Example_updated_with_Lib

app.py

@@ -112,158 +112,6 @@
 # if __name__ == "__main__":
 #     demo.launch()
 
-### 26 aug Use a pipeline as a high-level Logic
-# import spaces
-# import os
-# 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
-
-# huggingface_token = os.getenv("HF_TOKEN")
-
-# # 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
-# )
-
-# llm = None
-# llm_model = None
-
-# @spaces.GPU(duration=120)
-# 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
-    
-#     # 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(
-#                 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)}"
-
-#     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()
-
-#     # 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:
-#         stream = agent.get_chat_response(
-#             message,
-#             llm_sampling_settings=settings,
-#             chat_history=messages,
-#             returns_streaming_generator=True,
-#             print_output=False
-#         )
-        
-#         outputs = ""
-#         for output in stream:
-#             outputs += output
-#             yield outputs
-#     except Exception as e:
-#         yield f"Error during response generation: {str(e)}"
-
-# description = """<p align="center">Using the Meta-Llama-3.1-8B-Instruct Model</p>"""
-
-# demo = gr.ChatInterface(
-#     respond,
-#     additional_inputs=[
-#         gr.Dropdown([
-#                 'Meta-Llama-3.1-8B-Instruct-Q5_K_M.gguf'
-#             ],
-#             value="Meta-Llama-3.1-8B-Instruct-Q5_K_M.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 Meta-Llama-3.1-8B-Instruct using llama.cpp", 
-#     description=description,
-#     chatbot=gr.Chatbot(
-#         scale=1, 
-#         likeable=False,
-#         show_copy_button=True
-#     )
-# )
-
-# if __name__ == "__main__":
-#     demo.launch()
-
-
 
 ####03 3.1 8b
 
@@ -583,7 +431,7 @@
 
 
 
-###04 
+###OCT04 LLAMA3.2 Vision Model
 from transformers import MllamaForConditionalGeneration, AutoProcessor, TextIteratorStreamer
 from PIL import Image
 import requests
@@ -659,18 +507,7 @@ def bot_streaming(message, history, max_new_tokens=250):
         yield buffer
 
 
-demo = gr.ChatInterface(fn=bot_streaming, title="Multimodal Llama", examples=[
-    [{"text": "Which era does this piece belong to? Give details about the era.", "files":["./examples/rococo.jpg"]},
-    200],
-    [{"text": "Where do the droughts happen according to this diagram?", "files":["./examples/weather_events.png"]},
-    250],
-    [{"text": "What happens when you take out white cat from this chain?", "files":["./examples/ai2d_test.jpg"]},
-    250],
-    [{"text": "How long does it take from invoice date to due date? Be short and concise.", "files":["./examples/invoice.png"]},
-    250],
-    [{"text": "Where to find this monument? Can you give me other recommendations around the area?", "files":["./examples/wat_arun.jpg"]}, 
-    250],
-    ],
+demo = gr.ChatInterface(fn=bot_streaming, title="Multimodal Llama",
       textbox=gr.MultimodalTextbox(), 
       additional_inputs = [gr.Slider(
               minimum=10,