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app.py

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+import gradio as gr
+import tensorflow as tf
+from transformers import AutoTokenizer, TFAutoModelForTokenClassification
+
+
+model_name = "krishnapal2308/NER-Task3"
+tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
+model = TFAutoModelForTokenClassification.from_pretrained(model_name)
+
+
+id2label = {
+    0: "O",
+    1: "B-treatment", 2: "I-treatment",
+    3: "B-chronic_disease", 4: "I-chronic_disease",
+    5: "B-cancer", 6: "I-cancer",
+    7: "B-allergy_name", 8: "I-allergy_name"
+}
+
+def predict(text):
+    inputs = tokenizer(text, return_tensors="tf", truncation=True, padding=True)
+    outputs = model(inputs)
+    predictions = tf.argmax(outputs.logits, axis=-1)
+    
+    tokens = tokenizer.convert_ids_to_tokens(inputs["input_ids"][0])
+    labels = [id2label[pred.numpy()] for pred in predictions[0]]
+
+    # Remove special tokens and group B- and I- tags
+    result = []
+    current_word = ""
+    current_label = None
+
+    for token, label in zip(tokens, labels):
+        if token in ["[CLS]", "[SEP]", "[PAD]"]:
+            continue  
+        
+        if token.startswith("##"):  
+            current_word += token[2:]  # Append without '##'
+        else:
+            if current_word:  # Save the previous word before starting a new one
+                result.append((current_word, current_label))
+            current_word = token
+            current_label = label[2:] if label.startswith("B-") else label[2:] if label.startswith("I-") and current_label == label[2:] else None
+
+    if current_word:  # Add the last word
+        result.append((current_word, current_label))
+    
+    final_result = []
+    to_skip = []
+    # Combining words with same labels
+    for ind, word_label in enumerate(result):
+        print(ind, word_label)
+        if ind not in to_skip:
+            if word_label[1]:
+                combined_word = word_label[0]
+                for next_ind, next_word_label in enumerate(result[ind+1:]):
+                    if word_label[1] == next_word_label[1]:
+                        to_skip.append(ind+next_ind+1)
+                        combined_word += ' '+next_word_label[0]
+                final_result.append((combined_word, word_label[1]))
+            else:
+                final_result.append((word_label[0], word_label[1]))
+
+    final_result = [(word, 'allergy') if label == 'allergy_name' else (word, label) for word, label in final_result]
+
+    return final_result
+
+def ner_function(text):
+    result = predict(text)
+    return result
+
+examples = [
+    ["The patient was diagnosed with stage 2 breast cancer and treated with tamoxifen."],
+    ["He has a history of type 2 diabetes and is allergic to penicillin."]
+]
+
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=ner_function,
+    inputs=gr.Textbox(lines=5, label="Input Text"),
+    outputs=gr.HighlightedText(label="Text with Entities"),
+    title="Clinical Trial Named Entity Recognition",
+    description="""
+This interface presents a Named Entity Recognition (NER) system specifically designed for analyzing clinical trial data.
+
+Leveraging a fine-tuned BERT-based model, the system is capable of identifying and classifying key medical entities such as treatments, chronic diseases, cancers, and allergies.
+
+Explore the provided examples to observe the model's capabilities in action.
+""",
+    examples=examples,
+    cache_examples=True,  
+    allow_flagging="never",  
+    theme="default"
+)
+
+# Launch the interface
+iface.launch()

requirements.txt

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+gradio
+tensorflow
+transformers
+numpy