Update app.py

app.py

@@ -1,43 +1,104 @@
-import os
-import requests
 import gradio as gr
-from llama_cpp import Llama
-
-# Download the model if not present
-def download_model(model_url, local_path):
-    if not os.path.exists(local_path):
-        print(f"Downloading model from {model_url}...")
-        response = requests.get(model_url)
-        with open(local_path, 'wb') as f:
-            f.write(response.content)
-        print("Model downloaded.")
-    else:
-        print("Model already exists.")
+from gpt4all import GPT4All
+from huggingface_hub import hf_hub_download
+
+title = "Mistral-7B-Instruct-GGUF Run On CPU-Basic Free Hardware"
+
+description = """
+🔎 [Mistral AI's Mistral 7B Instruct v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) [GGUF format model](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GGUF) , 4-bit quantization balanced quality gguf version, running on CPU. English Only (Also support other languages but the quality's not good). Using [GitHub - llama.cpp](https://github.com/ggerganov/llama.cpp) [GitHub - gpt4all](https://github.com/nomic-ai/gpt4all). 
+🔨 Running on CPU-Basic free hardware. Suggest duplicating this space to run without a queue. 
+Mistral does not support system prompt symbol (such as ```<<SYS>>```) now, input your system prompt in the first message if you need. Learn more: [Guardrailing Mistral 7B](https://docs.mistral.ai/usage/guardrailing). 
+"""
+
+"""
+[Model From TheBloke/Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GGUF)
+[Mistral-instruct-v0.1 System prompt](https://docs.mistral.ai/usage/guardrailing)
+"""
+
+model_path = "models"
+model_name = "unsloth.Q4_K_M.gguf"
+hf_hub_download(repo_id="vislupus/bulgarian-joke-master-gemma-2-2b-it-bnb-4bit-gguf", filename=model_name, local_dir=model_path, local_dir_use_symlinks=False)
 
-model_url = "https://huggingface.co/vislupus/bulgarian-joke-master-SmolLM2-135M-Instruct-bnb-4bit-gguf/blob/main/unsloth.Q4_K_M.gguf"
-local_model_path = "model.gguf"
+print("Start the model init process")
+model = model = GPT4All(model_name, model_path, allow_download = False, device="cpu")
+print("Finish the model init process")
+
+model.config["promptTemplate"] = "[INST] {0} [/INST]"
+model.config["systemPrompt"] = ""
+model._is_chat_session_activated = False
+
+max_new_tokens = 2048
+
+def generater(message, history, temperature, top_p, top_k):
+    prompt = "<s>"
+    for user_message, assistant_message in history:
+        prompt += model.config["promptTemplate"].format(user_message)
+        prompt += assistant_message + "</s>"
+    prompt += model.config["promptTemplate"].format(message)
+    outputs = []    
+    for token in model.generate(prompt=prompt, temp=temperature, top_k = top_k, top_p = top_p, max_tokens = max_new_tokens, streaming=True):
+        outputs.append(token)
+        yield "".join(outputs)
+
+def vote(data: gr.LikeData):
+    if data.liked:
+        return
+    else:
+        return
 
-download_model(model_url, local_model_path)
+chatbot = gr.Chatbot(avatar_images=('resourse/user-icon.png', 'resourse/chatbot-icon.png'),bubble_full_width = False)
 
-# Load the model
-llm = Llama(model_path=local_model_path)
+additional_inputs=[
+    gr.Slider(
+        label="temperature",
+        value=0.5,
+        minimum=0.0,
+        maximum=2.0,
+        step=0.05,
+        interactive=True,
+        info="Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic.",
+    ),
+    gr.Slider(
+        label="top_p",
+        value=1.0,
+        minimum=0.0,
+        maximum=1.0,
+        step=0.01,
+        interactive=True,
+        info="0.1 means only the tokens comprising the top 10% probability mass are considered. Suggest set to 1 and use temperature. 1 means 100% and will disable it",
+    ),
+    gr.Slider(
+        label="top_k",
+        value=40,
+        minimum=0,
+        maximum=1000,
+        step=1,
+        interactive=True,
+        info="limits candidate tokens to a fixed number after sorting by probability. Setting it higher than the vocabulary size deactivates this limit.",
+    )
+]
 
-# Gradio interface
-def generate_response(prompt):
-    """Generate a response using the GGUF model."""
-    output = llm(prompt, max_tokens=200)
-    return output['choices'][0]['text']
+character = "Sherlock Holmes"
+series = "Arthur Conan Doyle's novel"
 
-with gr.Blocks() as demo:
-    gr.Markdown("# GGUF Model Chatbot")
-    with gr.Row():
-        with gr.Column():
-            prompt_input = gr.Textbox(label="Your Prompt", placeholder="Type something...")
-            submit_button = gr.Button("Generate")
-        with gr.Column():
-            output_box = gr.Textbox(label="Model Response")
-    
-    submit_button.click(fn=generate_response, inputs=prompt_input, outputs=output_box)
+iface = gr.ChatInterface(
+    fn = generater,
+    title=title,
+    description = description,
+    chatbot=chatbot,
+    additional_inputs=additional_inputs,
+    examples=[
+        ["Hello there! How are you doing?"],
+        ["How many hours does it take a man to eat a Helicopter?"],
+        ["You are a helpful and honest assistant. Always answer as helpfully as possible. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information."],
+        ["I want you to act as a spoken English teacher and improver. I will speak to you in English and you will reply to me in English to practice my spoken English. I want you to strictly correct my grammar mistakes, typos, and factual errors. I want you to ask me a question in your reply. Now let's start practicing, you could ask me a question first. Remember, I want you to strictly correct my grammar mistakes, typos, and factual errors."],
+        [f"I want you to act like {character} from {series}. I want you to respond and answer like {character} using the tone, manner and vocabulary {character} would use. Do not write any explanations. Only answer like {character}. You must know all of the knowledge of {character}."]
+    ]
+)
 
-demo.launch()
+with gr.Blocks(css="resourse/style/custom.css") as demo:
+    chatbot.like(vote, None, None)
+    iface.render()
 
+if __name__ == "__main__":
+    demo.queue(max_size=3).launch()