use gradio

app.py

@@ -1,17 +1,8 @@
-import os
-import streamlit as st
+import gradio as gr
 from openai import OpenAI
 import time
 import re
 
-# Set up API key
-API_KEY = os.getenv("API_KEY")
-URL = os.getenv("URL")
-client = OpenAI(
-    api_key=API_KEY,
-    base_url=URL
-)
-
 # Available models
 MODELS = [
     "Meta-Llama-3.1-405B-Instruct",
@@ -19,20 +10,18 @@ MODELS = [
     "Meta-Llama-3.1-8B-Instruct"
 ]
 
-# Available search strategies
-SEARCH_STRATEGY = [
-    "None",
-    "Greedy-Best-Score",
-    "Iterative-Refinement",
-    "Monte-Carlo-Tree-Search"
-]
+def create_client(api_key, base_url):
+    return OpenAI(
+        api_key=api_key,
+        base_url=base_url
+    )
 
 def chat_with_ai(message, chat_history, system_prompt):
     messages = [
         {"role": "system", "content": system_prompt},
     ]
     
-    for human, ai, _ in chat_history:
+    for human, ai in chat_history:
         messages.append({"role": "user", "content": human})
         messages.append({"role": "assistant", "content": ai})
     
@@ -40,11 +29,13 @@ def chat_with_ai(message, chat_history, system_prompt):
     
     return messages
 
-def respond(message, chat_history, model, system_prompt, thinking_budget):
-    messages = chat_with_ai(message, chat_history, system_prompt.format(budget = thinking_budget))
+def respond(message, chat_history, model, system_prompt, thinking_budget, api_key, base_url):
+    client = create_client(api_key, base_url)
+    messages = chat_with_ai(message, chat_history, system_prompt.format(budget=thinking_budget))
     response = ""
     start_time = time.time()
-    with st.spinner("AI is thinking..."):
+    
+    try:
         for chunk in client.chat.completions.create(
             model=model,
             messages=messages,
@@ -53,147 +44,113 @@ def respond(message, chat_history, model, system_prompt, thinking_budget):
             content = chunk.choices[0].delta.content or ""
             response += content
             yield response, time.time() - start_time
+    except Exception as e:
+        yield f"Error: {str(e)}", time.time() - start_time
 
-def parse_and_display_response(response):
-    # Extract answer and reflection
+def parse_response(response):
     answer_match = re.search(r'<answer>(.*?)</answer>', response, re.DOTALL)
     reflection_match = re.search(r'<reflection>(.*?)</reflection>', response, re.DOTALL)
     
     answer = answer_match.group(1).strip() if answer_match else ""
     reflection = reflection_match.group(1).strip() if reflection_match else ""
     
-    # Remove answer, reflection, and final reward from the main response
-    response = re.sub(r'<answer>.*?</answer>', '', response, flags=re.DOTALL)
-    response = re.sub(r'<reflection>.*?</reflection>', '', response, flags=re.DOTALL)
-    response = re.sub(r'<reward>.*?</reward>\s*$', '', response, flags=re.DOTALL)
-    
-    # Extract and display steps
     steps = re.findall(r'<step>(.*?)</step>', response, re.DOTALL)
     
