Create app.py

app.py

@@ -0,0 +1,103 @@
+import gradio as gr
+import torch
+from threading import Thread
+from typing import Iterator
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
+
+MAX_MAX_NEW_TOKENS = 1024
+MAX_INPUT_TOKEN_LENGTH = 2048
+
+base_model_name = "m-a-p/OpenCodeInterpreter-DS-1.3B"
+model = AutoModelForCausalLM.from_pretrained(base_model_name, torch_dtype=torch.float32, device_map="cpu", low_cpu_mem_usage=True)
+
+tokenizer = AutoTokenizer.from_pretrained(base_model_name)
+
+def format_prompt(message, history):
+    system_prompt = "You are OpenCodeInterpreter, you are an expert programmer that helps to write code based on the user request, with concise explanations."
+    prompt = []
+    prompt.append({"role": "system", "content": system_prompt})
+    for user_prompt, bot_response in history:
+        prompt.extend([{"role": "user", "content": user_prompt}, {"role": "assistant", "content": bot_response}])
+    prompt.append({"role": "user", "content": message})
+    return prompt
+
+def generate(prompt: str, history: list[tuple[str, str]], max_new_tokens: int = 1024, temperature: float = 0.3,
+    top_p: float = 0.9, top_k: int = 50, repetition_penalty: float = 1 ) -> Iterator[str]:
+
+    temperature = float(temperature)
+    if temperature < 1e-2:
+        temperature = 1e-2
+    
+    formatted_prompt = []
+    formatted_prompt = format_prompt(prompt, history)
+    
+    input_ids = tokenizer.apply_chat_template(formatted_prompt, return_tensors="pt", add_generation_prompt=True)
+    if input_ids.shape[1] > MAX_INPUT_TOKEN_LENGTH:
+        input_ids = input_ids[:, -MAX_INPUT_TOKEN_LENGTH:]
+    input_ids = input_ids.to(model.device)
+
+    streamer = TextIteratorStreamer(tokenizer, timeout=15.0, skip_prompt=True, skip_special_tokens=True)
+    generation_kwargs    = dict({"input_ids": input_ids}, streamer=streamer, max_new_tokens=max_new_tokens, do_sample=False, top_p=top_p, top_k=top_k, 
+                              temperature=temperature, num_beams=1, repetition_penalty=repetition_penalty, eos_token_id=tokenizer.eos_token_id)
+
+    t = Thread(target=model.generate, kwargs=generation_kwargs  )
+    t.start()
+   
+    outputs = []
+    for chunk in streamer:
+        outputs.append(chunk)
+        yield "".join(outputs).replace("<|EOT|>","")
+        
+
+mychatbot = gr.Chatbot(layout="bubble", avatar_images=["user.png", "botoci.png"], bubble_full_width=False, show_label=False, show_copy_button=True, likeable=True,)
+
+additional_inputs =  additional_inputs=[
+        gr.Slider(
+            label="Max new tokens",
+            minimum=1,
+            maximum=MAX_MAX_NEW_TOKENS,
+            step=1,
+            value=512,
+        ),
+        gr.Slider(
+            label="Temperature",
+            minimum=0,
+            maximum=1.0,
+            step=0.1,
+            value=0.3,
+        ),
+        gr.Slider(
+            label="Top-p",
+            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,
+        )]
+
+iface  = gr.ChatInterface(fn=generate,
+                        chatbot=mychatbot,
+                        additional_inputs=additional_inputs,
+                        description=" Running on CPU. The response may be slow for cpu environments. 🙏🏻",
+                        retry_btn=None,
+                        undo_btn=None
+                       )
+
+with gr.Blocks() as demo:
+    gr.HTML("<center><h1>Tomoniai's Chat with OpenCodeInterpreter</h1></center>")
+    iface.render()
+    
+demo.queue(max_size=10).launch(show_api=False)