Update app.py

app.py

@@ -1,12 +1,7 @@
-# Importing required libraries
-import warnings
-warnings.filterwarnings("ignore")
-
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+from typing import List, Tuple, Optional
 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 import MessagesFormatterType
@@ -14,10 +9,16 @@ 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 huggingface_hub import hf_hub_download
-import gradio as gr
+import logging
+import sys
 from logger import logging
 from exception import CustomExceptionHandling
 
+app = FastAPI(
+    title="Dolphin Llama.cpp API",
+    description="API for interacting with Dolphin3.0 models using Llama.cpp",
+    version="1.0.0"
+)
 
 # Download gguf model files
 if not os.path.exists("./models"):
@@ -39,65 +40,33 @@ hf_hub_download(
     local_dir="./models",
 )
 
-
-# Set the title and description
-title = "Dolphin🐬 Llama.cpp"
-description = """**[Dolphin 3.0](https://huggingface.co/collections/cognitivecomputations/dolphin-30-677ab47f73d7ff66743979a3)** is a powerful, general-purpose local AI model designed for coding, math, and various other tasks, aiming similar to the models like ChatGPT and Claude.
-This interactive chat interface allows you to experiment with the [`Dolphin3.0-Qwen2.5-0.5B`](https://huggingface.co/cognitivecomputations/Dolphin3.0-Qwen2.5-0.5B) and [`Dolphin3.0-Llama3.2-1B`](https://huggingface.co/cognitivecomputations/Dolphin3.0-Llama3.2-1B) text models 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."""
-
-
 llm = None
 llm_model = None
 
-def respond(
-    message: str,
-    history: List[Tuple[str, str]],
-    model: str = "Dolphin3.0-Qwen2.5-0.5B-Q6_K.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 Dolphin-3 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.
-    """
+class ChatRequest(BaseModel):
+    message: str
+    history: List[Tuple[str, str]] = []
+    model: str = "Dolphin3.0-Qwen2.5-0.5B-Q6_K.gguf"
+    system_message: str = "You are Dolphin, a helpful AI assistant focused on accurate and ethical responses."
+    max_tokens: int = 1024
+    temperature: float = 0.7
+    top_p: float = 0.95
+    top_k: int = 40
+    repeat_penalty: float = 1.1
+
+class ChatResponse(BaseModel):
+    response: str
+
+def initialize_llm(model: str):
+    global llm, llm_model
     try:
-        # Load the global variables
-        global llm
-        global llm_model
-
-        # Ensure model is not None
-        if model is None:
-            model = "Dolphin3.0-Qwen2.5-0.5B-Q6_K.gguf"
+        model_path = f"models/{model}"
+        if not os.path.exists(model_path):
+            raise HTTPException(status_code=400, detail=f"Model file not found at {model_path}")
 
-        # 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}",
+                model_path=model_path,
                 flash_attn=False,
                 n_gpu_layers=0,
                 n_batch=8,
@@ -106,140 +75,58 @@ def respond(
                 n_threads_batch=8,
             )
             llm_model = model
+        return llm
+    except Exception as e:
+        raise CustomExceptionHandling(e, sys) from e
+
+@app.post("/chat", response_model=ChatResponse)
+async def chat(request: ChatRequest):
+    try:
+        # Initialize LLM
+        llm = initialize_llm(request.model)
         provider = LlamaCppPythonProvider(llm)
 
-        # Create the agent
+        # Create agent
         agent = LlamaCppAgent(
             provider,
-            system_prompt=f"{system_message}",
+            system_prompt=request.system_message,
             predefined_messages_formatter_type=MessagesFormatterType.CHATML,
             debug_output=True,
         )
 
-        # Set the settings like temperature, top-k, top-p, max tokens, etc.
+        # Set sampling settings
         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
-
+        settings.temperature = request.temperature
+        settings.top_k = request.top_k
+        settings.top_p = request.top_p
+        settings.max_tokens = request.max_tokens
+        settings.repeat_penalty = request.repeat_penalty
+        settings.stream = False
+
+        # Build chat history
         messages = BasicChatHistory()
+        for user_msg, assistant_msg in request.history:
+            messages.add_message({"role": Roles.user, "content": user_msg})
+            messages.add_message({"role": Roles.assistant, "content": assistant_msg})
 
-        # 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,
+        # Get response
+        response = agent.get_chat_response(
+            request.message,
             llm_sampling_settings=settings,
             chat_history=messages,
-            returns_streaming_generator=True,
             print_output=False,
         )
 
-        # Log the success
-        logging.info("Response stream generated successfully")
+        logging.info("Response generated successfully")
+        return ChatResponse(response=response)
 
-        # 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
 
+@app.get("/health")
+async def health_check():
+    return {"status": "healthy"}
 
-# Create a chat interface
-demo = gr.ChatInterface(
-    respond,
-    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.Dropdown(
-            choices=[
-                "Dolphin3.0-Llama3.2-1B-Q4_K_M.gguf",
-                "Dolphin3.0-Qwen2.5-0.5B-Q6_K.gguf",
-                "Qwen2.5-Coder-14B-Instruct-Q6_K.gguf",
-            ],
-            value="Dolphin3.0-Qwen2.5-0.5B-Q6_K.gguf",
-            label="Model",
-            info="Select the AI model to use for chat",
-        ),
-        gr.Textbox(
-            value="You are Dolphin, a helpful AI assistant focused on accurate and ethical responses.",
-            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(
-        share=True,
-        server_name="0.0.0.0",
-        server_port=7860,
-        show_api=True,
-    )
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8000)