Update app.py

app.py

@@ -3,21 +3,21 @@ os.environ['TF_ENABLE_ONEDNN_OPTS'] = '0'
 
 import tensorflow as tf
 import gradio as gr
-import pandas as pd
 import pickle
 from keras.utils import pad_sequences
+import json
+from gradio_client import Client
 
+# Load tokenizer and model
 max_len = 200
-
-# Load the tokenizer
 with open('tokenizer.pickle', 'rb') as handle:
     tokenizer = pickle.load(handle)
 
-# Load the pre-trained model
 model = tf.keras.models.load_model('toxic.h5')
 
 arr = ["toxic", "severe_toxic", "obscene", "threat", "insult", "identity_hate"]
 
+# Define the function to score comments
 def score_comment(comment):
     sequences = tokenizer.texts_to_sequences([comment])
     inp = pad_sequences(sequences, maxlen=max_len)
@@ -27,11 +27,30 @@ def score_comment(comment):
         text += '{}: {}\n'.format(arr[i], results[0][i] > 0.5)
     return text
 
-# Update the Gradio interface to use the latest syntax
-inputs = gr.Textbox(lines=2, placeholder='Comment to score')
+# Define API function
+def predict_api(comment):
+    sequences = tokenizer.texts_to_sequences([comment])
+    inp = pad_sequences(sequences, maxlen=max_len)
+    results = model.predict(inp)
+    response = {arr[i]: results[0][i] > 0.5 for i in range(len(arr))}
+    return response
+
+# Gradio Interface setup
+inputs = gr.Textbox(lines=2, placeholder='Enter comment here...')
 outputs = gr.Textbox()
 
+# Create Gradio interface
 interface = gr.Interface(fn=score_comment, inputs=inputs, outputs=outputs)
 
-# Launch the interface
+# API endpoint function (exposing the model via Gradio API)
+def api_route(request):
+    if request.method == 'POST':
+        data = request.get_json()
+        comment = data.get('comment')
+        if not comment:
+            return json.dumps({"error": "Missing 'comment' in request"}), 400
+        prediction = predict_api(comment)
+        return json.dumps({"prediction": prediction})
+
+# Launch the Gradio interface for the space (auto-shared link)
 interface.launch(share=True)