Update app.py

app.py

@@ -1,73 +1,72 @@
-import json
-import numpy as np
-import tensorflow as tf
-from PIL import Image
-import gradio as gr
-
-IMG_SIZE = 224
-MODEL_PATH = "model/animal_cnn.keras"
-NAMES_PATH = "model/class_names.json"
-
-# ─── Load Model ───────────────────────
-print("⏳ Loading model...")
-model = tf.keras.models.load_model(MODEL_PATH)
-
-with open(NAMES_PATH) as f:
-    class_names = json.load(f)
-
-print("✅ Model loaded")
-
-# ─── Emoji Map ───────────────────────
-EMOJI = {
-    "cat": "🐱", "dog": "🐶", "lion": "🦁", "tiger": "🐯",
-    "elephant": "🐘", "horse": "🐴", "bear": "🐻",
-    "default": "🐾"
-}
-
-def get_emoji(label):
-    for key in EMOJI:
-        if key in label.lower():
-            return EMOJI[key]
-    return EMOJI["default"]
-
-# ─── Preprocess ──────────────────────
-def prepare(img):
-    img = img.convert("RGB")
-    img = img.resize((IMG_SIZE, IMG_SIZE))
-    arr = np.array(img) / 255.0
-    arr = np.expand_dims(arr, axis=0)
-    return arr
-
-# ─── Predict Function (TOP 5) ─────────
-def predict(image):
-    arr = prepare(image)
-    preds = model.predict(arr)[0]
-
-    # Top 5 predictions
-    top_indices = np.argsort(preds)[::-1][:5]
-
-    result = "🐾 **Prediction Results:**\n\n"
-
-    for i, idx in enumerate(top_indices):
-        label = class_names[idx]
-        confidence = preds[idx] * 100
-        emoji = get_emoji(label)
-
-        if i == 0:
-            result += f"👉 **{emoji} {label.title()} ({confidence:.2f}%)**\n\n"
-        else:
-            result += f"{emoji} {label.title()} ({confidence:.2f}%)\n"
-
-    return result
-
-# ─── Gradio UI ──────────────────────
-app = gr.Interface(
-    fn=predict,
-    inputs=gr.Image(type="pil"),
-    outputs="markdown",
-    title="🐾 Animal Classifier",
-    description="Upload an image → AI will tell what animal it is"
-)
-
-# ─── Run ────────────────────────────
-app.launch()
+import os
+import json
+import numpy as np
+import tensorflow as tf
+from flask import Flask, request, jsonify
+from PIL import Image
+
+app = Flask(__name__)
+
+# ✅ Correct Paths (IMPORTANT FIX)
+MODEL_PATH = "animal_cnn.keras"
+CLASS_NAMES_PATH = "class_names.json"
+
+# ✅ Load Model
+print("⏳ Loading model...")
+model = tf.keras.models.load_model(MODEL_PATH)
+
+# ✅ Load Class Names
+with open(CLASS_NAMES_PATH, "r") as f:
+    class_names = json.load(f)
+
+print("✅ Model loaded successfully!")
+
+
+# ✅ Image Preprocessing Function
+def preprocess_image(image):
+    image = image.resize((224, 224))   # same as training
+    image = np.array(image) / 255.0
+    image = np.expand_dims(image, axis=0)
+    return image
+
+
+# ✅ Home Route (Test)
+@app.route("/")
+def home():
+    return "🐾 Animal Classifier API is Running!"
+
+
+# ✅ Prediction Route
+@app.route("/predict", methods=["POST"])
+def predict():
+    try:
+        if "file" not in request.files:
+            return jsonify({"error": "No file uploaded"}), 400
+
+        file = request.files["file"]
+
+        # Open image
+        image = Image.open(file).convert("RGB")
+
+        # Preprocess
+        processed_image = preprocess_image(image)
+
+        # Predict
+        predictions = model.predict(processed_image)[0]
+        top_index = np.argmax(predictions)
+        confidence = float(predictions[top_index])
+
+        result = {
+            "prediction": class_names[top_index],
+            "confidence": round(confidence * 100, 2)
+        }
+
+        return jsonify(result)
+
+    except Exception as e:
+        return jsonify({"error": str(e)}), 500
+
+
+# ✅ Run App
+if __name__ == "__main__":
+    app.run(host="0.0.0.0", port=7860)