Update app.py

app.py

@@ -6,39 +6,91 @@ import tensorflow as tf
 import numpy as np
 import pickle
 from PIL import Image
-import json
 
-# ----------------------------
-# Rebuild model safely (Keras 3 compatible)
-# ----------------------------
-with open("fusion_config.json", "r") as f:
-    model_json = f.read()
+# ===============================
+# CONSTANTS
+# ===============================
+IMG_SIZE = (128, 128)
+MAX_LEN = 50
+NUM_CLASSES = 4
+LABELS = ["Critical", "High", "Medium", "Low"]
 
-fusion_model = tf.keras.models.model_from_json(model_json)
-fusion_model.load_weights("fusion_weights.weights.h5")
+# ===============================
+# IMAGE ENCODER (MATCH TRAINING)
+# ===============================
+def build_image_encoder():
+    inp = tf.keras.Input(shape=(128,128,3), name="image_input")
+
+    x = tf.keras.layers.Conv2D(32, 3, activation="relu")(inp)
+    x = tf.keras.layers.MaxPooling2D()(x)
+    x = tf.keras.layers.Conv2D(64, 3, activation="relu")(x)
+    x = tf.keras.layers.MaxPooling2D()(x)
+    x = tf.keras.layers.Conv2D(128, 3, activation="relu")(x)
+    x = tf.keras.layers.MaxPooling2D()(x)
+    x = tf.keras.layers.Flatten()(x)
+    x = tf.keras.layers.Dense(128, activation="relu", name="image_embedding")(x)
+
+    return tf.keras.Model(inp, x)
+
+# ===============================
+# TEXT ENCODER (MATCH TRAINING)
+# ===============================
+def build_text_encoder(vocab_size=20000):
+    inp = tf.keras.Input(shape=(MAX_LEN,), name="text_input")
+
+    x = tf.keras.layers.Embedding(vocab_size, 128)(inp)
+    x = tf.keras.layers.LSTM(64)(x)
+    x = tf.keras.layers.Dense(64, activation="relu", name="text_embedding")(x)
+
+    return tf.keras.Model(inp, x)
+
+# ===============================
+# FUSION MODEL
+# ===============================
+image_encoder = build_image_encoder()
+text_encoder = build_text_encoder()
 
-print("✅ Model rebuilt + weights loaded")
+combined = tf.keras.layers.Concatenate()([
+    image_encoder.output,
+    text_encoder.output
+])
 
-# ----------------------------
-# Load tokenizer
-# ----------------------------
+x = tf.keras.layers.Dense(128, activation="relu")(combined)
+x = tf.keras.layers.Dense(64, activation="relu")(x)
+output = tf.keras.layers.Dense(NUM_CLASSES, activation="softmax")(x)
+
+fusion_model = tf.keras.Model(
+    inputs=[image_encoder.input, text_encoder.input],
+    outputs=output
+)
+
+# ===============================
+# LOAD WEIGHTS (SAFE)
+# ===============================
+fusion_model.load_weights("fusion_weights.weights.h5")
+print("✅ Fusion weights loaded")
+
+# ===============================
+# LOAD TOKENIZER
+# ===============================
 with open("tokenizer.pkl", "rb") as f:
     tokenizer = pickle.load(f)
 
-# ----------------------------
-IMG_SIZE = (128, 128)
-MAX_LEN = 50
-LABELS = ["Critical", "High", "Medium", "Low"]
-
+# ===============================
+# PREPROCESS
+# ===============================
 def preprocess_image(image):
     image = image.convert("RGB").resize(IMG_SIZE)
-    img = np.array(image, dtype=np.float32) / 255.0
+    img = np.array(image) / 255.0
     return np.expand_dims(img, 0)
 
 def preprocess_text(text):
     seq = tokenizer.texts_to_sequences([text or ""])
     return tf.keras.preprocessing.sequence.pad_sequences(seq, maxlen=MAX_LEN)
 
+# ===============================
+# PREDICT
+# ===============================
 def predict_ticket(image, text):
     if image is None:
         return {}
@@ -49,12 +101,12 @@ def predict_ticket(image, text):
     probs = fusion_model.predict([img, txt], verbose=0)[0]
     return dict(zip(LABELS, map(float, probs)))
 
+# ===============================
+# UI
+# ===============================
 interface = gr.Interface(
     fn=predict_ticket,
-    inputs=[
-        gr.Image(type="pil"),
-        gr.Textbox(lines=3)
-    ],
+    inputs=[gr.Image(type="pil"), gr.Textbox(lines=3)],
     outputs=gr.Label(num_top_classes=4),
     title="Ticket Severity Classifier"
 )