Update app.py

app.py

@@ -1,34 +1,101 @@
 import gradio as gr
-from transformers import pipeline
+from transformers import pipeline, AutoTokenizer
+
+# ==============================
+# 🔧 Load model and tokenizer
+# ==============================
+MODEL_NAME = "hazarri/fine_tuned_spanbert"
 
-# Load your fine-tuned SpanBERT ADR model
 ner_pipeline = pipeline(
     "token-classification",
-    model="hazarri/fine_tuned_spanbert",  # replace with your actual model name
+    model=MODEL_NAME,
     aggregation_strategy="simple"
 )
 
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+
+# ==============================
+# 🧠 1️⃣ ADR Extraction Function
+# ==============================
 def extract_adrs(text):
-    # Run the model
     results = ner_pipeline(text)
-
-    # Return empty list if no entities detected
     if not results:
         return []
+    return [r["word"] for r in results]
 
-    # Extract only the ADR words
-    adrs = [r["word"] for r in results]
+# ==============================
+# ✂️ 2️⃣ Tokenizer Function
+# ==============================
+def tokenize_text(text):
+    tokens = tokenizer.tokenize(text)
+    return tokens
 
-    return adrs
-
-# Gradio interface
-demo = gr.Interface(
+# ==============================
+# 🎨 Gradio Interfaces
+# ==============================
+adr_interface = gr.Interface(
     fn=extract_adrs,
-    inputs=gr.Textbox(lines=5, placeholder="Enter a medical review..."),
+    inputs=gr.Textbox(lines=4, placeholder="Enter a medical review..."),
     outputs=gr.JSON(label="Extracted ADRs"),
-    title="💊 SpanBERT ADR Extraction API",
-    description="Extracts adverse drug reactions (ADRs) from patient reviews using a fine-tuned SpanBERT model."
+    title="💊 ADR Extraction (SpanBERT)",
+    description="Extracts adverse drug reactions (ADRs) from text using a fine-tuned SpanBERT model.",
+    api_name="/predict"  # ✅ for remote calls via gradio_client
 )
 
+tokenizer_interface = gr.Interface(
+    fn=tokenize_text,
+    inputs=gr.Textbox(lines=3, placeholder="Enter text to tokenize..."),
+    outputs=gr.JSON(label="Tokens"),
+    title="🔤 Tokenizer",
+    description="Displays tokens produced by the model's tokenizer.",
+    api_name="/tokenize"  # ✅ second endpoint
+)
+
+# Combine both tools
+demo = gr.TabbedInterface(
+    [adr_interface, tokenizer_interface],
+    ["ADR Extraction", "Tokenizer"]
+)
+
+# ==============================
+# 🚀 Launch the app
+# ==============================
 if __name__ == "__main__":
     demo.launch()
+
+
+
+# import gradio as gr
+# from transformers import pipeline
+
+# # Load your fine-tuned SpanBERT ADR model
+# ner_pipeline = pipeline(
+#     "token-classification",
+#     model="hazarri/fine_tuned_spanbert",  # replace with your actual model name
+#     aggregation_strategy="simple"
+# )
+
+# def extract_adrs(text):
+#     # Run the model
+#     results = ner_pipeline(text)
+
+#     # Return empty list if no entities detected
+#     if not results:
+#         return []
+
+#     # Extract only the ADR words
+#     adrs = [r["word"] for r in results]
+
+#     return adrs
+
+# # Gradio interface
+# demo = gr.Interface(
+#     fn=extract_adrs,
+#     inputs=gr.Textbox(lines=5, placeholder="Enter a medical review..."),
+#     outputs=gr.JSON(label="Extracted ADRs"),
+#     title="💊 SpanBERT ADR Extraction API",
+#     description="Extracts adverse drug reactions (ADRs) from patient reviews using a fine-tuned SpanBERT model."
+# )
+
+# if __name__ == "__main__":
+#     demo.launch()