Create app.py

app.py

@@ -0,0 +1,146 @@
+import torch
+
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
+import gradio as gr
+from threading import Thread
+
+MODEL = "tiiuae/Falcon-EB-3B-Instruct"
+
+TITLE = "<h1><center>Falcon-E-3B-Instruct playground</center></h1>"
+SUB_TITLE = """<center>This interface has been created for quick validation purposes, do not use it for production.</center>"""
+
+CSS = """
+.duplicate-button {
+    margin: auto !important;
+    color: white !important;
+    background: black !important;
+    border-radius: 100vh !important;
+}
+h3 {
+    text-align: center;
+}
+"""
+
+END_MESSAGE = """
+\n
+**The conversation has reached to its end, please press "Clear" to restart a new conversation**
+"""
+
+device = "cuda" # for GPU usage or "cpu" for CPU usage
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL)
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL,
+    torch_dtype=torch.bfloat16,
+).to(device)
+
+model = torch.compile(model)
+
+def stream_chat(
+    message: str, 
+    history: list, 
+    temperature: float = 0.3, 
+    max_new_tokens: int = 128, 
+    top_p: float = 1.0, 
+    top_k: int = 20, 
+    penalty: float = 1.2,
+):
+    print(f'message: {message}')
+    print(f'history: {history}')
+
+    conversation = []
+    for prompt, answer in history:
+        conversation.extend([
+            {"role": "user", "content": prompt}, 
+            {"role": "assistant", "content": answer},
+        ])
+
+
+    conversation.append({"role": "user", "content": message})
+    input_text = tokenizer.apply_chat_template(conversation, tokenize=False, add_generation_prompt = True)
+        
+    inputs = tokenizer.encode(input_text, return_tensors="pt").to(device)
+    streamer = TextIteratorStreamer(tokenizer, timeout=60.0, skip_prompt=True, skip_special_tokens=True)
+    
+    generate_kwargs = dict(
+        input_ids=inputs, 
+        max_new_tokens = max_new_tokens,
+        do_sample = False if temperature == 0 else True,
+        top_p = top_p,
+        top_k = top_k,
+        temperature = temperature,
+        streamer=streamer,
+        pad_token_id = 10,
+    )
+
+    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
+
+
+    print(f'response: {buffer}')
+            
+chatbot = gr.Chatbot(height=600)
+
+with gr.Blocks(css=CSS, theme="soft") as demo:
+    gr.HTML(TITLE)
+    gr.HTML(SUB_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.Slider(
+                minimum=0,
+                maximum=1,
+                step=0.1,
+                value=0.3,
+                label="Temperature",
+                render=False,
+            ),
+            gr.Slider(
+                minimum=128,
+                maximum=4096,
+                step=1,
+                value=128,
+                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,
+            ),
+        ],
+        cache_examples=False,
+    )
+
+
+if __name__ == "__main__":
+    demo.launch()