Update app.py

app.py

@@ -27,7 +27,7 @@ DATA_FILENAME = "bert_base_ner_logs.csv"
 DATA_FILE = os.path.join("bert_base_ner_logs", DATA_FILENAME)
 DATASET_REPO_ID = "pragnakalp/bert_base_ner"
 print("is none?", HF_TOKEN is None)
-
+input_value = "The U.S. President Donald Trump came to visit Ahmedabad first time at Motera Stadium with our Prime Minister Narendra Modi in February 2020"
 try:
     hf_hub_download(
         repo_id=DATASET_REPO_ID,
@@ -129,16 +129,34 @@ def save_data_and_sendmail(article,output):
     except Exception as e:
         return "Error while sending mail" + str(e)
         
+with gr.Blocks(css = ".gradio-container {background-color: lightgray}") as demo:
+    gr.Markdown("<h1 style='text-align: center; margin-bottom: 1rem'>"
+                + "Named Entity Recognition Using BERT"
+                + "</h1>")
+    with gr.Row():   [Neon Coqui TTS Plugin](https://github.com/NeonGeckoCom/neon-tts-plugin-coqui)
+        with gr.Column():
+            input=gr.Textbox(lines=5, value=input_value, label="Input Text", elem_id="inp_div")
+            sub_btn = gr.Button("Submit")
+        output = [gr.Dataframe(row_count = (3, "dynamic"), col_count=(2, "fixed"), label="Here is the Result", headers=["Entities Found","Entity Types"])]
+    gr.Markdown(
+                """
+                Feel free to give us your <a href="https://www.pragnakalp.com/contact/"> feedback </a> on this NER demo. 
+                For all your Named Entity Recognition related requirements, we are here to help you.<br /> 
+                Email us your requirement at <a href="mailto:[email protected]"> [email protected] </a> . 
+                And don't forget to check out more interesting <a href="https://www.pragnakalp.com/services/natural-language-processing-services/">NLP services</a> we are offering.</ br>
+                <p style='text-align: center;><b>Developed by</b> : <a href="https://www.pragnakalp.com" target="_blank">Pragnakalp Techlabs </a>
+                
+                """)
+                
+    event = sub_btn.click(generate_emotion, inputs=input, outputs=output)
+demo.launch()
 
-inputs=gr.Textbox(lines=5, label="Sentences",elem_id="inp_div")
-outputs = [gr.Dataframe(row_count = (2, "dynamic"), col_count=(2, "fixed"), label="Here is the Result", headers=["Entities Found","Entity Types"])]
-
-demo = gr.Interface(
-    generate_emotion,
-    inputs,
-    outputs,
-    title="Entity Recognition For Input Text",
-    description="Feel free to give your feedback", 
-    css=".gradio-container {background-color: lightgray} #inp_div {background-color: [#7](https://www1.example.com/issues/7)FB3D5;"
-)
-demo.launch()
+# demo = gr.Interface(
+#     generate_emotion,
+#     inputs,
+#     outputs,
+#     title="Entity Recognition For Input Text",
+#     description="Feel free to give your feedback", 
+#     css=".gradio-container {background-color: lightgray} #inp_div {background-color: [#7](https://www1.example.com/issues/7)FB3D5;"
+# )
+# demo.launch()