Create app.py

app.py

@@ -0,0 +1,62 @@
+import gradio as gr
+from PIL import Image
+import numpy as np
+from pickle import load
+from tensorflow.keras.applications.xception import Xception
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing.sequence import pad_sequences
+from matplotlib import pyplot as plt
+
+def extract_features(filename, model):
+    try:
+        image = Image.open(filename)
+    except:
+        print("ERROR: Couldn't open image! Make sure the image path and extension is correct")
+    image = image.resize((299,299))
+    image = np.array(image)
+    # for images that has 4 channels, we convert them into 3 channels
+    if image.shape[2] == 4: 
+        image = image[..., :3]
+    image = np.expand_dims(image, axis=0)
+    image = image/127.5
+    image = image - 1.0
+    feature = model.predict(image)
+    return feature
+
+def word_for_id(integer, tokenizer):
+    for word, index in tokenizer.word_index.items():
+        if index == integer:
+            return word
+    return None
+
+def generate_desc(model, tokenizer, photo, max_length):
+    in_text = 'start'
+    for i in range(max_length):
+        sequence = tokenizer.texts_to_sequences([in_text])[0]
+        sequence = pad_sequences([sequence], maxlen=max_length)
+        pred = model.predict([photo,sequence], verbose=0)
+        pred = np.argmax(pred)
+        word = word_for_id(pred, tokenizer)
+        if word is None:
+            break
+        in_text += ' ' + word
+        if word == 'end':
+            break
+    return in_text.split()[1:-1]
+
+max_length = 32
+tokenizer = load(open("tokenizer.p","rb"))
+model = load_model('models/model_9.h5')
+xception_model = Xception(include_top=False, pooling="avg")
+
+def caption_generator(img_path):
+    photo = extract_features(img_path, xception_model)
+    img = Image.open(img_path)
+    description = generate_desc(model, tokenizer, photo, max_length)
+    description = ' '.join(description)
+    return description
+
+inputs = gr.inputs.File(label="Select an Image")
+outputs = gr.outputs.Textbox(label="Description")
+
+gr.Interface(fn=caption_generator , inputs=inputs, outputs=outputs, capture_session=True).launch()