Update app.py

app.py

@@ -1,38 +1,63 @@
-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')
-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()
+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()