Update app.py

app.py

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+import gradio as gr
+import PIL.Image
+import pandas as pd
+import numpy as np
+import boto3
+from io import BytesIO, StringIO
+from fastai.vision.all import *
+
+def get_x(r): return r['Image Path']
+def get_y(r): return r['Survived']
+
+def ProjectReportSplitter(df):
+    valid_pct = 0.2
+    unique_reports = df['Project Report'].unique()
+    valid_reports = np.random.choice(unique_reports, int(len(unique_reports) * valid_pct), replace=False)
+    valid_idx = df.index[df['Project Report'].isin(valid_reports)].tolist()
+    train_idx = df.index[~df.index.isin(valid_idx)].tolist()
+    return train_idx, valid_idx
+
+# Use a function to resolve path
+def get_x_transformed(r): return open_image_from_s3(get_x(r))
+
+dblock = DataBlock(
+    blocks=(ImageBlock(cls=PILImage), CategoryBlock),
+    splitter=ProjectReportSplitter,
+    get_x=get_x_transformed,
+    get_y=get_y,
+    item_tfms=Resize(460, method='pad', pad_mode='zeros'),
+    batch_tfms=aug_transforms(mult=2, do_flip=True, max_rotate=20, max_zoom=1.1, max_warp=0.2)
+)
+
+# Load your model
+learn = load_learner("templateClassifierDATAhalfEPOCHoneVISION.pkl")
+
+# Print the vocabulary of the model
+print("Model Vocabulary:", learn.dls.vocab)
+
+labels = learn.dls.vocab
+def predict(img):
+    img = PILImage.create(img)
+    pred,pred_idx,probs = learn.predict(img)
+    return {labels[i]: float(probs[i]) for i in range(len(labels))}
+
+# def predict(img):
+#     img = PILImage.create(img)
+#     pred, pred_idx, probs = learn.predict(img)
+#     results = {labels[i]: float(probs[i]) for i in range(len(labels))}
+#     # Adjust results to highlight when 'Survived' meets the 75% threshold
+#     if results['Survived'] >= 0.75:
+#         results['Survived'] = 1.0  # Indicating high confidence of survival
+#     else:
+#         results['Survived'] = 0.0  # Indicating it did not meet the threshold
+#     return results
+# def predict(img):
+#     img = PILImage.create(img)
+#     pred, pred_idx, probs = learn.predict(img)
+#     results = {labels[i]: float(probs[i]) for i in range(len(labels))}
+#     # Adjusting to display survival status based on the threshold
+#     survival_status = 'Survived' if results['Survived'] >= 0.75 else 'Not Survived'
+#     results['Survival Status'] = survival_status
+#     return results
+
+
+# Gradio interface setup
+title = "Photo Culling AI"
+description = "Upload your photo to check if it survives culling."
+article = "This interface uses a model trained to predict whether a photo is relevant for a project report."
+gr.Interface(fn=predict, inputs=gr.Image(), outputs=gr.Label(num_top_classes=2), title=title, description=description, article=article).launch(share=True,show_error=True)
+
+
+
+
+# import gradio as gr
+# import PIL.Image
+# import pandas as pd
+# import boto3
+# from io import BytesIO, StringIO
+# from fastai.vision.all import *
+
+
+# def get_x(r): return r['Image Path']
+    
+# def get_y(r): return r['Survived']
+
+# def ProjectReportSplitter(df):
+#     valid_pct = 0.2
+#     unique_reports = df['Project Report'].unique()
+#     valid_reports = np.random.choice(unique_reports, int(len(unique_reports) * valid_pct), replace=False)
+#     valid_idx = df.index[df['Project Report'].isin(valid_reports)].tolist()
+#     train_idx = df.index[~df.index.isin(valid_idx)].tolist()
+#     return train_idx, valid_idx
+
+
+# # Use a function to resolve path
+# def get_x_transformed(r): return open_image_from_s3(get_x(r))
+
+# dblock = DataBlock(
+#     blocks=(ImageBlock(cls=PILImage), CategoryBlock),
+#     splitter=ProjectReportSplitter,
+#     get_x=get_x_transformed,
+#     get_y=get_y,
+#     item_tfms=Resize(460, method='pad', pad_mode='zeros'),
+#     batch_tfms=aug_transforms(mult=2, do_flip=True, max_rotate=20, max_zoom=1.1, max_warp=0.2)
+# )
+
+# # Load your model
+# learn = load_learner("templateClassifierDATAhalfEPOCHoneVISION.pkl")
+
+# # Print the vocabulary of the model
+# print("Model Vocabulary:", learn.dls.vocab)
+
+
+# # Update prediction function to directly read from S3
+# def predict(img_path):
+#     pred, pred_idx, probs = learn.predict(img_path)
+#     return {learn.dls.vocab[i]: float(probs[i]) for i in range(len(probs))}
+
+# # Gradio interface setup
+# title = "Photo Culling AI"
+# description = "Upload your photo to check if it survives culling."
+# article = "This interface uses a model trained to predict whether a photo is relevant for a project report."
+# gr.Interface(fn=predict, inputs=gr.Image(), outputs=gr.Label(num_top_classes=2), title=title, description=description, article=article).launch(share=True)