
+        
+        ## Symptoms
+        [description]
+        
+        ## MRI observations
+        [description]
+        
+        ## Next steps
+        [description]
+        
+        ## Care strategies
+        [description]
+        """
+    elif mode == "pneumonia":
+        prompt = f"""
+        Explain in layman terms the medical condition '{label}' with {confidence:.1f}% confidence.
+        Focus on:
+        - What it is
+        - Symptoms
+        - What the patient should do
+        Format in markdown.
+        """
+    else:
+        prompt = f"Explain medical condition '{label}' with {confidence:.1f}% confidence."
+
+    response = await GEMINI_MODEL.generate_content_async(prompt)
+    return response.text
+