Update app.py

app.py

@@ -1,63 +1,113 @@
+import time
 import gradio as gr
-from huggingface_hub import InferenceClient
+from os import getenv
+from openai import OpenAI
 
+client = OpenAI(
+    base_url="https://openrouter.ai/api/v1",
+    api_key=getenv("OPENROUTER_API_KEY"),
+)
+
+css = """
+.thought {
+    opacity: 0.8; 
+    font-family: "Courier New", monospace;
+    border: 1px gray solid;
+    padding: 10px;
+    border-radius: 5px;
+}
 """
-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")
 
+js = """
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
+"""
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+with open("contemplator.txt", "r") as f:
+    system_msg = f.read()
 
-    messages.append({"role": "user", "content": message})
+def streaming(message, history, system_msg, model):
+    messages = [
+        {
+            "role": "system",
+            "content": system_msg
+        }
+    ]
+    for user, assistant in history:
+        messages.append({
+            "role": "user",
+            "content": user
+        })
+        messages.append({
+            "role": "assistant",
+            "content": assistant
+        })
 
-    response = ""
+    messages.append({
+        "role": "user",
+        "content": message
+    })
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
+    completion = client.chat.completions.create(
+        model=model,
+        messages=messages,
+        max_completion_tokens=100000,
         stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
+    )
+    
+    reply = ""
+    
+    start_time = time.time()
+    for i, chunk in enumerate(completion):
+        reply += chunk.choices[0].delta.content
+        answer = ""
+        if not "</inner_thoughts>" in reply:
+            thought_text = f'<div class="thought">{reply.replace("<inner_thoughts>", "").strip()}</div>'
+        else:
+            thought_text = f'<div class="thought">{reply.replace("<inner_thoughts>", "").split("</inner_thoughts>")[0].strip()}</div>'
+            answer = reply.split("</inner_thoughts>")[1].replace("<final_answer>", "").replace("</final_answer>", "").strip()
+        thinking_prompt = "<p>" + "Thinking" + "." * (i % 5 + 1) + "</p>"
+        yield thinking_prompt + thought_text + "<br>" + answer
 
-        response += token
-        yield response
+    thinking_prompt = f"<p>Thought for {time.time() - start_time:.2f} seconds</p>"
+    yield thinking_prompt + thought_text + "<br>" + answer
+        
+markdown = """
+## 🫐 Overthink 1(o1)
 
+Insprired by how o1 works, this LLM is instructed to generate very long and detailed chain-of-thoughts. It will think extra hard before providing an answer. 
+
+Actually this does help with reasoning, compared to normal step-by-step reasoning. I wrote a blog post about this [here](https://huggingface.co/blog/wenbopan/recreating-o1).
+
+Sometimes this LLM overthinks for super simple questions, but it's fun to watch. Hope you enjoy it!
+
+### System Message
+
+This is done by instructing the model with a large system message, which you can check on the top tab.
 """
-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)",
-        ),
-    ],
-)
 
+with gr.Blocks(theme=gr.themes.Soft(), css=css, fill_height=True) as demo:
+    with gr.Row(equal_height=True):
+        with gr.Column(scale=1, min_width=300):
+            with gr.Tab("Settings"):
+                gr.Markdown(markdown)
+                model = gr.Dropdown(["nousresearch/hermes-3-llama-3.1-405b:free", "nousresearch/hermes-3-llama-3.1-70b", "meta-llama/llama-3.1-405b-instruct"], value="nousresearch/hermes-3-llama-3.1-405b:free", label="Model")
+                show_thoughts = gr.Checkbox(True, label="Show Thoughts", interactive=True)
+            with gr.Tab("System Message"):
+                system_msg = gr.TextArea(system_msg, label="System Message")
+        with gr.Column(scale=3, min_width=300):
+            gr.ChatInterface(
+                streaming, 
+                additional_inputs=[
+                    system_msg,
+                    model
+                ],
+                examples=[
+                    ["How do you do?    ", None, None, None],
+                    ["How many R's in strawberry?", None, None, None],
+                    ["Solve the puzzle of 24 points: 2 4 9 1", None, None, None],
+                    ["Find x such that ⌈x⌉ + x = 23/7. Express x as a common fraction.", None, None, None],
+                ],
+            )
 
 if __name__ == "__main__":
     demo.launch()