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app.py

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+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. Place your trained model in the project folder.")
+
+model = tf.keras.models.load_model(MODEL_PATH)
+
+# ---------------- Gemini API ----------------
+# 1. Go to https://aistudio.google.com/app/apikey to create a FREE API key
+# 2. Replace below with your API key
+GEMINI_API_KEY = "AIzaSyC6LKYAB5F1B_j3BOBVFB9xt1-rPbZIMF0"  
+genai.configure(api_key=GEMINI_API_KEY)
+
+gemini_model = genai.GenerativeModel("gemini-1.5-flash")  # ✅ Free, fast model
+
+# ---------------- Prediction + Explanation ----------------
+def predict_and_explain(image):
+    # Preprocess image
+    img = image.resize((224, 224))  # Adjust if your model uses a different size
+    img_array = np.array(img) / 255.0
+    img_array = np.expand_dims(img_array, axis=0)
+    
+    # Predict
+    prediction = model.predict(img_array)[0][0]
+    if prediction > 0.5:
+        result = f"🟥 Malignant (Cancer Detected) with {prediction*100:.2f}% confidence"
+        prompt = "Explain in simple terms to a patient what it means that this skin lesion is Malignant."
+    else:
+        result = f"🟩 Benign (No Cancer) with {(1-prediction)*100:.2f}% confidence"
+        prompt = "Explain in simple terms to a patient what it means that this skin lesion is Benign."
+    
+    # Generate explanation using Gemini
+    explanation = "Explanation not available."
+    try:
+        response = gemini_model.generate_content(prompt)
+        explanation = response.text
+    except Exception as e:
+        explanation = f"AI explanation failed: {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"), gr.Textbox(label="Explanation")],
+    title="🧬 Skin Cancer Detection with AI Explanation (Gemini)",
+    description="Upload a skin lesion image. The model predicts if it is Malignant or Benign and explains the result in simple terms."
+)
+
+# ---------------- Launch ----------------
+if __name__ == "__main__":
+    demo.launch()

final_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8ff8ac00211462b4b533154bae22362e5501838219a1dfb9eecf3bd8121ca533
+size 24526712

requirements.txt

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+tensorflow
+opencv-python
+numpy
+gradio