Update app.py

app.py

@@ -1,82 +1,261 @@
+# Importing required libraries
+import warnings
+warnings.filterwarnings("ignore")
+
+import os
+import json
+import subprocess
+import sys
+from typing import List, Tuple
+from llama_cpp import Llama
+from llama_cpp_agent import LlamaCppAgent
+from llama_cpp_agent.providers import LlamaCppPythonProvider
+from llama_cpp_agent.chat_history import BasicChatHistory
+from llama_cpp_agent.chat_history.messages import Roles
+from llama_cpp_agent.messages_formatter import MessagesFormatter, PromptMarkers
+from huggingface_hub import hf_hub_download
 import gradio as gr
-from huggingface_hub import InferenceClient
-import torch
-from transformers import AutoTokenizer, Gemma3ForCausalLM
-
-model_path = "SRP-base-model-training/gemma_3_800M_sft_v2_translation-kazparc_latest"
-tokenizer = AutoTokenizer.from_pretrained(model_path)
-model = Gemma3ForCausalLM.from_pretrained(
-    model_path,
-    torch_dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32,
-    device_map="auto"
+from logger import logging
+from exception import CustomExceptionHandling
+
+
+# Load the Environment Variables from .env file
+huggingface_token = os.getenv("HUGGINGFACE_TOKEN")
+
+# Download gguf model files
+if not os.path.exists("./models"):
+    os.makedirs("./models")
+
+hf_hub_download(
+    repo_id="bartowski/google_gemma-3-1b-it-GGUF",
+    filename="google_gemma-3-1b-it-Q4_K_M.gguf",
+    local_dir="./models",
+)
+hf_hub_download(
+    repo_id="bartowski/google_gemma-3-1b-it-GGUF",
+    filename="google_gemma-3-1b-it-Q5_K_M.gguf",
+    local_dir="./models",
 )
-model.eval()
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(message, history, system_message, max_tokens, temperature, top_p):
-    messages = [{"role": "system", "content": system_message}]
-    
-    # Rebuild full chat history
-    for user_msg, assistant_msg in history:
-        if user_msg:
-            messages.append({"role": "user", "content": user_msg})
-        if assistant_msg:
-            messages.append({"role": "assistant", "content": assistant_msg})
-    messages.append({"role": "user", "content": message})
-
-    # Convert chat to single prompt
-    prompt = ""
-    for msg in messages:
-        role = msg["role"]
-        content = msg["content"]
-        if role == "system":
-            prompt += f"[SYSTEM] {content}\n"
-        elif role == "user":
-            prompt += f"[USER] {content}\n"
-        elif role == "assistant":
-            prompt += f"[ASSISTANT] {content}\n"
-    prompt += "[ASSISTANT]"
-
-    # Tokenize
-    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
-    input_len = inputs["input_ids"].shape[-1]
-
-    # Generate tokens (with streaming behavior)
-    generated_text = ""
-    with torch.no_grad():
-        output_ids = model.generate(
-            **inputs,
-            max_new_tokens=max_tokens,
-            do_sample=True,
-            temperature=temperature,
-            top_p=top_p,
-            repetition_penalty=1.2,
-            pad_token_id=tokenizer.eos_token_id
+
+
+# Define the prompt markers for Gemma 3
+gemma_3_prompt_markers = {
+    Roles.system: PromptMarkers("", "\n"),  # System prompt should be included within user message
+    Roles.user: PromptMarkers("<start_of_turn>user\n", "<end_of_turn>\n"),
+    Roles.assistant: PromptMarkers("<start_of_turn>model\n", "<end_of_turn>\n"),
+    Roles.tool: PromptMarkers("", ""),  # If you need tool support
+}
+
+# Create the formatter
+gemma_3_formatter = MessagesFormatter(
+    pre_prompt="",  # No pre-prompt
+    prompt_markers=gemma_3_prompt_markers,
+    include_sys_prompt_in_first_user_message=True,  # Include system prompt in first user message
+    default_stop_sequences=["<end_of_turn>", "<start_of_turn>"],
+    strip_prompt=False,  # Don't strip whitespace from the prompt
+    bos_token="<bos>",  # Beginning of sequence token for Gemma 3
+    eos_token="<eos>",  # End of sequence token for Gemma 3
+)
+
+
+# Set the title and description
+title = "Gemma Llama.cpp"
+description = """Google released **[Gemma 3](https://blog.google/technology/developers/gemma-3/)**, a family of multimodal models that offers advanced capabilities like large context and multilingual support.
+This interactive chat interface allows you to experiment with the [`gemma-3-1b-it`](https://huggingface.co/google/gemma-3-1b-it) text model using various prompts and generation parameters.
+Users can select different model variants (GGUF format), system prompts, and observe generated responses in real-time. 
+Key generation parameters, such as ⁣`temperature`, `max_tokens`, `top_k` and others are exposed below for tuning model behavior.
+For a detailed technical walkthrough, please refer to the accompanying **[blog post](https://sitammeur.medium.com/build-your-own-gemma-3-chatbot-with-gradio-and-llama-cpp-46457b22a28e)**."""
+
+
+llm = None
+llm_model = None
+
+def respond(
+    message: str,
+    history: List[Tuple[str, str]],
+    model: str = "google_gemma-3-1b-it-Q4_K_M.gguf",  # Set default model
+    system_message: str = "You are a helpful assistant.",
+    max_tokens: int = 1024,
+    temperature: float = 0.7,
+    top_p: float = 0.95,
+    top_k: int = 40,
+    repeat_penalty: float = 1.1,
+):
+    """
+    Respond to a message using the Gemma3 model via Llama.cpp.
+    Args:
+        - message (str): The message to respond to.
+        - history (List[Tuple[str, str]]): The chat history.
+        - model (str): The model to use.
+        - system_message (str): The system message to use.
+        - max_tokens (int): The maximum number of tokens to generate.
+        - temperature (float): The temperature of the model.
+        - top_p (float): The top-p of the model.
