Create app.py

app.py

@@ -0,0 +1,74 @@
+import streamlit as st
+from transformers import pipeline
+from PIL import Image
+
+MODEL_1 = "google/vit-base-patch16-224"
+MIN_ACEPTABLE_SCORE = 0.1
+MAX_N_LABELS = 5
+MODEL_2 = "nateraw/vit-age-classifier"
+MODELS = [
+            "google/vit-base-patch16-224", #Classifição geral
+            "nateraw/vit-age-classifier", #Classifição de idade
+            "microsoft/resnet-50", #Classifição geral
+            #NOT OK "microsoft/beit-base-patch16-224-pt22k-ft22k", #Classifição geral
+            "Falconsai/nsfw_image_detection", #Classifição NSFW
+            "cafeai/cafe_aesthetic", #Classifição de estética
+            "timm/vit_large_patch14_clip_224.openai_ft_in12k_in1k", #Classifição geral
+            "timm/vit_base_patch16_224_in21k", #Classifição geral escolhida pelo copilot 
+            "microsoft/resnet-18", #Classifição geral
+            "microsoft/resnet-34", #Classifição geral escolhida pelo copilot 
+            "microsoft/resnet-101", #Classifição geral escolhida pelo copilot 
+            "microsoft/resnet-152", #Classifição geral escolhida pelo copilot
+            "microsoft/resnet-50-kinetics-400", #Classifição geral escolhida pelo copilot
+            "microsoft/swin-tiny-patch4-window7-224",#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")
+    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", use_column_width=True)
+
+        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()