-    with st.expander("Show thinking process", expanded=False):
-        for i, step in enumerate(steps, 1):
-            st.markdown(f"**Step {i}:**")
-            st.write(step.strip())
-            st.markdown("---")
-    
-    # Display answer and reflection
-    if answer:
-        st.markdown("### Answer:")
-        st.write(answer)
-    
-    if reflection:
-        st.markdown("### Reflection:")
-        st.write(reflection)
-
-def display_message_with_code_blocks(message):
-    # First, check if the message contains the special tags
-    if '<step>' in message or '<answer>' in message or '<reflection>' in message:
-        parse_and_display_response(message)
-    else:
-        # If not, use the original display logic
-        parts = re.split(r'(```[\s\S]*?```)', message)
+    return answer, reflection, steps
+
+def process_chat(message, history, model, system_prompt, thinking_budget, api_key, base_url):
+    if not api_key or not base_url:
+        history.append((message, "Please provide both API Key and Base URL before starting the chat."))
+        return history, history
+
+    full_response = ""
+    thinking_time = 0
+    
+    for response, elapsed_time in respond(message, history, model, system_prompt, thinking_budget, api_key, base_url):
+        full_response = response
+        thinking_time = elapsed_time
+    
+    if full_response.startswith("Error:"):
+        history.append((message, full_response))
+        return history, history
+
+    answer, reflection, steps = parse_response(full_response)
+    
+    formatted_response = f"**Answer:** {answer}\n\n**Reflection:** {reflection}\n\n**Thinking Steps:**\n"
+    for i, step in enumerate(steps, 1):
+        formatted_response += f"**Step {i}:** {step}\n"
+    
+    formatted_response += f"\n**Thinking time:** {thinking_time:.2f} s"
+    
+    history.append((message, formatted_response))
+    return history, history
+
+with gr.Blocks() as demo:
+    gr.Markdown("# Llama3.1-Instruct-O1")
+    gr.Markdown("[Powered by Llama3.1 models through SN Cloud](https://sambanova.ai/fast-api?api_ref=907266)")
+    
+    with gr.Row():
+        api_key = gr.Textbox(label="API Key", type="password")
+        base_url = gr.Textbox(label="Base URL", value="https://api.endpoints.anyscale.com/v1")
+    
+    with gr.Row():
+        model = gr.Dropdown(choices=MODELS, label="Select Model", value=MODELS[0])
+        thinking_budget = gr.Slider(minimum=1, maximum=100, value=1, step=1, label="Thinking Budget")
+    
+    system_prompt = gr.Textbox(
+        label="System Prompt",
+        value="""
+        You are a helpful assistant in normal conversation.
+        When given a problem to solve, you are an expert problem-solving assistant. Your task is to provide a detailed, step-by-step solution to a given question. Follow these instructions carefully:
+
+        1. Read the given question carefully and reset counter between <count> and </count> to {budget}
+        2. Generate a detailed, logical step-by-step solution.
+        3. Enclose each step of your solution within <step> and </step> tags.
+        4. You are allowed to use at most {budget} steps (starting budget), keep track of it by counting down within tags <count> </count>, STOP GENERATING MORE STEPS when hitting 0, you don't have to use all of them.
+        5. Do a self-reflection when you are unsure about how to proceed, based on the self-reflection and reward, decides whether you need to return to the previous steps.
+        6. After completing the solution steps, reorganize and synthesize the steps into the final answer within <answer> and </answer> tags.
+        7. Provide a critical, honest and subjective self-evaluation of your reasoning process within <reflection> and </reflection> tags.
+        8. Assign a quality score to your solution as a float between 0.0 (lowest quality) and 1.0 (highest quality), enclosed in <reward> and </reward> tags.
+
+        Example format:            
+        <count> [starting budget] </count>
         
