initial commit, app.py not built

app.py

@@ -0,0 +1,65 @@
+# prompt: create gradio app to load the model and run it 
+
+import gradio as gr
+import tensorflow as tf
+import numpy as np
+import requests
+from tensorflow.keras.preprocessing.image import img_to_array, load_img
+from tensorflow.keras.applications.inception_v3 import preprocess_input
+import re
+
+# Load the model
+model = tf.keras.models.load_model('caption_model.h5')
+
+# Load tokenizer (you'll need to adapt this to your actual tokenizer loading)
+# Replace with your actual tokenizer loading
+# Example using pickle
+import pickle
+with open('tokenizer.pickle', 'rb') as handle:
+    tokenizer = pickle.load(handle)
+
+vocab_size = len(tokenizer.word_index) + 1
+max_caption_length = 34 # Replace with your actual max_caption_length
+cnn_output_dim = 2048
+
+
+def preprocess_image(image_path):
+    img = load_img(image_path, target_size=(299, 299))
+    img = img_to_array(img)
+    img = np.expand_dims(img, axis=0)
+    img = preprocess_input(img)
+    return img
+
+def greedy_generator(image_features):
+    in_text = 'start '
+    for _ in range(max_caption_length):
+        sequence = tokenizer.texts_to_sequences([in_text])[0]
+        sequence = tf.keras.preprocessing.sequence.pad_sequences([sequence], maxlen=max_caption_length).reshape((1,max_caption_length))
+        prediction = model.predict([image_features.reshape(1,cnn_output_dim), sequence], verbose=0)
+        idx = np.argmax(prediction)
+        word = tokenizer.index_word[idx]
+        in_text += ' ' + word
+        if word == 'end':
+            break
+    in_text = in_text.replace('start ', '')
+    in_text = in_text.replace(' end', '')
+    return in_text
+
+
+def predict(image):
+    processed_image = preprocess_image(image)
+    image_features = model.layers[2].predict(processed_image, verbose = 0) # assuming InceptionV3 is the second layer
+    image_features = image_features.flatten()
+
+    caption = greedy_generator(image_features)
+    return caption
+
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="filepath"),
+    outputs="text",
+    title="Image Captioning",
+    description="Upload an image and get a caption!"
+)
+
+iface.launch()

caption_model.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2cde03932fe1d948942415aa2cc0574bca844ea7a73f03b2f604189d3453d825
+size 66378520