Update app.py

app.py

@@ -1,64 +1,142 @@
+import os
+from threading import Thread
+from typing import Iterator
+
 import gradio as gr
-from huggingface_hub import InferenceClient
+import spaces
+import torch
+import json
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
 
+
+DESCRIPTION = """\
+Shakti is a 250 million parameter language model specifically optimized for resource-constrained environments such as edge devices, including smartphones, wearables, and IoT systems. With support for vernacular languages and domain-specific tasks, Shakti excels in industries such as healthcare, finance, and customer service
+For more details, please check [here](https://arxiv.org/pdf/2410.11331v1).
 """
-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("HuggingFaceH4/zephyr-7b-beta")
 
+MAX_MAX_NEW_TOKENS = 2048
+DEFAULT_MAX_NEW_TOKENS = 1024
+MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "2048"))
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+model_id = "SandLogicTechnologies/Shakti-250M"
+tokenizer = AutoTokenizer.from_pretrained(model_id, token=os.getenv("SHAKTI"))
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    device_map="auto",
+    torch_dtype=torch.bfloat16,
+    token=os.getenv("SHAKTI")
 
-    messages.append({"role": "user", "content": message})
+)
+model.eval()
 
-    response = ""
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
+@spaces.GPU(duration=90)
+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 = []
+    for user, assistant in chat_history:
+        conversation.extend(
+            [
+                {"role": "user", "content": user},
+                {"role": "assistant", "content": assistant},
+            ]
+        )
+    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"Trimmed input from conversation as it was longer than {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,
-    ):
-        token = message.choices[0].delta.content
+        top_k=top_k,
+        temperature=temperature,
+        num_beams=1,
+        repetition_penalty=repetition_penalty,
+    )
+    t = Thread(target=model.generate, kwargs=generate_kwargs)
+    t.start()
 
-        response += token
-        yield response
+    outputs = []
+    for text in streamer:
+        outputs.append(text)
+        yield "".join(outputs)
 
 
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
+chat_interface = gr.ChatInterface(
+    fn=generate,
     additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", 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=0.7, step=0.1, label="Temperature"),
         gr.Slider(
+            label="Max new tokens",
+            minimum=1,
+            maximum=MAX_MAX_NEW_TOKENS,
+            step=1,
+            value=DEFAULT_MAX_NEW_TOKENS,
+        ),
+        gr.Slider(
+            label="Temperature",
             minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
+            maximum=4.0,
+            step=0.1,
+            value=0.6,
         ),
+        # gr.Slider(
+        #     label="Top-p (nucleus sampling)",
+        #     minimum=0.05,
+        #     maximum=1.0,
+        #     step=0.05,
+        #     value=0.9,
+        # ),
+        # gr.Slider(
+        #     label="Top-k",
+        #     minimum=1,
+        #     maximum=1000,
+        #     step=1,
+        #     value=50,
+        # ),
+        # gr.Slider(
+        #     label="Repetition penalty",
+        #     minimum=1.0,
+        #     maximum=2.0,
+        #     step=0.05,
+        #     value=1.2,
+        # ),
+    ],
+    stop_btn=None,
+    examples=[
+        ["Can you explain the pathophysiology of hypertension and its impact on the cardiovascular system?"],
+        ["What are the potential side effects of beta-blockers in the treatment of arrhythmias?"],
+        ["What foods are good for boosting the immune system?"],
+        ["What is the difference between a stock and a bond?"],
+        ["How can I start saving for retirement?"],
+        ["What are some low-risk investment options?"],
+        ["What is a power of attorney and when is it used?"],
+        ["What are the key differences between a will and a trust?"],
+        ["How do I legally protect my business name?"]
     ],
+    cache_examples=False,
 )
 
+with gr.Blocks(css="style.css", fill_height=True) as demo:
+    gr.Markdown(DESCRIPTION)
+    gr.DuplicateButton(value="Duplicate Space for private use", elem_id="duplicate-button")
+    chat_interface.render()
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.queue(max_size=20).launch()