Add a separate interface for tuned model

README.md

@@ -42,7 +42,7 @@ Otherwise, you can try the setup script:
 bash setup.sh
 ```
 
-It attempts to install with [uv](https://docs.astral.sh/uv/) (a fast, Rust-based Python package and project manager) using `.python-version` file and `pyproject.toml` file. This is the recommended way to manage the project, since its resolver is faster and more reliable than `pip`.
+It attempts to install with [uv](https://docs.astral.sh/uv/) (a fast, Rust-based Python package and project manager) using `pyproject.toml` file. This is the recommended way to manage the project, since its dependency resolver is faster and more reliable than `pip`.
 
 Otherwise, it falls back to `pip` installation.
 

src/app/app2.py

@@ -0,0 +1,70 @@
+"""Gradio interface for generating mnemonics from instructions.
+
+TODO: Combine this interface with the chatbot interface in app.py.
+"""
+
+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "chiffonng/gemma2-9b-it-mnemonics"
+
+# Load model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+
+def generate_text(instruction: str) -> str:
+    """Generate mnemonic from user input/instruction.
+
+    Args:
+        instruction (str): User instructions to generate mnemonic.
+
+    Returns:
+        str: Generated mnemonic text.
+    """
+    inputs = tokenizer.encode(instruction, return_tensors="pt")
+    outputs = model.generate(inputs, max_length=256)
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+
+# Create simple Gradio interface
+demo = gr.Interface(
+    fn=generate_text,
+    inputs=gr.Textbox(label="Instruction"),
+    outputs=gr.Textbox(label="Output"),
+    title="Mnemonic Generation",
+    description="Enter an instruction to generate mnemonic text.",
+)
+
+
+def chatbot_response(message: str, history: list) -> list:
+    """Generates a response from the chatbot based on the input message and updates the conversation history.
+
+    Args:
+        message (str): The input message from the user.
+        history (list): The conversation history, a list of tuples where each tuple contains a user message and a chatbot response.
+
+    Returns:
+        list: The updated conversation history with the new message and response appended.
+    """
+    inputs = tokenizer.encode(message, return_tensors="pt")
+    outputs = model.generate(inputs, max_length=100)
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    history.append((message, response))
+    return history
+
+
+# Create Gradio ChatInterface
+chatbot = gr.ChatInterface(
+    fn=chatbot_response,
+    title="Mnemonic Generation Chatbot",
+    description="Chat with the model to generate mnemonics.",
+    retry_btn=True,
+    undo_btn=True,
+    clear_btn=True,
+)
+
+
+# Launch the interface
+demo.launch()
+# chatbot.launch()