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app.py

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+import tensorflow as tf
+from transformers import BertTokenizer, TFBertForSequenceClassification
+import numpy as np
+import json
+import requests
+import gradio as gr
+import logging
+
+bert_tokenizer = BertTokenizer.from_pretrained('MultiTokenizer_ep10')
+bert_model = TFBertForSequenceClassification.from_pretrained('MultiModel_ep10')
+
+# def send_results_to_api(data, result_url):
+#     headers = {'Content-Type':'application/json'}
+#     response = requests.post(result_url, json = data, headers=headers)
+
+#     if response.status_code == 200:
+#         return response.json
+#     else:
+#         return {'error':f"failed to send result to API: {response.status_code}"}
+
+def predict_text(params):
+    try:
+        params = json.loads(params)
+    except JSONDecodeError as e:
+        logging.error(f"Invalid JSON input: {e.msg} at line {e.lineno} column {e.colno}")
+        return {"error": f"Invalid JSON input: {e.msg} at line {e.lineno} column {e.colno}"}
+        
+    texts = params.get("texts",[])
+    # api = params.get("api", "")
+    # job_id = params.get("job_id","")
+
+    if not texts:
+        return {"error": "Missing required parameters: 'urls'"}
+
+    solutions = []
+
+    for text in texts:
+        encoding = bert_tokenizer.encode_plus(
+                                            text,
+                                            add_special_tokens=True,
+                                            max_length=128,
+                                            return_token_type_ids=True,
+                                            padding = 'max_length',
+                                            truncation=True,
+                                            return_attention_mask=True,
+                                            return_tensors='tf'
+                                            )
+        input_ids = encoding['input_ids']
+        token_type_ids = encoding['token_type_ids']
+        attention_mask = encoding['attention_mask']
+
+        pred = bert_model.predict([input_ids, token_type_ids, attention_mask])
+        logits = pred.logits
+        pred_label = tf.argmax(logits, axis=1).numpy()[0]
+        
+        label = {0: 'BUSINESS', 1: 'COMEDY', 2: 'CRIME', 3: 'FOOD & DRINK', 4: 'POLITICS', 5: 'SPORTS', 6: 'TRAVEL'}
+        result = {'text':text, 'label':[label[pred_label]]}
+        solutions.append(result)
+
+    # result_url = f"{api}/{job_id}"
+    # send_results_to_api(solutions, result_url)
+    return json.dumps({"solutions":solutions})
+
+
+inputt = gr.Textbox(label="Parameters in Json Format... Eg. {'texts':['text1', 'text2']")
+outputt = gr.JSON()
+
+application = gr.Interface(fn = predict_text, inputs = inputt, outputs = outputt, title='Multi Text Classification with API Integration..')
+application.launch()

requirements.txt

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+tensorflow
+tf-keras
+transformers