Se cambio todo a un codigo de web

app.py

@@ -1,63 +1,130 @@
+import os
+from threading import Thread
+from typing import Iterator
+
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-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("ussipan/SipanGPT-0.1-Llama-3.2-1B-GGUF")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are SipanGPT, an artificial intelligence assistant responsible for providing technical support for the Information Technology Department (DTI) to students, professors, and administrative staff at the Señor de Sipán University, a private university in the Lambayeque region of Peru.", 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=1.5, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
+#import spaces
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
+
+MAX_MAX_NEW_TOKENS = 2048
+DEFAULT_MAX_NEW_TOKENS = 1024
+MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096"))
+
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+# Download model from Huggingface Hub
+# Change this to meta-llama or the correct org name from Huggingface Hub
+model_id = "ussipan/SipanGPT-0.1-Llama-3.2-1B-GGUF"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    device_map="auto",
+    torch_dtype=torch.bfloat16,
 )
+model.eval()
+
+# Main Gradio inference function
+def generate(
+    message: str,
+    chat_history: list[tuple[str, str]],
+    max_new_tokens: int = 1024,
+    temperature: float = 0.6,
+    top_p: float = 0.9,
+    top_k: int = 50,
+    repetition_penalty: float = 1.2,
+) -> Iterator[str]:
+
+    conversation = [{k: v for k, v in d.items() if k != 'metadata'} for d in chat_history]
+    conversation.append({"role": "user", "content": message})
+
+    input_ids = tokenizer.apply_chat_template(conversation, add_generation_prompt=True, return_tensors="pt")
+    if input_ids.shape[1] > MAX_INPUT_TOKEN_LENGTH:
+        input_ids = input_ids[:, -MAX_INPUT_TOKEN_LENGTH:]
+        gr.Warning(f"Se recortó la entrada de la conversación porque era más larga que {MAX_INPUT_TOKEN_LENGTH} tokens.")
+    input_ids = input_ids.to(model.device)
+
+    streamer = TextIteratorStreamer(tokenizer, timeout=20.0, skip_prompt=True, skip_special_tokens=True)
+    generate_kwargs = dict(
+        {"input_ids": input_ids},
+        streamer=streamer,
+        max_new_tokens=max_new_tokens,
+        do_sample=True,
+        top_p=top_p,
+        top_k=top_k,
+        temperature=temperature,
+        num_beams=1,
+        repetition_penalty=repetition_penalty,
+    )
+    t = Thread(target=model.generate, kwargs=generate_kwargs)
+    t.start()
+
+    conversation.append({"role": "assistant", "content": ""})
+    outputs = []
+    for text in streamer:
+        outputs.append(text)
+        bot_response = "".join(outputs)
+        conversation[-1]['content'] = bot_response
+        yield "", conversation
+
+
+# Implementing Gradio 5 features and building a ChatInterface UI yourself
+PLACEHOLDER = """<div style="padding: 20px; text-align: center; display: flex; flex-direction: column; align-items: center;">
+   <img src="https://corladlalibertad.org.pe/wp-content/uploads/2024/01/USS.jpg" style="width: 80%; max-width: 550px; height: auto; opacity: 0.55; margin-bottom: 10px;">
+   <h1 style="font-size: 28px; margin: 0;">SipánGPT 0.1 Llama 3.2</h1>
+   <p style="font-size: 8px; margin: 5px 0 0; opacity: 0.65;">
+       <a href="https://huggingface.co/spaces/ysharma/Llama3-2_with_Gradio-5" target="_blank" style="color: inherit; text-decoration: none;">Source Code</a>
+   </p>
+</div>"""
+
+
+def handle_retry(history, retry_data: gr.RetryData):
+    new_history = history[:retry_data.index]
+    previous_prompt = history[retry_data.index]['content']
+    yield from generate(previous_prompt, chat_history = new_history, max_new_tokens = 1024, temperature = 0.6, top_p = 0.9, top_k = 50, repetition_penalty = 1.2)
+
+def handle_like(data: gr.LikeData):
+    if data.liked:
+        print("Votaste positivamente esta respuesta: ", data.value)
+    else:
+        print("Votaste negativamente esta respuesta: ", data.value)
+
+def handle_undo(history, undo_data: gr.UndoData):
+    chatbot = history[:undo_data.index]
+    prompt = history[undo_data.index]['content']
+    return chatbot, prompt
+
+def chat_examples_fill(data: gr.SelectData):
+    yield from generate(data.value['text'], chat_history = [], max_new_tokens = 1024, temperature = 0.6, top_p = 0.9, top_k = 50, repetition_penalty = 1.2)
+
+
+with gr.Blocks(theme=gr.themes.Soft(), fill_height=True) as demo:
+    with gr.Column(elem_id="container", scale=1):
+        chatbot = gr.Chatbot(
+            label="SipánGPT 0.1 Llama 3.2",
+            show_label=False,
+            type="messages",
+            scale=1,
+            suggestions = [
+                {"text": "Háblame del reglamento de estudiantes de la universidad"},
+                {"text": "Qué becas ofrece la universidad"},
+                ],
+            placeholder = PLACEHOLDER,
+            )
+
+    msg = gr.Textbox(submit_btn=True, show_label=False)
+    with gr.Accordion('Additional inputs', open=False):
+        max_new_tokens = gr.Slider(label="Max new tokens", minimum=1, maximum=MAX_MAX_NEW_TOKENS, step=1, value=DEFAULT_MAX_NEW_TOKENS, )
+        temperature = gr.Slider(label="Temperature",minimum=0.1, maximum=4.0, step=0.1, value=0.6,)
+        top_p = gr.Slider(label="Top-p (nucleus sampling)", minimum=0.05, maximum=1.0, step=0.05, value=0.9, )
+        top_k = gr.Slider(label="Top-k", minimum=1, maximum=1000, step=1, value=50, )
+        repetition_penalty = gr.Slider(label="Repetition penalty", minimum=1.0, maximum=2.0, step=0.05, value=1.2, )
+
+    msg.submit(generate, [msg, chatbot, max_new_tokens, temperature, top_p, top_k, repetition_penalty], [msg, chatbot])
+    chatbot.retry(handle_retry, chatbot, [msg, chatbot])
+    chatbot.like(handle_like, None, None)
+    chatbot.undo(handle_undo, chatbot, [chatbot, msg])
+    chatbot.suggestion_select(chat_examples_fill, None, [msg, chatbot] )
 
 
-if __name__ == "__main__":
-    demo.launch()
+demo.launch()

requirements.txt

@@ -1 +1,3 @@
-huggingface_hub==0.22.2
+accelerate==0.33.0
+bitsandbytes==0.43.2
+transformers