Update app.py

app.py

@@ -1,63 +1,38 @@
-import streamlit as st
-from transformers import pipeline
-from PIL import Image
-
-MODEL_1 = "ShaharAdar/best-model-try"
-MIN_ACEPTABLE_SCORE = 0.1
-MAX_N_LABELS = 5
-MODELS = [
-            "ShaharAdar/best-model-try", #Classifição geral
-        ]
-
-def classify(image, model):
-    classifier = pipeline("image-classification", model=model)
-    result= classifier(image)
-    return result
-
-def save_result(result):
-    st.write("In the future, this function will save the result in a database.")
-
-def print_result(result):
-
-    comulative_discarded_score = 0
-    for i in range(len(result)):
-        if result[i]['score'] < MIN_ACEPTABLE_SCORE:
-            comulative_discarded_score += result[i]['score']
-        else:
-            st.write(result[i]['label'])
-            st.progress(result[i]['score'])
-            st.write(result[i]['score'])
-
-    st.write(f"comulative_discarded_score:")
-    st.progress(comulative_discarded_score)
-    st.write(comulative_discarded_score)
-    
-
-
-def main():
-    st.title("Image Classification")
-    st.write("This is a simple web app to test and compare different image classifier models using Hugging Face's image-classification pipeline.")
-    st.write("From time to time more models will be added to the list. If you want to add a model, please open an issue on the GitHub repository.")
-    st.write("If you like this project, please consider liking it or buying me a coffee. It will help me to keep working on this and other projects. Thank you!")
-
-   
-    input_image = st.file_uploader("Upload Image")
-    shosen_model = st.selectbox("Select the model to use",  MODELS)
-    
-    
-    if input_image is not None:
-        image_to_classify = Image.open(input_image)
-        st.image(image_to_classify, caption="Uploaded Image")
-        if st.button("Classify"):
-            image_to_classify = Image.open(input_image)
-            classification_obj1 =[]
-            #avable_models = st.selectbox
-            
-            classification_result = classify(image_to_classify, shosen_model)
-            classification_obj1.append(classification_result)
-            print_result(classification_result)
-            save_result(classification_result)
-
-
-if __name__ == "__main__":
-    main()
+import numpy as np
+import tensorflow as tf
+import gradio as gr
+from tensorflow.keras.optimizers import Adam
+from huggingface_hub import from_pretrained_keras
+
+reloaded_model = from_pretrained_keras('ShaharAdar/best-model-try', return_dict=False)
+reloaded_model.compile(optimizer=Adam(0.00001),
+                       loss='categorical_crossentropy',
+                       metrics=['accuracy']
+)
+
+def classify_image(image):
+    # Resize the image to 224x224 as expected by your model
+    image = tf.image.resize(image, (224, 224))
+
+    # Add a batch dimension and make prediction
+    image = tf.expand_dims(image, 0)  # model expects a batch of images
+    preds = reloaded_model.predict(image)
+
+    # Assuming the output is a softmax layer, get the predicted class index
+    predicted_class = tf.argmax(preds, axis=1).numpy()[0]
+
+    # Optionally, convert class index to label if you have a mapping
+    labels = ['Clams', 'Corals', 'Crabs', 'Dolphin', 'Eel', 'Fish',
+              'Jelly Fish', 'Lobster', 'Nudibranchs', 'Octopus', 'Otter',
+              'Penguin', 'Puffers', 'Sea Rays', 'Sea Urchins', 'Seahorse',
+              'Seal', 'Sharks', 'Shrimp', 'Squid', 'Starfish',
+              'Turtle_Tortoise', 'Whale']    # example labels
+    return labels[predicted_class]
+
+import gradio as gr
+
+# Define the interface
+iface = gr.Interface(fn=classify_image, inputs="image", outputs="text")
+
+# Launch the application
+iface.launch()