multi1/ol1.py

import streamlit as st
import json
import time
import requests  # Add this import for making HTTP requests to Ollama
from dotenv import load_dotenv
import os

# Load environment variables
load_dotenv()

# Get configuration from .env file
OLLAMA_URL = os.getenv('OLLAMA_URL', 'http://localhost:11434')
OLLAMA_MODEL = os.getenv('OLLAMA_MODEL', 'llama3.1:70b')

def make_api_call(messages, max_tokens, is_final_answer=False):
    for attempt in range(3):
        try:
            response = requests.post(
                f"{OLLAMA_URL}/api/chat",
                json={
                    "model": OLLAMA_MODEL,
                    "messages": messages,
                    "stream": False,
                    "format": "json", # important, or most of the time ollama does not generate valid json response
                    "options": {
                        "num_predict": max_tokens,
                        "temperature": 0.2
                    }
                }
            )
            response.raise_for_status()
            return json.loads(response.json()["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 = [ # add two sentences to encourage json format response
        {"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.

Example of a valid JSON response:
```json
{
    "title": "Identifying Key Information",
    "content": "To begin solving this problem, we need to carefully examine the given information and identify the crucial elements that will guide our solution process. This involves...",
    "next_action": "continue"
}```.
You MUST response using the expected json schema, and your response must be valid json. This JSON response is essential for our job.
"""},
        {"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':
            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="ol1 prototype - Ollama version", page_icon="🧠", layout="wide")

    st.title("ol1: Using Ollama 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 Ollama so that the reasoning step is local!

    Forked from [bklieger-groq](https://github.com/bklieger-groq)
    Open source [repository here](https://github.com/tcsenpai/ol1-p1)
    """)

    st.markdown(f"**Current Configuration:**")
    st.markdown(f"- Ollama URL: `{OLLAMA_URL}`")
    st.markdown(f"- Ollama Model: `{OLLAMA_MODEL}`")

    # 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}")
                        # this will not work were there codes in the content
                        #st.markdown(content.replace('\n', '<br>'), unsafe_allow_html=True)
                        st.markdown(content, 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()