-        for part in parts:
-            if part.startswith('```') and part.endswith('```'):
-                # This is a code block
-                code = part.strip('`').strip()
-                lang = code.split('\n')[0] if '\n' in code else ''
-                code = '\n'.join(code.split('\n')[1:]) if lang else code
-                st.code(code, language=lang, line_numbers=True)
-            else:
-                # This is regular text
-                st.write(part)
-
-def main():
-    st.set_page_config(page_title="AI Chatbot", layout="wide")
-    
-    st.title("Llama3.1-Instruct-O1")
-    st.markdown("<a href='https://sambanova.ai/fast-api?api_ref=907266' target='_blank'>Powered by Llama3.1 models through SN Cloud</a>", unsafe_allow_html=True)
-    
-    if "chat_history" not in st.session_state:
-        st.session_state.chat_history = []
-    
-    col1, col2 = st.columns([1, 1])
-    
-    with col1:
-        model = st.selectbox("Select Model", MODELS, index=0)
-        thinking_budget = st.slider("Thinking Budget", 1, 100, 1, help="Control how much it thinks, pick between 1 to 100 inclusive")
-    
-    with col2:
-        system_prompt = st.text_area(
-            "System Prompt",
-            value="""
-            You are a helpful assistant in normal conversation.
-            When given a problem to solve, you are an expert problem-solving assistant. Your task is to provide a detailed, step-by-step solution to a given question. Follow these instructions carefully:
-
-            1. Read the given question carefully and reset counter between <count> and </count> to {budget}
-            2. Generate a detailed, logical step-by-step solution.
-            3. Enclose each step of your solution within <step> and </step> tags.
-            4. You are allowed to use at most {budget} steps (starting budget), keep track of it by counting down within tags <count> </count>, STOP GENERATING MORE STEPS when hitting 0, you don't have to use all of them.
-            5. Do a self-reflection when you are unsure about how to proceed, based on the self-reflection and reward, decides whether you need to return to the previous steps.
-            6. After completing the solution steps, reorganize and synthesize the steps into the final answer within <answer> and </answer> tags.
-            7. Provide a critical, honest and subjective self-evaluation of your reasoning process within <reflection> and </reflection> tags.
-            8. Assign a quality score to your solution as a float between 0.0 (lowest quality) and 1.0 (highest quality), enclosed in <reward> and </reward> tags.
-
-            Example format:            
-            <count> [starting budget] </count>
-            
-            <step> [Content of step 1] </step>
-            <count> [remaining budget] </count>
-
-            <step> [Content of step 2] </step>
-            <reflection> [Evaluation of the steps so far] </reflection>
-            <reward> [Float between 0.0 and 1.0] </reward>
-            <count> [remaining budget] </count>
-
-            <step> [Content of step 3 or Content of some previous step] </step>
-            <count> [remaining budget] </count>
-            
-            ...
-
-            <step>  [Content of final step] </step>
-            <count> [remaining budget] </count>
-            
-            <answer> [Final Answer] </answer>
-
-            <reflection> [Evaluation of the solution] </reflection>
-
-            <reward> [Float between 0.0 and 1.0] </reward>
-            """,
-            height=200
-        )
-    
-    st.markdown("---")
-    
-    for human, ai, thinking_time in st.session_state.chat_history:
-        with st.chat_message("human"):
-            st.write(human)
-        with st.chat_message("ai"):
-            display_message_with_code_blocks(ai)
-            st.caption(f"Thinking time: {thinking_time:.2f} s")
-    
-    message = st.chat_input("Type your message here...")
-    
-    if message:
-        with st.chat_message("human"):
-            st.write(message)
+        <step> [Content of step 1] </step>
+        <count> [remaining budget] </count>
+
+        <step> [Content of step 2] </step>
+        <reflection> [Evaluation of the steps so far] </reflection>
+        <reward> [Float between 0.0 and 1.0] </reward>
+        <count> [remaining budget] </count>
+
+        <step> [Content of step 3 or Content of some previous step] </step>
+        <count> [remaining budget] </count>
         
-        with st.chat_message("ai"):
-            response_placeholder = st.empty()
-            time_placeholder = st.empty()
-            for response, elapsed_time in respond(message, st.session_state.chat_history, model, system_prompt, thinking_budget):
-                response_placeholder.markdown(response)
-                time_placeholder.caption(f"Thinking time: {elapsed_time:.2f} s")
-            response_placeholder.empty()
-            time_placeholder.empty()
-            display_message_with_code_blocks(response)
-            time_placeholder.caption(f"Thinking time: {elapsed_time:.2f} s")
+        ...
+    
+        <step>  [Content of final step] </step>
+        <count> [remaining budget] </count>
         
-        st.session_state.chat_history.append((message, response, elapsed_time))
+        <answer> [Final Answer] </answer>
+
+        <reflection> [Evaluation of the solution] </reflection>
+
+        <reward> [Float between 0.0 and 1.0] </reward>
+        """,
+        lines=10
+    )
     
-    if st.button("Clear Chat"):
-        st.session_state.chat_history = []
-        st.rerun()
+    chatbot_ui = gr.Chatbot()
+    msg = gr.Textbox(label="Type your message here...")
+    clear = gr.Button("Clear Chat")
+
+    chat_history = gr.State([])
+
+    msg.submit(
+        process_chat,  # Use the renamed function
+        [msg, chat_history, model, system_prompt, thinking_budget, api_key, base_url],
+        [chatbot_ui, chat_history]
+    )
+    clear.click(lambda: ([], []), None, [chatbot_ui, chat_history], queue=False)
 
-if __name__ == "__main__":
-    main()
+demo.launch()