Create app.py

app.py

@@ -0,0 +1,46 @@
+import os
+
+import gradio as gr
+import requests
+
+API_TOKEN = os.environ['API_TOKEN']
+
+state = []
+
+headers = {'Authorization': f'Bearer {API_TOKEN}'}
+
+def query(payload, model_id):
+    API_URL = f'https://api-inference.huggingface.co/models/{model_id}'
+    response = requests.post(API_URL, headers=headers, json=payload)
+    return response.json()
+
+def chat(message):
+    response = query({
+        'inputs': message,
+        'parameters': {
+            'max_length': 500,
+        }
+    }, 'IssakaAI/health-chatbot')
+    reply = ''
+    if isinstance(response, list):
+        reply = response[0]['generated_text'][len(message) + 1:]
+    elif isinstance(response, dict):
+        reply = f'{response["error"]}. Please wait about {int(response["estimated_time"])} seconds.'
+    state.append((message, reply))
+    return gr.Textbox.update(value=''), state
+
+def clear_message():
+    state.clear()
+    return gr.Chatbot.update(value=[])
+
+with gr.Blocks() as blk:
+    gr.Markdown('# Interact with IssakaAI NLP models')
+    with gr.Row():
+        chatbot = gr.Chatbot()
+        with gr.Box():
+            message = gr.Textbox(value='What is the menstrual cycle?', lines=10)
+            send = gr.Button('Send', variant='primary')
+            clear = gr.Button('Clear history', variant='secondary')
+    send.click(fn=chat, inputs=message, outputs=[message, chatbot])
+    clear.click(fn=clear_message, inputs=[], outputs=chatbot)
+blk.launch(debug=True)