Spaces:
Sleeping
Sleeping
csjhonathan
commited on
Commit
·
045bec2
1
Parent(s):
713fb63
ajusta moderador para aceitar base64
Browse files
app.py
CHANGED
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@@ -5,6 +5,8 @@ import torch
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import timm
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from PIL import Image
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import numpy as np
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try:
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eva02_model = timm.create_model('hf_hub:SmilingWolf/wd-eva02-large-tagger-v3', pretrained=True)
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@@ -24,6 +26,18 @@ except Exception as e:
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content_model = pipeline("image-classification", model="facebook/convnext-base-224")
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nsfw_model = pipeline("image-classification", model="Falconsai/nsfw_image_detection")
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def analyze_with_eva02(image):
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if eva02_model is None:
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return [], []
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@@ -50,7 +64,33 @@ def analyze_with_eva02(image):
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return detected_tags, tag_scores
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def analyze_image(
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if eva02_model is not None:
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eva02_tags_detected, eva02_scores = analyze_with_eva02(image)
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combined_labels = " ".join(eva02_tags_detected).lower()
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@@ -131,14 +171,12 @@ def analyze_image(image):
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"grave", "tombstone", "funeral", "coffin", "burial"
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]
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-
# Palavras-chave para comportamento SUSPEITO (não normal)
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suspicious_keywords = [
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"unconscious", "motionless", "lifeless", "rigid", "cold", "pale",
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"injured", "wounded", "bleeding", "hurt", "pain", "distress",
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"abandoned", "neglected", "starving", "malnourished", "chained", "caged"
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]
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# Palavras-chave para comportamento NORMAL de cachorros (não suspeito)
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normal_dog_behavior = [
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"sleeping", "resting", "lying", "sitting", "playing", "running", "walking",
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"happy", "excited", "alert", "awake", "active", "energetic", "playful"
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@@ -155,14 +193,10 @@ def analyze_image(image):
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abuse = any(keyword in combined_labels for keyword in abuse_keywords)
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death = any(keyword in combined_labels for keyword in death_keywords)
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# Detecção mais inteligente para cachorros
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has_suspicious_behavior = any(keyword in combined_labels for keyword in suspicious_keywords)
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has_normal_behavior = any(keyword in combined_labels for keyword in normal_dog_behavior)
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has_death_indicators = any(keyword in combined_labels for keyword in death_keywords)
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# Se é um cachorro, só marcar como suspeito se:
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# 1. Tem indicadores de morte OU
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# 2. Tem comportamento suspeito E NÃO tem comportamento normal
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if is_dog:
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suspicious_animal = has_death_indicators or (has_suspicious_behavior and not has_normal_behavior)
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dead_dog = has_death_indicators
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@@ -228,9 +262,13 @@ def analyze_image(image):
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demo = gr.Interface(
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fn=analyze_image,
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inputs=
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outputs="json",
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title="Dog / Human Safety Detector"
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)
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if __name__ == "__main__":
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import timm
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from PIL import Image
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import numpy as np
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import base64
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from io import BytesIO
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try:
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eva02_model = timm.create_model('hf_hub:SmilingWolf/wd-eva02-large-tagger-v3', pretrained=True)
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content_model = pipeline("image-classification", model="facebook/convnext-base-224")
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nsfw_model = pipeline("image-classification", model="Falconsai/nsfw_image_detection")
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def decode_base64_image(base64_string):
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if ',' in base64_string:
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base64_string = base64_string.split(',')[1]
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try:
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image_data = base64.b64decode(base64_string)
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image = Image.open(BytesIO(image_data))
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return image
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except Exception as e:
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print(f"Erro ao decodificar base64: {e}")
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return None
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def analyze_with_eva02(image):
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if eva02_model is None:
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return [], []
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return detected_tags, tag_scores
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def analyze_image(image_input, base64_input=None):
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image = None
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if base64_input and isinstance(base64_input, str) and base64_input.strip():
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image = decode_base64_image(base64_input)
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if image is None:
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return {
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"content": "unknown",
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"adult_content": False,
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"violence": False,
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"sensitive_content": False,
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"content_description": "Erro ao processar imagem base64."
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}
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elif image_input is not None:
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image = image_input
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else:
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return {
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"content": "unknown",
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"adult_content": False,
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"violence": False,
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"sensitive_content": False,
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"content_description": "Nenhuma imagem fornecida."
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}
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if isinstance(image, np.ndarray):
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image = Image.fromarray(image)
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if eva02_model is not None:
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eva02_tags_detected, eva02_scores = analyze_with_eva02(image)
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combined_labels = " ".join(eva02_tags_detected).lower()
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"grave", "tombstone", "funeral", "coffin", "burial"
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]
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suspicious_keywords = [
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"unconscious", "motionless", "lifeless", "rigid", "cold", "pale",
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"injured", "wounded", "bleeding", "hurt", "pain", "distress",
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"abandoned", "neglected", "starving", "malnourished", "chained", "caged"
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]
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normal_dog_behavior = [
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"sleeping", "resting", "lying", "sitting", "playing", "running", "walking",
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"happy", "excited", "alert", "awake", "active", "energetic", "playful"
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abuse = any(keyword in combined_labels for keyword in abuse_keywords)
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death = any(keyword in combined_labels for keyword in death_keywords)
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has_suspicious_behavior = any(keyword in combined_labels for keyword in suspicious_keywords)
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has_normal_behavior = any(keyword in combined_labels for keyword in normal_dog_behavior)
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has_death_indicators = any(keyword in combined_labels for keyword in death_keywords)
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if is_dog:
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suspicious_animal = has_death_indicators or (has_suspicious_behavior and not has_normal_behavior)
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dead_dog = has_death_indicators
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demo = gr.Interface(
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fn=analyze_image,
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inputs=[
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gr.Image(type="pil", label="Upload de Imagem ou Cole Base64"),
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gr.Textbox(label="Ou Cole String Base64 Aqui", lines=3, placeholder="data:image/jpeg;base64,/9j/4AAQ...")
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],
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outputs="json",
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title="Dog / Human Safety Detector",
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description="Envie uma imagem ou cole uma string base64 para análise de moderação"
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)
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if __name__ == "__main__":
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