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Update pages/Entorno de Ejecución.py
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pages/Entorno de Ejecución.py
CHANGED
@@ -6,6 +6,7 @@ from PIL import Image
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import os
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import cv2
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import numpy as np
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st.set_page_config(
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page_title = 'Patacotrón',
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@@ -44,6 +45,15 @@ def multiclass_prediction(classifier, important_class): #made for hf zero-shot p
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class_score = clase['score']
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return (labels[0] if len(labels) == 1 else labels, score, class_score)
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@st.cache_resource
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def load_clip():
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classifier = pipeline("zero-shot-image-classification", model = 'openai/clip-vit-large-patch14-336')
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@@ -185,7 +195,7 @@ with vit:
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# y_gorrito = clase["score"]
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#y_gorrito = classifier[0]["score"]
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-
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y_gorrito = vit_ensemble(classifiers, img)
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if round(float(y_gorrito*100)) >= threshold:
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import os
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import cv2
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import numpy as np
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import requests
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st.set_page_config(
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page_title = 'Patacotrón',
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class_score = clase['score']
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return (labels[0] if len(labels) == 1 else labels, score, class_score)
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API_URL = "https://api-inference.huggingface.co/models"
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headers = {"Authorization": f"Bearer {token}"}
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def query(filename, model_name): #HF API
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with open(filename, "rb") as f:
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data = f.read()
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response = requests.post(API_URL + / + model_name', headers=headers, data=data)
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return response.json #.json
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@st.cache_resource
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def load_clip():
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classifier = pipeline("zero-shot-image-classification", model = 'openai/clip-vit-large-patch14-336')
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# y_gorrito = clase["score"]
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#y_gorrito = classifier[0]["score"]
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api_result = query(img, model_choice[0])
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y_gorrito = vit_ensemble(classifiers, img)
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if round(float(y_gorrito*100)) >= threshold:
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