Update app.py

app.py

@@ -1,40 +1,52 @@
-import gradio as gr
-import tensorflow as tf
+from fastapi import FastAPI, Request, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from tensorflow.keras.models import load_model
 import numpy as np
-from PIL import Image
 
-# Load the trained model
-model = tf.keras.models.load_model("cancer_classifier.h5")
+# ---------- Load the Trained Model ----------
+MODEL_PATH = "cancer_classifier.h5"
+model = load_model(MODEL_PATH)
 
-# Define the class names (make sure they match your dataset)
-class_names = ['Basophil', 'Eosinophil', 'Erythroblast', 'IG', 'lymphocyte', 'Monocyte', 'Neutrophil', 'Platelet']
+# ---------- Initialize FastAPI ----------
+app = FastAPI(title="GenAI Health Insight API", version="2.0")
 
-# Define prediction function
-def predict(image):
-    image = image.resize((224, 224))
-    img_array = np.array(image) / 255.0
-    img_array = np.expand_dims(img_array, axis=0)
+# ---------- Enable CORS (so your friend’s frontend can call it) ----------
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],  # Or set ["https://yourfrontend.vercel.app"] for extra safety
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
 
-    prediction = model.predict(img_array)
-    class_index = np.argmax(prediction)
-    confidence = float(np.max(prediction))
+# ---------- Root Endpoint ----------
+@app.get("/")
+def home():
+    return {"message": "Welcome to GenAI Health Insight API 🚀"}
 
-    return {
-        "Predicted Class": class_names[class_index],
-        "Confidence": round(confidence * 100, 2)
+# ---------- Prediction Endpoint ----------
+@app.post("/predict")
+async def predict(request: Request):
+    """
+    Input example (JSON):
+    {
+        "features": [value1, value2, ..., valueN]
     }
+    """
+    data = await request.json()
+    input_data = data.get("features")
 
-# Gradio interface
-iface = gr.Interface(
-    fn=predict,
-    inputs=gr.Image(type="pil", label="Upload Blood Cell Image"),
-    outputs=[
-        gr.Label(label="Prediction"),
-    ],
-    title="🧬 Blood Cell Cancer Classifier",
-    description="Upload a blood cell image to classify whether it's cancerous or not using a fine-tuned EfficientNetB3 model.",
-    theme="gradio/soft"
-)
+    if input_data is None:
+        raise HTTPException(status_code=400, detail="Missing 'features' in request body")
+    
+    # Prepare and predict
+    input_array = np.array(input_data).reshape(1, -1)
+    prediction = model.predict(input_array)
+    result = float(prediction[0][0])
+
+    return {"prediction": result, "status": "success"}
 
-if __name__ == "__main__":
-    iface.launch()
+# ---------- Health Check Endpoint ----------
+@app.get("/health")
+def health():
+    return {"status": "healthy"}