Update app.py

app.py

@@ -2,71 +2,85 @@ import os
 import time
 import spaces
 import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer, BitsAndBytesConfig
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
 import gradio as gr
 from threading import Thread
 
 HF_TOKEN = os.environ.get("HF_TOKEN", None)
 MODEL = "AGI-0/Art-v0-3B"
 
-TITLE = """<h2>Link to the model: <a href="https://huggingface.co/AGI-0/Art-v0-3B">click here</a></h2>"""
-
-PLACEHOLDER = """
-<center>
-<p>Hi! How can I help you today?</p>
-</center>
-"""
-
-CSS = """
-.duplicate-button {
-    margin: auto !important;
-    color: white !important;
-    background: black !important;
-    border-radius: 100vh !important;
-}
-h3 {
-    text-align: center;
-}
-"""
-
-device = "cuda"  # for GPU usage or "cpu" for CPU usage
+device = "cuda"  # Use "cpu" if no GPU available
 
 tokenizer = AutoTokenizer.from_pretrained(MODEL)
 model = AutoModelForCausalLM.from_pretrained(
-    MODEL,
-    torch_dtype=torch.bfloat16,
-    device_map="auto")
+    MODEL, torch_dtype=torch.bfloat16, device_map="auto"
+)
 end_of_sentence = tokenizer.convert_tokens_to_ids("<|im_end|>")
 
+class ConversationManager:
+    def __init__(self):
+        self.user_history = []  # User-facing history with formatting
+        self.model_history = []  # Model-facing history without formatting
+
+    def add_exchange(self, user_message, model_response):
+        formatted_response = self.format_response(model_response)
+        self.model_history.append((user_message, model_response))
+        self.user_history.append((user_message, formatted_response))
+        print(f"\nModel History Updated: {self.model_history}")
+        print(f"\nUser History Updated: {self.user_history}")
+
+    def format_response(self, response):
+        """Format response for UI while keeping raw text for model."""
+        if "<|end_reasoning|>" in response:
+            parts = response.split("<|end_reasoning|>")
+            reasoning, rest = parts[0], parts[1] if len(parts) > 1 else ""
+            return f"<details><summary>Click to see reasoning</summary>\n\n{reasoning}\n\n</details>\n\n{rest}"
+        return response
+
+    def get_user_history(self):
+        return self.user_history
+
+    def get_model_history(self):
+        return self.model_history
+
+conversation_manager = ConversationManager()
+
 @spaces.GPU()
 def stream_chat(
-    message: str, 
+    message: str,
     history: list,
-    system_prompt: str = "",  # Include system prompt
-    temperature: float = 0.2, 
-    max_new_tokens: int = 4096, 
-    top_p: float = 1.0, 
-    top_k: int = 1, 
+    system_prompt: str,
+    temperature: float = 0.2,
+    max_new_tokens: int = 4096,
+    top_p: float = 1.0,
+    top_k: int = 1,
     penalty: float = 1.1,
 ):
-    print(f'message: {message}')
-    print(f'history: {history}')
-
+    print(f'User Message: {message}')
+    
+    model_history = conversation_manager.get_model_history()
+    print(f'Model History: {model_history}')
+    
     conversation = []
-    for prompt, answer in history:
+    for prompt, answer in model_history:
         conversation.extend([
-            {"role": "user", "content": prompt}, 
+            {"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)
+    print(f'Formatted Conversation for Model: {conversation}')
     
+    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, 
+        input_ids=input_ids,
         max_new_tokens=max_new_tokens,
         do_sample=False if temperature == 0 else True,
         top_p=top_p,
@@ -77,43 +91,37 @@ def stream_chat(
         streamer=streamer,
     )
 
+    buffer = ""
+    original_response = ""
+
     with torch.no_grad():
         thread = Thread(target=model.generate, kwargs=generate_kwargs)
         thread.start()
-    
-    buffer = ""
-    user_buffer = ""
-    found_token = False
-    
-    for new_text in streamer:
-        buffer += new_text
-        user_buffer += new_text
 
-        if "<|end_reasoning|>" in user_buffer and not found_token:
-            parts = user_buffer.split("<|end_reasoning|>")
-            reasoning = parts[0]
-            rest = parts[1] if len(parts) > 1 else ""
-            user_buffer = f"<details><summary>Click to see reasoning</summary>\n\n{reasoning}\n\n</details>\n\n{rest}"
-            found_token = True
-            
-        yield user_buffer
+        for new_text in streamer:
+            buffer += new_text
+            original_response += new_text
+            print(f'Streaming: {buffer}')
+            formatted_buffer = conversation_manager.format_response(buffer)
+            yield formatted_buffer, history + [[message, formatted_buffer]]
 
-    history.append((message, buffer)) # Crucial: Append the original buffer
 
+    conversation_manager.add_exchange(message, original_response)
 
-chatbot = gr.Chatbot(height=600, placeholder=PLACEHOLDER)
+chatbot = gr.Chatbot(height=600, placeholder="<center><p>Hi! How can I help you today?</p></center>")
 
-with gr.Blocks(css=CSS, theme="soft") as demo:
-    gr.HTML(TITLE)
+demo = gr.Blocks()
+with demo:
+    gr.HTML("<h2>Link to the model: <a href='https://huggingface.co/AGI-0/Art-v0-3B'>click here</a></h2>")
     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), # Added system prompt textbox here
+        additional_inputs_accordion=gr.Accordion(label="⚙️ Parameters", open=False, render=False),
         additional_inputs=[
-           gr.Textbox(value="", label="System Prompt", lines=2, render=False), # Added system prompt textbox
-           gr.Slider(minimum=0, maximum=1, step=0.1, value=0.2, label="Temperature", render=False),
+            gr.Textbox(value="", label="System Prompt", render=False),
+            gr.Slider(minimum=0, maximum=1, step=0.1, value=0.2, label="Temperature", render=False),
             gr.Slider(minimum=128, maximum=8192, step=1, value=4096, 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=50, step=1, value=1, label="top_k", render=False),