Update app.py

app.py

@@ -1,40 +1,73 @@
-import gradio as gr
-import tensorflow as tf
-import numpy as np
-from PIL import Image
-
-# ---------- Load your trained model ----------
-model = tf.keras.models.load_model("final_model.h5")
-
-# ---------- Prediction function ----------
-def predict(image):
-    # Resize the input image to the size your model expects (update if different)
-    img = image.resize((224, 224))  # change size if your model was trained differently
-    img_array = np.array(img) / 255.0  # normalize
-    
-    # Expand dimensions for batch
-    img_array = np.expand_dims(img_array, axis=0)
-    
-    # Predict
-    prediction = model.predict(img_array)[0][0]  # assumes binary classification
-    
-    # Convert probability to label
-    if prediction > 0.5:
-        result = f"🟥 Malignant (Cancer Detected) with {prediction*100:.2f}% confidence"
-    else:
-        result = f"🟩 Benign (No Cancer) with {(1-prediction)*100:.2f}% confidence"
-    
-    return result
-
-# ---------- Define Gradio UI ----------
-demo = gr.Interface(
-    fn=predict,
-    inputs=gr.Image(type="pil", label="Upload Skin Lesion Image"),
-    outputs=gr.Textbox(label="Prediction"),
-    title="🧬 Skin Cancer Detection",
-    description="Upload a skin lesion image and the model will predict whether it is Benign or Malignant."
-)
-
-# ---------- Launch ----------
-if __name__ == "__main__":
-    demo.launch()
+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+import os
+import google.generativeai as genai  # ✅ Gemini API
+
+# ---------------- Load Model ----------------
+MODEL_PATH = "final_model.h5"
+if not os.path.exists(MODEL_PATH):
+    raise FileNotFoundError(
+        f"{MODEL_PATH} not found. Please upload your trained model (final_model.h5)."
+    )
+
+# Load Keras model
+model = tf.keras.models.load_model(MODEL_PATH)
+
+# ---------------- Gemini API ----------------
+# 1. Get a free API key: https://aistudio.google.com/app/apikey
+# 2. On Hugging Face, store your key as a secret:
+#    Settings → Repository secrets → Add "GEMINI_API_KEY"
+GEMINI_API_KEY = os.getenv("AIzaSyC6LKYAB5F1B_j3BOBVFB9xt1-rPbZIMF0")  # ✅ safer than hardcoding
+
+if GEMINI_API_KEY:
+    genai.configure(api_key=GEMINI_API_KEY)
+    gemini_model = genai.GenerativeModel("gemini-1.5-flash")  # ✅ Free, fast model
+else:
+    gemini_model = None
+    print("⚠️ Warning: GEMINI_API_KEY not set. Explanations will be skipped.")
+
+# ---------------- Prediction + Explanation ----------------
+def predict_and_explain(image: Image.Image):
+    # Preprocess image for model
+    img = image.resize((224, 224))  # Adjust to your training input size
+    img_array = np.array(img) / 255.0
+    img_array = np.expand_dims(img_array, axis=0)
+
+    # Model prediction
+    prediction = model.predict(img_array, verbose=0)[0][0]
+
+    if prediction > 0.5:
+        result = f"🟥 Malignant (Cancer Detected) — Confidence: {prediction*100:.2f}%"
+        prompt = "Explain in simple terms to a patient what it means that this skin lesion is malignant."
+    else:
+        result = f"🟩 Benign (No Cancer) — Confidence: {(1-prediction)*100:.2f}%"
+        prompt = "Explain in simple terms to a patient what it means that this skin lesion is benign."
+
+    # Generate explanation with Gemini
+    explanation = "⚠️ Gemini explanation not available (API key missing)."
+    if gemini_model:
+        try:
+            response = gemini_model.generate_content(prompt)
+            explanation = response.text
+        except Exception as e:
+            explanation = f"⚠️ AI explanation failed: {str(e)}"
+
+    return result, explanation
+
+# ---------------- Gradio UI ----------------
+demo = gr.Interface(
+    fn=predict_and_explain,
+    inputs=gr.Image(type="pil", label="📤 Upload Skin Lesion Image"),
+    outputs=[
+        gr.Textbox(label="🔎 Prediction", interactive=False),
+        gr.Textbox(label="📖 Explanation (AI-powered)", interactive=False),
+    ],
+    title="🧬 Skin Cancer Detection with AI Explanation (Gemini)",
+    description="Upload a skin lesion image. The model predicts if it is **Malignant** or **Benign** and Gemini explains the result in simple terms.",
+)
+
+# ---------------- Launch ----------------
+if __name__ == "__main__":
+    demo.launch()