Update app.py

app.py

@@ -1,71 +1,71 @@
-import gradio as gr
-from PIL import Image
-import numpy as np
-import tensorflow as tf
-from tensorflow.keras.applications.vgg16 import VGG16, preprocess_input
-from tensorflow.keras.preprocessing.image import img_to_array
-from tensorflow.keras.preprocessing.sequence import pad_sequences
-import pickle
-
-# Load your pre-trained model and tokenizer
-model = tf.keras.models.load_model("model.h5")
-with open("tokenizer.pkl", "rb") as handle:
-    tokenizer = pickle.load(handle)
-
-# Load your precomputed features if required (else comment out)
-# with open("features.pkl", "rb") as f:
-#     features = pickle.load(f)
-
-# Image feature extractor model
-feature_extractor = VGG16()
-feature_extractor = tf.keras.Model(feature_extractor.input, feature_extractor.layers[-2].output)
-
-# Description generation function
-def generate_caption(image):
-    # Preprocess the image
-    image = image.resize((224, 224))
-    image = img_to_array(image)
-    image = np.expand_dims(image, axis=0)
-    image = preprocess_input(image)
-
-    # Extract features
-    feature = feature_extractor.predict(image, verbose=0)
-
-    # Generate caption (mock example: replace with your real inference loop)
-    input_text = 'startseq'
-    max_length = 34  # set this to your model's max_length
-
-    for _ in range(max_length):
-        sequence = tokenizer.texts_to_sequences([input_text])[0]
-        sequence = pad_sequences([sequence], maxlen=max_length)
-        yhat = model.predict([feature, sequence], verbose=0)
-        yhat = np.argmax(yhat)
-        word = ''
-        for w, i in tokenizer.word_index.items():
-            if i == yhat:
-                word = w
-                break
-        if word == 'endseq' or word == '':
-            break
-        input_text += ' ' + word
-
-    caption = input_text.replace('startseq', '').strip()
-    return caption
-
-# Gradio Interface
-title = "📸 Image Caption Generator"
-description = "Upload an image and let the AI generate a descriptive caption for it."
-theme = "soft"
-
-iface = gr.Interface(
-    fn=generate_caption,
-    inputs=gr.Image(type="pil"),
-    outputs=gr.Textbox(label="Generated Caption"),
-    title=title,
-    description=description,
-    theme=theme,
-    allow_flagging="never"
-)
-
-if __name__ == "__main__":
-    iface.launch()
+import gradio as gr
+from PIL import Image
+import numpy as np
+import tensorflow as tf
+from tensorflow.keras.applications.vgg16 import VGG16, preprocess_input
+from tensorflow.keras.preprocessing.image import img_to_array
+from tensorflow.keras.preprocessing.sequence import pad_sequences
+import pickle
+
+# Load your pre-trained model and tokenizer
+model = tf.keras.models.load_model("caption_model.h5")
+with open("tokenizer.pkl", "rb") as handle:
+    tokenizer = pickle.load(handle)
+
+# Load your precomputed features if required (else comment out)
+# with open("features.pkl", "rb") as f:
+#     features = pickle.load(f)
+
+# Image feature extractor model
+feature_extractor = VGG16()
+feature_extractor = tf.keras.Model(feature_extractor.input, feature_extractor.layers[-2].output)
+
+# Description generation function
+def generate_caption(image):
+    # Preprocess the image
+    image = image.resize((224, 224))
+    image = img_to_array(image)
+    image = np.expand_dims(image, axis=0)
+    image = preprocess_input(image)
+
+    # Extract features
+    feature = feature_extractor.predict(image, verbose=0)
+
+    # Generate caption (mock example: replace with your real inference loop)
+    input_text = 'startseq'
+    max_length = 34  # set this to your model's max_length
+
+    for _ in range(max_length):
+        sequence = tokenizer.texts_to_sequences([input_text])[0]
+        sequence = pad_sequences([sequence], maxlen=max_length)
+        yhat = model.predict([feature, sequence], verbose=0)
+        yhat = np.argmax(yhat)
+        word = ''
+        for w, i in tokenizer.word_index.items():
+            if i == yhat:
+                word = w
+                break
+        if word == 'endseq' or word == '':
+            break
+        input_text += ' ' + word
+
+    caption = input_text.replace('startseq', '').strip()
+    return caption
+
+# Gradio Interface
+title = "📸 Image Caption Generator"
+description = "Upload an image and let the AI generate a descriptive caption for it."
+theme = "soft"
+
+iface = gr.Interface(
+    fn=generate_caption,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Textbox(label="Generated Caption"),
+    title=title,
+    description=description,
+    theme=theme,
+    allow_flagging="never"
+)
+
+if __name__ == "__main__":
+    iface.launch()