adding chatbot

app.py

@@ -0,0 +1,130 @@
+import gradio as gr
+import openai
+import examples as chatbot_examples
+from dotenv import load_dotenv
+import os
+
+load_dotenv()  # take environment variables from .env.
+
+# In order to authenticate, secrets must have been set, and the user supplied credentials match
+def auth(username, password):
+    app_username = os.getenv("APP_USERNAME")
+    app_password = os.getenv("APP_PASSWORD")
+
+    if app_username and app_password:
+        if(username == app_username and password == app_password):
+            print("Logged in successfully.")
+            return True
+        else:
+            print("Username or password does not match.")
+    else:
+        print("Credential secrets not set.")
+    return False
+    
+# Define a function to get the AI's reply using the OpenAI API
+def get_ai_reply(message, model="gpt-3.5-turbo", system_message=None, temperature=0, message_history=[]):
+    # Initialize the messages list
+    messages = []
+    
+    # Add the system message to the messages list
+    if system_message is not None:
+        messages += [{"role": "system", "content": system_message}]
+
+    # Add the message history to the messages list
+    if message_history is not None:
+        messages += message_history
+    
+    # Add the user's message to the messages list
+    messages += [{"role": "user", "content": message}]
+    
+    # Make an API call to the OpenAI ChatCompletion endpoint with the model and messages
+    completion = openai.ChatCompletion.create(
+        model=model,
+        messages=messages,
+        temperature=temperature
+    )
+    
+    # Extract and return the AI's response from the API response
+    return completion.choices[0].message.content.strip()
+
+# Define a function to handle the chat interaction with the AI model
+def chat(model, system_message, message, chatbot_messages, history_state):
+    # Initialize chatbot_messages and history_state if they are not provided
+    chatbot_messages = chatbot_messages or []
+    history_state = history_state or []
+    
+    # Try to get the AI's reply using the get_ai_reply function
+    try:
+        ai_reply = get_ai_reply(message, model=model, system_message=system_message, message_history=history_state)
+            
+        # Append the user's message and the AI's reply to the chatbot_messages list
+        chatbot_messages.append((message, ai_reply))
+
+        # Append the user's message and the AI's reply to the history_state list
+        history_state.append({"role": "user", "content": message})
+        history_state.append({"role": "assistant", "content": ai_reply})
+
+        # Return None (empty out the user's message textbox), the updated chatbot_messages, and the updated history_state
+    except Exception as e:
+        # If an error occurs, raise a Gradio error
+        raise gr.Error(e)
+        
+    return None, chatbot_messages, history_state
+
+# Define a function to launch the chatbot interface using Gradio
+def get_chatbot_app(additional_examples=[]):
+    # Load chatbot examples and merge with any additional examples provided
+    examples = chatbot_examples.load_examples(additional=additional_examples)
+    
+    # Define a function to get the names of the examples
+    def get_examples():
+        return [example["name"] for example in examples]
+
+    # Define a function to choose an example based on the index
+    def choose_example(index):
+        if(index!=None):
+            system_message = examples[index]["system_message"].strip()
+            user_message = examples[index]["message"].strip()
+            return system_message, user_message, [], []
+        else:
+            return "", "", [], []
+
+    # Create the Gradio interface using the Blocks layout
+    with gr.Blocks() as app:
+        with gr.Tab("Conversation"):
+            with gr.Row():
+                with gr.Column():
+                    # Create a dropdown to select examples
+                    example_dropdown = gr.Dropdown(get_examples(), label="Examples", type="index")
+                    # Create a button to load the selected example
+                    example_load_btn = gr.Button(value="Load")
+                    # Create a textbox for the system message (prompt)
+                    system_message = gr.Textbox(label="System Message (Prompt)", value="You are a helpful assistant.")
+                with gr.Column():
+                    # Create a dropdown to select the AI model
+                    model_selector = gr.Dropdown(
+                        ["gpt-3.5-turbo"],
+                        label="Model",
+                        value="gpt-3.5-turbo"
+                    )
+                    # Create a chatbot interface for the conversation
+                    chatbot = gr.Chatbot(label="Conversation")
+                    # Create a textbox for the user's message
+                    message = gr.Textbox(label="Message")
+                    # Create a state object to store the conversation history
+                    history_state = gr.State()
+                    # Create a button to send the user's message
+                    btn = gr.Button(value="Send")
+
+                # Connect the example load button to the choose_example function
+                example_load_btn.click(choose_example, inputs=[example_dropdown], outputs=[system_message, message, chatbot, history_state])
+                # Connect the send button to the chat function
+                btn.click(chat, inputs=[model_selector, system_message, message, chatbot, history_state], outputs=[message, chatbot, history_state])
+        # Return the app
+        return app
+        
+# Call the launch_chatbot function to start the chatbot interface using Gradio
+# Set the share parameter to False, meaning the interface will not be publicly accessible
+app = get_chatbot_app()
+app.queue()  # this is to be able to queue multiple requests at once
+app.launch(auth=auth)

requirements.txt

@@ -0,0 +1,3 @@
+gradio == 3.27.0
+openai == 0.27.4
+python-dotenv == 1.0.0