Update app.py

app.py

@@ -1,14 +1,14 @@
 # Import the required libraries
-import os
-import requests
-import json
+import os  # Provides a way of using operating system-dependent functionality
+import requests  # For making HTTP requests to the API
+import json  # For handling JSON data
 from dotenv import load_dotenv, find_dotenv
 import gradio as gr
 
 # Load environment variables from .env file
 load_dotenv(find_dotenv())
-hf_api_key = os.getenv('HF_API_KEY')
-API_URL = os.getenv('HF_API_NER_BASE')
+hf_api_key = os.getenv('HF_API_KEY')  # Hugging Face API key
+API_URL = os.getenv('HF_API_NER_BASE')  # Endpoint for the NER model
 
 # Define the `get_completion` function to interact with the Hugging Face API
 def get_completion(inputs, parameters=None, endpoint_url=None):
@@ -22,43 +22,28 @@ def get_completion(inputs, parameters=None, endpoint_url=None):
     try:
         response = requests.post(endpoint_url, headers=headers, data=json.dumps(data))
         response.raise_for_status()
-        return response.json()
+        return response.json()  # Return the API's JSON response
     except requests.exceptions.RequestException as e:
         print(f"Error: {e}")
-        return []
+        return [{"entity": "Error", "word": "Error", "score": 0}]
 
 # Function to perform Named Entity Recognition (NER)
-def ner(input_text):
-    # Call the Hugging Face API
-    output = get_completion(input_text, parameters=None, endpoint_url=API_URL)
-
-    # Check if the API response is valid
-    if not output or not isinstance(output, list):
-        return {"text": input_text, "entities": []}
-
-    # Format the output for HighlightedText
-    entities = [
-        (entity["start"], entity["end"], entity["entity_group"])
-        for entity in output if "start" in entity and "end" in entity and "entity_group" in entity
-    ]
-    return {"text": input_text, "entities": entities}
+def ner(input):
+    output = get_completion(input, parameters=None, endpoint_url=API_URL)
+    return {"text": input, "entities": output}
 
 # Create a Gradio interface
 iface = gr.Interface(
     fn=ner,
-    inputs=gr.Textbox(label="Text to find entities", lines=2),
-    outputs=gr.HighlightedText(label="Text with entities"),
+    inputs=[gr.Textbox(label="Text to find entities", lines=2)],
+    outputs=[gr.HighlightedText(label="Text with entities")],
     title="NER with dslim/bert-base-NER",
     description="Find entities using the `dslim/bert-base-NER` model under the hood!",
     allow_flagging="never",
-    examples=[
-        "My name is Michela and I live in Italy.",
-        "My name is Andrew and I work at HuggingFace.",
-        "Barack Obama was the president of the United States."
-    ]
+    examples=["My name is Michela and I live in Italy", "My name is Andrew and work at HuggingFace"]
 )
 
-# Launch the app
+# Launch the app (this will allow you to test locally before uploading to Hugging Face)
 iface.launch()
 
 
@@ -66,3 +51,4 @@ iface.launch()
 
 
 
+