Create app.py

app.py

@@ -0,0 +1,62 @@
+import tensorflow as tf
+from transformers import BertTokenizer, TFBertForSequenceClassification
+import numpy as np
+import json
+import requests
+import gradio as gr
+
+
+bert_tokenizer = BertTokenizer.from_pretrained('BinaryTokenizer_ep5')
+bert_model = TFBertForSequenceClassification.from_pretained('BinaryModel_ep5')
+
+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 process_audio(params):
+    params = json.loads(params)
+
+    texts = params.get("texts",[])
+    api = params.get("api", "")
+    job_id = params.get("job_id","")
+
+    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 = {1:'positive', 0:'negative'}
+        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}, indent=4)
+
+
+inputt = gr.Textbox(label="Parameters in Json Format...")
+outputt = gr.JSON()
+
+application = gr.Interface(fn = process_audio(), inputs = inputt, outputs = outputt, title='Multi Text Classification with API Integration..')
+application.launch()