Create app.py

app.py

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+import os
+import streamlit as st
+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",
+    "Meta-Llama-3.1-70B-Instruct",
+    "Meta-Llama-3.1-8B-Instruct"
+]
+
+# Available search strategies
+SEARCH_STRATEGY = [
+    "None",
+    "Greedy-Best-Score",
+    "Iterative-Refinement",
+    "Monte-Carlo-Tree-Search"
+]
+
+def chat_with_ai(message, chat_history, system_prompt):
+    messages = [
+        {"role": "system", "content": system_prompt},
+    ]
+    
+    for human, ai, _ in chat_history:
+        messages.append({"role": "user", "content": human})
+        messages.append({"role": "assistant", "content": ai})
+    
+    messages.append({"role": "user", "content": message})
+    
+    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))
+    response = ""
+    start_time = time.time()
+    with st.spinner("AI is thinking..."):
+        for chunk in client.chat.completions.create(
+            model=model,
+            messages=messages,
+            stream=True
+        ):
+            content = chunk.choices[0].delta.content or ""
+            response += content
+            yield response, time.time() - start_time
+
+def parse_and_display_response(response):
+    # Extract answer and reflection
+    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)
+        
+        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)
+        
+        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")
+        
+        st.session_state.chat_history.append((message, response, elapsed_time))
+    
+    if st.button("Clear Chat"):
+        st.session_state.chat_history = []
+        st.experimental_rerun()
+
+if __name__ == "__main__":
+    main()