Update app.py

app.py

@@ -1,48 +1,201 @@
-import os
-import gradio as gr
-from llama_cpp import Llama
 import spaces
+import subprocess
+from llama_cpp import Llama
+from llama_cpp_agent import LlamaCppAgent, MessagesFormatterType
+from llama_cpp_agent.providers import LlamaCppPythonProvider
+from llama_cpp_agent.chat_history import BasicChatHistory
+from llama_cpp_agent.chat_history.messages import Roles
+import gradio as gr
+from huggingface_hub import hf_hub_download
+import os
+import cv2
+
+huggingface_token = os.environ.get('HF_TOKEN')
 
-llm = None  # Global model variable
+# Download the Meta-Llama-3.1-8B-Instruct model
+hf_hub_download(
+    repo_id="bartowski/Meta-Llama-3.1-8B-Instruct-GGUF",
+    filename="Meta-Llama-3.1-8B-Instruct-Q5_K_M.gguf",
+    local_dir="./models",
+    token=huggingface_token
+)
+
+hf_hub_download(
+    repo_id="bartowski/Mistral-Nemo-Instruct-2407-GGUF",
+    filename="Mistral-Nemo-Instruct-2407-Q5_K_M.gguf",
+    local_dir="./models",
+    token=huggingface_token
+)
+
+hf_hub_download(
+    repo_id="bartowski/gemma-2-2b-it-GGUF",
+    filename="gemma-2-2b-it-Q6_K_L.gguf",
+    local_dir="./models",
+    token=huggingface_token
+)
+
+hf_hub_download(
+    repo_id="bartowski/openchat-3.6-8b-20240522-GGUF",
+    filename="openchat-3.6-8b-20240522-Q6_K.gguf",
+    local_dir="./models",
+    token=huggingface_token
+)
+
+hf_hub_download(
+    repo_id="bartowski/Llama-3-Groq-8B-Tool-Use-GGUF",
+    filename="Llama-3-Groq-8B-Tool-Use-Q6_K.gguf",
+    local_dir="./models",
+    token=huggingface_token
+)
+
+hf_hub_download(
+    repo_id="bartowski/MiniCPM-V-2_6-GGUF",
+    filename="MiniCPM-V-2_6-Q6_K.gguf",
+    local_dir="./models",
+    token=huggingface_token
+)
+
+hf_hub_download(
+    repo_id="CaioXapelaum/Llama-3.1-Storm-8B-Q5_K_M-GGUF",
+    filename="llama-3.1-storm-8b-q5_k_m.gguf",
+    local_dir="./models",
+    token=huggingface_token
+)
+
+hf_hub_download(
+    repo_id="CaioXapelaum/Orca-2-7b-Patent-Instruct-Llama-2-Q5_K_M-GGUF",
+    filename="orca-2-7b-patent-instruct-llama-2-q5_k_m.gguf",
+    local_dir="./models",
+    token=huggingface_token
+)
+
+
+
+llm = None
+llm_model = None
+
+cv2.setNumThreads(1)
 
 @spaces.GPU()
-def chat(message, history):
+def respond(
+    message,
+    history: list[tuple[str, str]],
+    model,
+    system_message,
+    max_tokens,
+    temperature,
+    top_p,
+    top_k,
+    repeat_penalty,
+):
+    chat_template = MessagesFormatterType.GEMMA_2
+
     global llm
+    global llm_model
 
-    # Initialize Llama model if not already loaded
-    if llm is None:
+    # Load model only if it's not already loaded or if a new model is selected
+    if llm is None or llm_model != model:
         try:
-            llm = Llama.from_pretrained(
-                repo_id="bartowski/Meta-Llama-3.1-70B-Instruct-GGUF",
-                filename="Meta-Llama-3.1-70B-Instruct-Q4_K_S.gguf",
-                verbose=False  # Set to True if you need debug information
+            llm = Llama(
+                model_path=f"models/{model}",
+                flash_attn=True,
+                n_gpu_layers=81,  # Adjust based on available GPU resources
+                n_batch=1024,
+                n_ctx=8192,
             )
+            llm_model = model
         except Exception as e:
             return f"Error loading model: {str(e)}"
 
-    # Prepare the chat history
-    chat_history = [{"role": "user", "content": user_input} for user_input, _ in history]
-    chat_history.append({"role": "user", "content": message})
+    provider = LlamaCppPythonProvider(llm)
+
+    agent = LlamaCppAgent(
+        provider,
+        system_prompt=f"{system_message}",
+        predefined_messages_formatter_type=chat_template,
+        debug_output=True
+    )
+
+    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()
 
-    # Stream the response back as it's generated
+    # Add user and assistant messages to the 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)
+
+    # Stream the response
     try:
-        response_stream = llm.create_chat_completion_stream(
-            messages=chat_history,
-            stream=True  # Ensure streaming is enabled
+        stream = agent.get_chat_response(
+            message,
+            llm_sampling_settings=settings,
+            chat_history=messages,
+            returns_streaming_generator=True,
+            print_output=False
         )
 
-        output = ""
-        for chunk in response_stream:
-            output += chunk['text']  # Collect and stream text chunks
-            yield output  # Stream each updated response chunk
-
+        outputs = ""
+        for output in stream:
+            outputs += output
+            yield outputs
     except Exception as e:
-        yield f"Error generating response: {str(e)}"
+        yield f"Error during response generation: {str(e)}"
 
-# Gradio Chat Interface setup
 demo = gr.ChatInterface(
-    fn=chat,  # Function to call when the user sends a message
-    title="Chat with Meta-Llama 3.1 70B",  # Title for the chat interface
+    fn=respond,
+    additional_inputs=[
+        gr.Dropdown([
+                'Meta-Llama-3.1-8B-Instruct-Q5_K_M.gguf',
+                'Mistral-Nemo-Instruct-2407-Q5_K_M.gguf',
+                'gemma-2-2b-it-Q6_K_L.gguf',
+                'openchat-3.6-8b-20240522-Q6_K.gguf',
+                'Llama-3-Groq-8B-Tool-Use-Q6_K.gguf',
+                'MiniCPM-V-2_6-Q6_K.gguf',
+                'llama-3.1-storm-8b-q5_k_m.gguf',
+                'orca-2-7b-patent-instruct-llama-2-q5_k_m.gguf'
+            ],
+            value="gemma-2-2b-it-Q6_K_L.gguf",
+            label="Model"
+        ),
+        gr.Textbox(value="You are a helpful assistant.", label="System message"),
+        gr.Slider(minimum=1, maximum=4096, value=2048, step=1, label="Max 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.Slider(
+            minimum=0,
+            maximum=100,
+            value=40,
+            step=1,
+            label="Top-k",
+        ),
+        gr.Slider(
+            minimum=0.0,
+            maximum=2.0,
+            value=1.1,
+            step=0.1,
+            label="Repetition penalty",
+        ),
+    ],
+    retry_btn="Retry",
+    undo_btn="Undo",
+    clear_btn="Clear",
+    submit_btn="Send",
+    title="Chat with lots of Models and LLMs using llama.cpp",
     chatbot=gr.Chatbot(
         scale=1,
         likeable=False,
@@ -51,4 +204,4 @@ demo = gr.ChatInterface(
 )
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()