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from transformers import pipeline
from PIL import Image
import gradio as gr
# Sentiment / text classification
text_classifier = pipeline(
"text-classification",
model="distilbert-base-uncased-finetuned-sst-2-english"
)
# Fake news detection
fake_detector = pipeline(
"text-classification",
model="mrm8488/bert-tiny-finetuned-fake-news-detection" # public
)
image_classifier = pipeline(
"image-classification",
model="microsoft/resnet-50" # public
)
leaf_classifier = pipeline(
"image-classification",
model="linkanjarad/mobilenet_v2_1.0_224-plant-disease-identification" # public
)
fruitveg_classifier = pipeline(
"image-classification",
model="Schram03/fruits-classification" # public
)
def classify_text(text):
result = text_classifier(text)[0]
return {result['label']: float(result['score'])}
def detect_fake(text):
result = fake_detector(text)[0]
return {result['label']: float(result['score'])}
def classify_image(image):
result = image_classifier(image)[0]
return {result['label']: float(result['score'])}
def detect_leaf_disease(image):
result = leaf_classifier(image)[0]
return {result['label']: float(result['score'])}
def detect_fruitveg(image):
result = fruitveg_classifier(image)[0]
return {result['label']: float(result['score'])}
with gr.Blocks() as demo:
with gr.Tab("Text Classification"):
gr.Interface(fn=classify_text, inputs="text", outputs="label")
with gr.Tab("Fake News Detection"):
gr.Interface(fn=detect_fake, inputs="text", outputs="label")
with gr.Tab("General Image Classification"):
gr.Interface(fn=classify_image, inputs=gr.Image(type="pil"), outputs="label")
with gr.Tab("Leaf Disease Detection"):
gr.Interface(fn=detect_leaf_disease, inputs=gr.Image(type="pil"), outputs="label")
with gr.Tab("Fruit/Veg Detection"):
gr.Interface(fn=detect_fruitveg, inputs=gr.Image(type="pil"), outputs="label")
# def my_function(text):
# return "You entered: " + text
# demo = gr.Interface(fn=my_function, inputs="text", outputs="text")
# demo = gr.Interface(fn=classify_image, inputs=gr.Image(type="pil"), outputs="label")
demo.launch()
def classify_and_detect(text):
t_result = classify_text(text)
f_result = detect_fake(text)
return t_result, f_result
gr.Interface(fn=classify_and_detect, inputs="text", outputs=["label", "label"])
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