Update app.py

app.py

@@ -1,102 +1,102 @@
-import gradio as gr
-from keras.models import load_model
-from tensorflow.keras.preprocessing.text import Tokenizer
-from tensorflow.keras.preprocessing.text import tokenizer_from_json
-from tensorflow.keras.preprocessing.sequence import pad_sequences
-from transformers import pipeline
-import torch
-from torchvision import transforms
-from PIL import Image
-import numpy as np
-import json
-import os
-
-
-# Loading the saved tokenizer and model
-with open('tokenizer.json') as f:
-    data = json.load(f)
-    tokenizer = tokenizer_from_json(data)
-
-loaded_model = load_model("BiLSTM_INAPPRO_TEXT_CLASSIFIER.h5")
-
-# Function to classify text
-def classify_text(text):
-    # Tokenize and pad sequences
-    sequence = tokenizer.texts_to_sequences([text])
-    padded_sequence = pad_sequences(sequence, maxlen=128)
-
-    result = loaded_model.predict(padded_sequence)
-    print(result)
-
-    if result[0][0] >= 0.5:
-        label = "Inappropriate"
-    else:
-        label = "Appropriate"
-
-    return [round(result[0][0], 4)*100, label]
-
-
-model = pipeline("image-classification", model="Pratik-hf/Inappropriate-image-classification-using-ViT")
-
-# Function to classify image
-def classify_image(image):
-    print(image)
-    # Forward pass
-    with torch.no_grad():
-        outputs = model(image)
-
-    # Get predicted class probabilities
-    # Get the label with the highest probabilities
-    prediction = max(outputs, key=lambda x: x['score'])
-
-    if prediction['label'] == "LABEL_0":
-        prediction_label = "Safe"
-    else:
-        prediction_label = "Unsafe"
-
-    # Print predicted probabilities for each class
-    print("Predicted probabilities:", prediction)
-
-    return [round(prediction['score'], 4)*100, prediction_label]
-
-
-
-# Define Gradio interface
-def classify_inputs(text=None, image=None):
-    if text is not None:
-        text_result = classify_text(text)
-    if image is not None:
-        image_result = classify_image(image)
-    return text_result, image_result
-
-
-
-
-with gr.Blocks() as demo:
-    with gr.Tab("Text"):
-        gr.Markdown(
-        """
-        # Inappropriate text Detction
-        Give input below to see the output.
-        """)
-        text_input = gr.Textbox(label="Input Text", lines=5)
-        btn1 = gr.Button("Classify Text")
-        with gr.Row():
-            output_text_percentage = gr.Text(label="Percentage")
-            output_text_label = gr.Text(label="Label")
-        btn1.click(fn=classify_text, inputs=text_input, outputs=[output_text_percentage, output_text_label])
-    with gr.Tab("Image"):
-        gr.Markdown(
-        """
-        # Inappropriate Image Detction
-        Give input below to see the output.
-        """)
-        image_input = gr.Image(type="pil")
-        btn2 = gr.Button("Classify Image")
-        with gr.Row():
-            output_image_percentage = gr.Text(label="Percentage")
-            output_image_label = gr.Text(label="Label")
-        btn2.click(fn=classify_image, inputs=image_input, outputs=[output_image_percentage, output_image_label] )
-
-demo.launch()
-
+import gradio as gr
+from keras.models import load_model
+from tensorflow.keras.preprocessing.text import Tokenizer
+from tensorflow.keras.preprocessing.text import tokenizer_from_json
+from tensorflow.keras.preprocessing.sequence import pad_sequences
+from transformers import pipeline
+import torch
+from torchvision import transforms
+from PIL import Image
+import numpy as np
+import json
+import os
+
+
+# Loading the saved tokenizer and model
+with open('tokenizer.json') as f:
+    data = json.load(f)
+    tokenizer = tokenizer_from_json(data)
+
+loaded_model = load_model("BiLSTM_INAPPRO_TEXT_CLASSIFIER.h5")
+
+# Function to classify text
+def classify_text(text):
+    # Tokenize and pad sequences
+    sequence = tokenizer.texts_to_sequences([text])
+    padded_sequence = pad_sequences(sequence, maxlen=128)
+
+    result = loaded_model.predict(padded_sequence)
+    print(result)
+
+    if result[0][0] >= 0.5:
+        label = "Inappropriate"
+    else:
+        label = "Appropriate"
+
+    return [round(result[0][0], 4)*100, label]
+
+
+model = pipeline("image-classification", model="Pratik-hf/Inappropriate-image-classification-using-ViT")
+
+# Function to classify image
+def classify_image(image):
+    print(image)
+    # Forward pass
+    with torch.no_grad():
+        outputs = model(image)
+
+    # Get predicted class probabilities
+    # Get the label with the highest probabilities
+    prediction = max(outputs, key=lambda x: x['score'])
+
+    if prediction['label'] == "LABEL_0":
+        prediction_label = "Safe"
+    else:
+        prediction_label = "Unsafe"
+
+    # Print predicted probabilities for each class
+    print("Predicted probabilities:", prediction)
+
+    return [round(prediction['score'], 4)*100, prediction_label]
+
+
+
+# Define Gradio interface
+def classify_inputs(text=None, image=None):
+    if text is not None:
+        text_result = classify_text(text)
+    if image is not None:
+        image_result = classify_image(image)
+    return text_result, image_result
+
+
+
+
+with gr.Blocks() as demo:
+    with gr.Tab("Text"):
+        gr.Markdown(
+        """
+        # Inappropriate text Detction
+        Give input below to see the output.
+        """)
+        text_input = gr.Textbox(label="Input Text", lines=5)
+        btn1 = gr.Button("Classify Text")
+        with gr.Row():
+            output_text_percentage = gr.Text(label="Percentage")
+            output_text_label = gr.Text(label="Label")
+        btn1.click(fn=classify_text, inputs=text_input, outputs=[output_text_percentage, output_text_label])
+    with gr.Tab("Image"):
+        gr.Markdown(
+        """
+        # Inappropriate Image Detction
+        Give input below to see the output.
+        """)
+        image_input = gr.Image(type="pil")
+        btn2 = gr.Button("Classify Image")
+        with gr.Row():
+            output_image_percentage = gr.Text(label="Percentage")
+            output_image_label = gr.Text(label="Label")
+        btn2.click(fn=classify_image, inputs=image_input, outputs=[output_image_percentage, output_image_label] )
+
+demo.launch(share=True)
+