Update app.py

app.py

@@ -1,24 +1,8 @@
-from fastapi import FastAPI, UploadFile, File
-from fastapi.responses import JSONResponse
-from fastapi.middleware.cors import CORSMiddleware
+import gradio as gr
 from keras.models import load_model
 from huggingface_hub import hf_hub_download
 from PIL import Image
 import numpy as np
-import gradio as gr
-
-# -------------------------------
-# FASTAPI SETUP
-# -------------------------------
-app = FastAPI(title="Cancer Detection API")
-
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"],
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
 
 # -------------------------------
 # MODEL LOADING
@@ -27,6 +11,7 @@ MODEL_PATH = hf_hub_download(
     repo_id="aadityaramrame/blood-cell-cancer-detector",
     filename="cancer_classifier.h5"
 )
+
 model = load_model(MODEL_PATH)
 
 # Class mapping
@@ -40,36 +25,7 @@ CLASSES = [
 ]
 
 # -------------------------------
-# FASTAPI ENDPOINTS
-# -------------------------------
-@app.get("/")
-async def root():
-    return {"message": "🚀 Cancer Detection API is live!"}
-
-@app.post("/predict")
-async def predict(file: UploadFile = File(...)):
-    try:
-        image = Image.open(file.file).convert("RGB").resize((224, 224))
-        img_array = np.expand_dims(np.array(image) / 255.0, axis=0)
-
-        prediction = model.predict(img_array)
-        predicted_class = int(np.argmax(prediction))
-        confidence = float(np.max(prediction))
-
-        label = CLASSES[predicted_class]
-
-        return JSONResponse(
-            content={
-                "predicted_class": label,
-                "confidence": round(confidence, 3),
-                "raw_index": predicted_class
-            }
-        )
-    except Exception as e:
-        return JSONResponse(content={"error": str(e)}, status_code=500)
-
-# -------------------------------
-# GRADIO FRONTEND
+# PREDICTION FUNCTION
 # -------------------------------
 def classify_cancer(image):
     try:
@@ -79,19 +35,31 @@ def classify_cancer(image):
         predicted_class = int(np.argmax(prediction))
         confidence = float(np.max(prediction))
         label = CLASSES[predicted_class]
-        return {label: confidence}
+        return f"🧫 **Predicted Cell Type:** {label}\n📊 **Confidence:** {confidence:.3f}"
     except Exception as e:
-        return {"Error": str(e)}
+        return f"⚠️ Error: {str(e)}"
 
-gradio_interface = gr.Interface(
+# -------------------------------
+# GRADIO INTERFACE
+# -------------------------------
+demo = gr.Interface(
     fn=classify_cancer,
-    inputs=gr.Image(type="pil", label="Upload Blood Cell Image"),
-    outputs=gr.Label(num_top_classes=3, label="Predicted Cell Type"),
-    title="🧫 Blood Cell Cancer Detection",
-    description="Upload a blood cell image to predict the cell type using a trained CNN model.",
+    inputs=gr.Image(type="pil", label="📸 Upload Blood Cell Image"),
+    outputs=gr.Markdown(label="Result"),
+    title="🧬 Blood Cell Cancer Detection",
+    description=(
+        "Upload a blood cell image to classify its type using a trained CNN model.\n"
+        "Model trained on microscopic blood cell images for cancer detection."
+    ),
+    examples=[
+        ["example1.jpg"],
+        ["example2.jpg"]
+    ],
     theme="soft"
 )
 
-# Mount Gradio on FastAPI
-app = gr.mount_gradio_app(app, gradio_interface, path="/")
-
+# -------------------------------
+# LAUNCH
+# -------------------------------
+if __name__ == "__main__":
+    demo.launch()