refactored login details to environment variables

app.py

@@ -1,19 +1,15 @@
-# import gradio as gr
-#
-# def greet(name):
-#     return "Hello ...." + name + "!!"
-#
-# iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-# iface.launch()
-
-import gradio as gr
-
+import os
+from dotenv import load_dotenv
 from test_bot.Gradio_Test import Gradio_Test
 
+load_dotenv()
 gradio_test = Gradio_Test()
-
 demo = gradio_test.create_demo()
 
+username = os.getenv('HF_USERNAME')
+password = os.getenv('HF_PASSWORD')
+
 if __name__ == "__main__":
+    demo.launch(auth=(username, password))
     #demo.launch()
-    demo.launch(auth=("admin", "pass1234"))
+    #demo.launch(auth=("admin", "pass1234"))

test_bot/Gradio_Test.py

@@ -9,54 +9,20 @@ from test_bot.api.Open_API import Open_API
 Open_API().setup()
 
 
+TITLE = "# Meet Bobby Tables (head of Application Security). v0.2.0"
 
 class Gradio_Test:
 
 
     def __init__(self):
+        #self.demo = None
         pass
 
     def title(self):
-        return "# Meet Bobby Tables (head of Application Security)"
+        return TITLE
 
 
-
-    # def predict(self, message, history):
-    #     print('--'*50)
-    #     print("Message:", message)
-    #     print("History:", history)
-    #     print('--' * 50)
-    #     history_openai_format = []
-    #     history_openai_format.append(self.default_prompt())
-    #     for human, assistant in history:
-    #         history_openai_format.append({"role": "user", "content": human})
-    #         history_openai_format.append({"role": "assistant", "content": assistant})
-    #     history_openai_format.append({"role": "user", "content": message})
-    #
-    #     #pprint(history_openai_format)
-    #     response = openai.ChatCompletion.create(
-    #         model='gpt-3.5-turbo',
-    #         messages=history_openai_format,
-    #         temperature=1.0,
-    #         stream=True
-    #     )
-    #
-    #     #token_count = list_set(response)
-    #     #print("Number of tokens used:", token_count)
-    #
-    #     partial_message = ""
-    #     for chunk in response:
-    #         if len(chunk['choices'][0]['delta']) != 0:
-    #             next_content = chunk['choices'][0]['delta']['content']
-    #             partial_message = partial_message + next_content
-    #             yield  next_content
-    #             yield partial_message
-
     def create_demo(self):
-        # def predict(message, history):
-        #     open_api = Open_API().setup()
-        #     return open_api.create()
-
         #return gr.ChatInterface(self.predict).queue()
         default_text = "Hi, good morning"
         chat_predict = Chat_Predict()
@@ -66,19 +32,9 @@ class Gradio_Test:
             gr.ChatInterface(chat_predict.predict, textbox=textbox_input)
 
         demo.queue()
+        #self.demo = demo
         return demo
-        #return
-
-    def create_demo__2(self):
-        title = self.title()
-
-        with gr.Blocks() as demo:
-            gr.Markdown(title)
-            inp = gr.Textbox(placeholder="What is your name?")
-            out = gr.Textbox()
-
-            inp.change(fn=lambda x: f"Welcome .... , {x}!",
-                       inputs=inp,
-                       outputs=out)
 
-        return demo
+    # def launch(self):
+    #     self.demo.queue()
+    #     #self.demo.launch()