Upload 2 files

app.py

@@ -0,0 +1,70 @@
+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')    #path + '/BinaryTokenizer_ep5'
+bert_model = TFBertForSequenceClassification.from_pretrained('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 results to API: {response.status_code}"}
+
+
+def predict_text(params):
+    try:
+        params = json.loads(params)
+    except Exception as e:
+        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": f"Invalid JSON input {e.msg} at line {e.lineno} column {e.colno}"}
+    
+    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})
+
+
+inputt = gr.Textbox(label="Parameter in JSON format (e.g., {'texts': ['sample text', 'sample text2']'})")
+outputt = gr.JSON()
+
+application = gr.Interface(fn=predict_text, inputs=inputt, outputs=outputt, title="Text Classification with BERT and API Integration")
+application.launch()

requirements.txt

@@ -0,0 +1,3 @@
+tensorflow
+transformers
+tf-keras