- Add Gradio-based chat interface in `app.py` for persona-based chat assistant.

- Update `config.py` to include new configuration options for Ollama and API keys.
- Add `requirements.txt` to specify necessary Python packages.
- Enhance `utils.py` with detailed docstrings and improved function structure.

app.py

@@ -1,2 +1,26 @@
-# This is the file for the gradio app
+import gradio as gr
+from prompts import PERSONAS
+from utils import generate_response
+from config import DEFAULT_PERSONA
 
+
+def chat_fn(persona, user_input, history):
+    return generate_response(PERSONAS[persona], user_input, history)
+
+
+with gr.Blocks() as app:
+    gr.Markdown("## 🤖 LLMates: Persona-based Chat Assistant")
+
+    persona = gr.Dropdown(
+        choices=list(PERSONAS.keys()), value=DEFAULT_PERSONA, label="Choose Persona"
+    )
+    chatbox = gr.Chatbot(label="LLMates")
+    msg = gr.Textbox(label="Type your message...")
+    state = gr.State([])
+
+    def user_submit(user_input, history):
+        return chat_fn(persona.value, user_input, history)
+
+    msg.submit(user_submit, [msg, state], [chatbox, state])
+
+app.launch()

config.py

@@ -1,13 +1,15 @@
 import os
 from dotenv import load_dotenv
 
-load_dotenv()  # load .env variables
+load_dotenv()
 
-# ✅ LLM model config
-MODEL_NAME = os.getenv("OPENAI_MODEL", "gpt-4o")
+# Model and key
 OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
+OPENAI_MODEL = os.getenv("OPENAI_MODEL", "gpt-4o")
+USE_OLLAMA = os.getenv("USE_OLLAMA", "false").lower() == "true"
+OLLAMA_MODEL = os.getenv("OLLAMA_MODEL", "llama3")
 
-# ✅ System-wide constants
+# UI + LLM behavior config
 DEFAULT_PERSONA = "Python Tutor"
-MAX_HISTORY_TURNS = 10  # truncate if needed
 TEMPERATURE = 0.7
+MAX_TURNS = 10

requirements.txt

@@ -0,0 +1,4 @@
+openai
+gradio
+python-dotenv
+requests

utils.py

@@ -1,31 +1,60 @@
 from config import OPENAI_API_KEY, OPENAI_MODEL, USE_OLLAMA, OLLAMA_MODEL
 import requests
 import openai
-import os
 
 openai.api_key = OPENAI_API_KEY
 
+
 def query_openai(messages):
+    """Query the OpenAI API with the given messages.
+
+    Args:
+        messages (list): A list of message dictionaries, where each dictionary contains
+                       'role' and 'content' keys representing the conversation history.
+
+    Returns:
+        str: The assistant's response as a string, or an error message if the API call fails.
+    """
     try:
-        response = openai.ChatCompletion.create(
-            model=OPENAI_MODEL,
-            messages=messages
-        )
+        response = openai.ChatCompletion.create(model=OPENAI_MODEL, messages=messages)
         return response["choices"][0]["message"]["content"]
     except Exception as e:
         return f"⚠️ OpenAI Error: {e}"
 
+
 def query_ollama(prompt):
+    """Query a local Ollama instance with the given prompt.
+
+    Args:
+        prompt (str): The input prompt to send to the Ollama model.
+
+    Returns:
+        str: The model's response as a string, or an error message if the API call fails.
+    """
     try:
         res = requests.post(
             "http://localhost:11434/api/generate",
-            json={"model": OLLAMA_MODEL, "prompt": prompt}
+            json={"model": OLLAMA_MODEL, "prompt": prompt},
         )
         return res.json()["response"]
     except Exception as e:
         return f"⚠️ Ollama Error: {e}"
 
+
 def generate_response(persona, user_input, history):
+    """Generate a response using either OpenAI or Ollama based on configuration.
+
+    Args:
+        persona (str): The system prompt or persona that defines the assistant's behavior.
+        user_input (str): The latest user input message.
+        history (list): A list of tuples representing the conversation history,
+                      where each tuple is (user_message, bot_response).
+
+    Returns:
+        tuple: A tuple containing:
+            - Updated conversation history including the new exchange
+            - The same history (for compatibility with some interfaces)
+    """
     if USE_OLLAMA:
         full_prompt = f"{persona}\n\n"
         for u, b in history:
@@ -41,4 +70,4 @@ def generate_response(persona, user_input, history):
         reply = query_openai(messages)
 
     history.append((user_input, reply))
-    return history, history
+    return history, history