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# ================================
# ENVIRONMENT (MUST BE FIRST)
# ================================
import os
os.environ["KERAS_BACKEND"] = "tensorflow"
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
# ================================
# IMPORTS
# ================================
import gradio as gr
import tensorflow as tf
import numpy as np
import pickle
from PIL import Image
# ================================
# CONSTANTS (MUST MATCH TRAINING)
# ================================
IMG_SIZE = (128, 128)
MAX_LEN = 50
LABELS = ["Critical", "High", "Medium", "Low"]
# ================================
# LOAD MODEL (KERAS 3 SAFE)
# ================================
model = tf.keras.models.load_model(
"fusion_model_keras3.keras",
compile=False
)
print("✅ Fusion model loaded")
# ================================
# LOAD TOKENIZER
# ================================
with open("tokenizer.pkl", "rb") as f:
tokenizer = pickle.load(f)
print("✅ Tokenizer loaded")
# ================================
# IMAGE PREPROCESS
# ================================
def preprocess_image(image: Image.Image):
image = image.convert("RGB")
image = image.resize(IMG_SIZE)
img = np.array(image, dtype=np.float32) / 255.0
img = np.expand_dims(img, axis=0)
return img
# ================================
# TEXT PREPROCESS
# ================================
def preprocess_text(text: str):
if text is None:
text = ""
seq = tokenizer.texts_to_sequences([text])
padded = tf.keras.preprocessing.sequence.pad_sequences(
seq, maxlen=MAX_LEN
)
return padded
# ================================
# PREDICTION FUNCTION
# ================================
def predict_ticket(image, text):
if image is None:
return {
"Critical": 0.0,
"High": 0.0,
"Medium": 0.0,
"Low": 0.0,
}
img = preprocess_image(image)
txt = preprocess_text(text)
probs = model.predict([img, txt], verbose=0)[0]
return {
"Critical": float(probs[0]),
"High": float(probs[1]),
"Medium": float(probs[2]),
"Low": float(probs[3]),
}
# ================================
# GRADIO UI
# ================================
interface = gr.Interface(
fn=predict_ticket,
inputs=[
gr.Image(type="pil", label="📤 Upload Ticket Screenshot"),
gr.Textbox(
lines=4,
placeholder="Describe the issue (recommended)",
label="✍️ Ticket Description"
)
],
outputs=gr.Label(num_top_classes=4, label="🚨 Predicted Severity"),
title="🎫 Ticket Severity Classification",
description=(
"CNN + NLP **Fusion Model** for ticket urgency detection.\n\n"
"**Classes:** Critical | High | Medium | Low"
)
)
interface.launch() |