app.py

import streamlit as st
import groq
import os
import json
import time

def get_groq_client():
    api_key = os.environ.get("GROQ_API_KEY")
    if not api_key:
        api_key = st.session_state.get('groq_api_key')
    
    if api_key:
        return groq.Groq(api_key=api_key)
    return None

def set_api_key():
    st.session_state['groq_api_key'] = st.session_state.api_key_input
    st.rerun()  # Updated from experimental_rerun() to rerun()

client = get_groq_client()

def make_api_call(messages, max_tokens, is_final_answer=False):
    for attempt in range(3):
        try:
            response = client.chat.completions.create(
                model="llama-3.1-70b-versatile",
                messages=messages,
                max_tokens=max_tokens,
                temperature=0.2,
                response_format={"type": "json_object"}
            )
            return json.loads(response.choices[0].message.content)
        except Exception as e:
            if attempt == 2:
                if is_final_answer:
                    return {"title": "Error", "content": f"Failed to generate final answer after 3 attempts. Error: {str(e)}"}
                else:
                    return {"title": "Error", "content": f"Failed to generate step after 3 attempts. Error: {str(e)}", "next_action": "final_answer"}
            time.sleep(1)  # Wait for 1 second before retrying

def generate_response(prompt):
    messages = [
        {"role": "system", "content": """You are an expert AI assistant that explains your reasoning step by step. For each step, provide a title that describes what you're doing in that step, along with the content. Decide if you need another step or if you're ready to give the final answer. Respond in JSON format with 'title', 'content', and 'next_action' (either 'continue' or 'final_answer') keys. USE AS MANY REASONING STEPS AS POSSIBLE. AT LEAST 3. BE AWARE OF YOUR LIMITATIONS AS AN LLM AND WHAT YOU CAN AND CANNOT DO. IN YOUR REASONING, INCLUDE EXPLORATION OF ALTERNATIVE ANSWERS. CONSIDER YOU MAY BE WRONG, AND IF YOU ARE WRONG IN YOUR REASONING, WHERE IT WOULD BE. FULLY TEST ALL OTHER POSSIBILITIES. YOU CAN BE WRONG. WHEN YOU SAY YOU ARE RE-EXAMINING, ACTUALLY RE-EXAMINE, AND USE ANOTHER APPROACH TO DO SO. DO NOT JUST SAY YOU ARE RE-EXAMINING. USE AT LEAST 3 METHODS TO DERIVE THE ANSWER. USE BEST PRACTICES."""},
        {"role": "user", "content": prompt},
        {"role": "assistant", "content": "Thank you! I will now think step by step following my instructions, starting at the beginning after decomposing the problem."}
    ]
    
    steps = []
    step_count = 1
    total_thinking_time = 0
    
    while True:
        start_time = time.time()
        step_data = make_api_call(messages, 300)
        end_time = time.time()
        thinking_time = end_time - start_time
        total_thinking_time += thinking_time
        
        steps.append((f"Step {step_count}: {step_data['title']}", step_data['content'], thinking_time))
        
        messages.append({"role": "assistant", "content": json.dumps(step_data)})
        
        if step_data['next_action'] == 'final_answer' or step_count > 25: # Maximum of 25 steps to prevent infinite thinking time. Can be adjusted.
            break
        
        step_count += 1

        # Yield after each step for Streamlit to update
        yield steps, None  # We're not yielding the total time until the end

    # Generate final answer
    messages.append({"role": "user", "content": "Please provide the final answer based on your reasoning above."})
    
    start_time = time.time()
    final_data = make_api_call(messages, 200, is_final_answer=True)
    end_time = time.time()
    thinking_time = end_time - start_time
    total_thinking_time += thinking_time
    
    steps.append(("Final Answer", final_data['content'], thinking_time))

    yield steps, total_thinking_time

def main():
    st.set_page_config(page_title="g1 prototype", page_icon="🧠", layout="wide")
    
    st.title("g1: Using Llama-3.1 70b on Groq to create o1-like reasoning chains")
    
    st.markdown("""
    This is an early prototype of using prompting to create o1-like reasoning chains to improve output accuracy. It is not perfect and accuracy has yet to be formally evaluated. It is powered by Groq so that the reasoning step is fast!
                
    Open source [repository here](https://github.com/bklieger-groq)
    """)
    
    if not client:
        st.warning("No Groq API key found. Please enter your API key below or add it to the Hugging Face Space secrets.")
        api_key_input = st.text_input("Enter your Groq API key:", type="password", key="api_key_input")
        st.button("Submit API Key", on_click=set_api_key)
        
        st.markdown("""
        To add your API key to the Hugging Face Space secrets:
        1. Go to the Settings tab of this Space
        2. Scroll down to the "Repository secrets" section
        3. Click on "New secret"
        4. Set the secret name as `GROQ_API_KEY`
        5. Paste your Groq API key as the value
        6. Click "Add secret"
        7. Rebuild the Space
        """)
        st.stop()
    
    # Text input for user query
    user_query = st.text_input("Enter your query:", placeholder="e.g., How many 'R's are in the word strawberry?")
    
    if user_query:
        st.write("Generating response...")
        
        # Create empty elements to hold the generated text and total time
        response_container = st.empty()
        time_container = st.empty()
        
        # Generate and display the response
        for steps, total_thinking_time in generate_response(user_query):
            with response_container.container():
                for i, (title, content, thinking_time) in enumerate(steps):
                    if title.startswith("Final Answer"):
                        st.markdown(f"### {title}")
                        st.markdown(content.replace('\n', '<br>'), unsafe_allow_html=True)
                    else:
                        with st.expander(title, expanded=True):
                            st.markdown(content.replace('\n', '<br>'), unsafe_allow_html=True)
            
            # Only show total time when it's available at the end
            if total_thinking_time is not None:
                time_container.markdown(f"**Total thinking time: {total_thinking_time:.2f} seconds**")

if __name__ == "__main__":
    main()