+        - top_k (int): The top-k of the model.
+        - repeat_penalty (float): The repetition penalty of the model.
+    Returns:
+        str: The response to the message.
+    """
+    try:
+        # Load the global variables
+        global llm
+        global llm_model
+
+        # Ensure model is not None
+        if model is None:
+            model = "google_gemma-3-1b-it-Q4_K_M.gguf"
+
+        # Load the model
+        if llm is None or llm_model != model:
+            # Check if model file exists
+            model_path = f"models/{model}"
+            if not os.path.exists(model_path):
+                yield f"Error: Model file not found at {model_path}. Please check your model path."
+                return
+
+            llm = Llama(
+                model_path=f"models/{model}",
+                flash_attn=False,
+                n_gpu_layers=0,
+                n_batch=8,
+                n_ctx=2048,
+                n_threads=8,
+                n_threads_batch=8,
+            )
+            llm_model = model
+        provider = LlamaCppPythonProvider(llm)
+
+        # Create the agent
+        agent = LlamaCppAgent(
+            provider,
+            system_prompt=f"{system_message}",
+            custom_messages_formatter=gemma_3_formatter,
+            debug_output=True,
         )
-        output = output_ids[0][input_len:]
-        for i in range(output.shape[0]):
-            token = output[i].unsqueeze(0)
-            text_piece = tokenizer.decode(token, skip_special_tokens=True)
-            generated_text += text_piece
-            yield generated_text
-
-# Gradio UI
+
+        # Set the settings like temperature, top-k, top-p, max tokens, etc.
+        settings = provider.get_provider_default_settings()
+        settings.temperature = temperature
+        settings.top_k = top_k
+        settings.top_p = top_p
+        settings.max_tokens = max_tokens
+        settings.repeat_penalty = repeat_penalty
+        settings.stream = True
+
+        messages = BasicChatHistory()
+
+        # Add the chat history
+        for msn in history:
+            user = {"role": Roles.user, "content": msn[0]}
+            assistant = {"role": Roles.assistant, "content": msn[1]}
+            messages.add_message(user)
+            messages.add_message(assistant)
+
+        # Get the response stream
+        stream = agent.get_chat_response(
+            message,
+            llm_sampling_settings=settings,
+            chat_history=messages,
+            returns_streaming_generator=True,
+            print_output=False,
+        )
+
+        # Log the success
+        logging.info("Response stream generated successfully")
+
+        # Generate the response
+        outputs = ""
+        for output in stream:
+            outputs += output
+            yield outputs
+
+    # Handle exceptions that may occur during the process
+    except Exception as e:
+        # Custom exception handling
+        raise CustomExceptionHandling(e, sys) from e
+
+
+# Create a chat interface
 demo = gr.ChatInterface(
     respond,
-    additional_inputs_accordion=gr.Accordion(label="⚙️ Parameters", open=False),
+    examples=[["What is the capital of France?"], ["Tell me something about artificial intelligence."], ["What is gravity?"]],
+    additional_inputs_accordion=gr.Accordion(
+        label="⚙️ Parameters", open=False, render=False
+    ),
     additional_inputs=[
-        gr.Textbox(value="You are a helpful assistant.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p"),
+        gr.Dropdown(
+            choices=[
+                "google_gemma-3-1b-it-Q4_K_M.gguf",
+                "google_gemma-3-1b-it-Q5_K_M.gguf",
+            ],
+            value="google_gemma-3-1b-it-Q4_K_M.gguf",
+            label="Model",
+            info="Select the AI model to use for chat",
+        ),
+        gr.Textbox(
+            value="You are a helpful assistant.",
+            label="System Prompt",
+            info="Define the AI assistant's personality and behavior",
+            lines=2,
+        ),
+        gr.Slider(
+            minimum=512,
+            maximum=2048,
+            value=1024,
+            step=1,
+            label="Max Tokens",
+            info="Maximum length of response (higher = longer replies)",
+        ),
+        gr.Slider(
+            minimum=0.1,
+            maximum=2.0,
+            value=0.7,
+            step=0.1,
+            label="Temperature",
+            info="Creativity level (higher = more creative, lower = more focused)",
+        ),
+        gr.Slider(
+            minimum=0.1,
+            maximum=1.0,
+            value=0.95,
+            step=0.05,
+            label="Top-p",
+            info="Nucleus sampling threshold",
+        ),
+        gr.Slider(
+            minimum=1,
+            maximum=100,
+            value=40,
+            step=1,
+            label="Top-k",
+            info="Limit vocabulary choices to top K tokens",
+        ),
+        gr.Slider(
+            minimum=1.0,
+            maximum=2.0,
+            value=1.1,
+            step=0.1,
+            label="Repetition Penalty",
+            info="Penalize repeated words (higher = less repetition)",
+        ),
     ],
     theme="Ocean",
+    submit_btn="Send",
+    stop_btn="Stop",
+    title=title,
+    description=description,
+    chatbot=gr.Chatbot(scale=1, show_copy_button=True, resizable=True),
+    flagging_mode="never",
+    editable=True,
+    cache_examples=False,
 )
 
+
+# Launch the chat interface
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch(
+        share=False,
+        server_name="0.0.0.0",
+        server_port=7860,
+        show_api=False,
+    )