update

app.py

@@ -50,63 +50,236 @@
 #     demo.launch()
 
 
+##Running smothly CHATBOT
 
+# 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("meta-llama/Meta-Llama-3-8B-Instruct")
+
+# def respond(
+#     message: str,
+#     history: list[tuple[str, str]],  # This will not be used
+#     system_message: str,
+#     max_tokens: int,
+#     temperature: float,
+#     top_p: float,
+# ):
+#     # Build the messages list
+#     messages = [{"role": "system", "content": system_message}]
+#     messages.append({"role": "user", "content": message})
+
+#     response = ""
+
+#     try:
+#         # Generate response from the model
+#         for msg in client.chat_completion(
+#             messages=messages,
+#             max_tokens=max_tokens,
+#             stream=True,
+#             temperature=temperature,
+#             top_p=top_p,
+#         ):
+#             if msg.choices[0].delta.content is not None:
+#                 token = msg.choices[0].delta.content
+#                 response += token
+#             yield response
+#     except Exception as e:
+#         yield f"An error occurred: {e}"
+
+# """
+# 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 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(
+#             minimum=0.1,
+#             maximum=1.0,
+#             value=0.95,
+#             step=0.05,
+#             label="Top-p (nucleus sampling)",
+#         ),
+#     ],
+# )
+
+# if __name__ == "__main__":
+#     demo.launch()
+
+
+### 20aug
+
+import os
+import time
+import spaces
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer, BitsAndBytesConfig
 import gradio as gr
-from huggingface_hub import InferenceClient
+from threading import Thread
 
+MODEL_LIST = ["meta-llama/Meta-Llama-3.1-8B-Instruct"]
+HF_TOKEN = os.environ.get("HF_API_TOKEN", None)
+MODEL = os.environ.get("MODEL_ID")
+
+TITLE = "<h1><center>Meta-Llama3.1-8B</center></h1>"
+
+PLACEHOLDER = """
+<center>
+<p>Hi! How can I help you today?</p>
+</center>
 """
-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
+
+
+CSS = """
+.duplicate-button {
+    margin: auto !important;
+    color: white !important;
+    background: black !important;
+    border-radius: 100vh !important;
+}
+h3 {
+    text-align: center;
+}
 """
-client = InferenceClient("meta-llama/Meta-Llama-3-8B-Instruct")
-
-def respond(
-    message: str,
-    history: list[tuple[str, str]],  # This will not be used
-    system_message: str,
-    max_tokens: int,
-    temperature: float,
-    top_p: float,
+
+device = "cuda" # for GPU usage or "cpu" for CPU usage
+
+quantization_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_compute_dtype=torch.bfloat16,
+    bnb_4bit_use_double_quant=True,
+    bnb_4bit_quant_type= "nf4")
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL)
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+    quantization_config=quantization_config)
+
+@spaces.GPU()
+def stream_chat(
+    message: str, 
+    history: list,
+    system_prompt: str,
+    temperature: float = 0.8, 
+    max_new_tokens: int = 1024, 
+    top_p: float = 1.0, 
+    top_k: int = 20, 
+    penalty: float = 1.2,
 ):
-    # Build the messages list
-    messages = [{"role": "system", "content": system_message}]
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    try:
-        # Generate response from the model
-        for msg in client.chat_completion(
-            messages=messages,
-            max_tokens=max_tokens,
-            stream=True,
-            temperature=temperature,
-            top_p=top_p,
-        ):
-            if msg.choices[0].delta.content is not None:
-                token = msg.choices[0].delta.content
-                response += token
-            yield response
-    except Exception as e:
-        yield f"An error occurred: {e}"
+    print(f'message: {message}')
+    print(f'history: {history}')
+
+    conversation = [
+        {"role": "system", "content": system_prompt}
+    ]
+    for prompt, answer in history:
+        conversation.extend([
+            {"role": "user", "content": prompt}, 
+            {"role": "assistant", "content": answer},
+        ])
+
+    conversation.append({"role": "user", "content": message})
+
+    input_ids = tokenizer.apply_chat_template(conversation, add_generation_prompt=True, return_tensors="pt").to(model.device)
+    
+    streamer = TextIteratorStreamer(tokenizer, timeout=60.0, skip_prompt=True, skip_special_tokens=True)
+    
+    generate_kwargs = dict(
+        input_ids=input_ids, 
+        max_new_tokens = max_new_tokens,
+        do_sample = False if temperature == 0 else True,
+        top_p = top_p,
+        top_k = top_k,
+        temperature = temperature,
+        repetition_penalty=penalty,
+        eos_token_id=[128001,128008,128009],
+        streamer=streamer,
+    )
+
+    with torch.no_grad():
+        thread = Thread(target=model.generate, kwargs=generate_kwargs)
+        thread.start()
+        
+    buffer = ""
+    for new_text in streamer:
+        buffer += new_text
+        yield buffer
+
+            
+chatbot = gr.Chatbot(height=600, placeholder=PLACEHOLDER)
+
+with gr.Blocks(css=CSS, theme="soft") as demo:
+    gr.HTML(TITLE)
+    gr.DuplicateButton(value="Duplicate Space for private use", elem_classes="duplicate-button")
+    gr.ChatInterface(
+        fn=stream_chat,
+        chatbot=chatbot,
+        fill_height=True,
+        additional_inputs_accordion=gr.Accordion(label="⚙️ Parameters", open=False, render=False),
+        additional_inputs=[
+            gr.Textbox(
+                value="You are a helpful assistant",
+                label="System Prompt",
+                render=False,
+            ),
+            gr.Slider(
+                minimum=0,
+                maximum=1,
+                step=0.1,
+                value=0.8,
+                label="Temperature",
+                render=False,
+            ),
+            gr.Slider(
+                minimum=128,
+                maximum=8192,
+                step=1,
+                value=1024,
+                label="Max new tokens",
+                render=False,
+            ),
+            gr.Slider(
+                minimum=0.0,
+                maximum=1.0,
+                step=0.1,
+                value=1.0,
+                label="top_p",
+                render=False,
+            ),
+            gr.Slider(
+                minimum=1,
+                maximum=20,
+                step=1,
+                value=20,
+                label="top_k",
+                render=False,
+            ),
+            gr.Slider(
+                minimum=0.0,
+                maximum=2.0,
+                step=0.1,
+                value=1.2,
+                label="Repetition penalty",
+                render=False,
+            ),
+        ],
+        examples=[
+            ["Help me study vocabulary: write a sentence for me to fill in the blank, and I'll try to pick the correct option."],
+            ["What are 5 creative things I could do with my kids' art? I don't want to throw them away, but it's also so much clutter."],
+            ["Tell me a random fun fact about the Roman Empire."],
+            ["Show me a code snippet of a website's sticky header in CSS and JavaScript."],
+        ],
+        cache_examples=False,
+    )
 
-"""
-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 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(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
 
 if __name__ == "__main__":
     demo.launch()