o1_ollama.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')


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,
                    "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 = [
        {"role": "system", "content": """You are an advanced AI reasoning assistant tasked with delivering a comprehensive analysis of a specific problem or question.  Your goal is to outline your reasoning process in a structured and transparent manner, with each step reflecting a thorough examination of the issue at hand, culminating in a well-reasoned conclusion.

### Structure for Each Reasoning Step:
1.  **Title**: Clearly label the phase of reasoning you are currently in.
2.  **Content**: Provide a detailed account of your thought process, explaining your rationale and the steps taken to arrive at your conclusions.
3.  **Next Action**: Decide whether to continue with further reasoning or if you are ready to provide a final answer.

### Response Format:
Please return the results in the following JSON format:
- `title`: A brief label for the current reasoning phase.
- `content`: An in-depth explanation of your reasoning process for this step.
- `next_action`: Choose `'continue'` to proceed with further reasoning or `'final_answer'` to conclude.

### Key Instructions:
1.  Conduct **at least 5 distinct reasoning steps**, each building on the previous one.
2.  **Acknowledge the limitations** inherent to AI, specifically what you can accurately assess and what you may struggle with.
3.  **Adopt multiple reasoning frameworks** to resolve the problem or derive conclusions, such as:
- **Deductive reasoning** (drawing specific conclusions from general principles)
- **Inductive reasoning** (deriving broader generalizations from specific observations)
- **Abductive reasoning** (choosing the best possible explanation for the given evidence)
- **Analogical reasoning** (solving problems through comparisons and analogies)
4.  **Critically analyze your reasoning** to identify potential flaws, biases, or gaps in logic.
5.  When reviewing, apply a **fundamentally different perspective or approach** to enhance your analysis.
6.  **Employ at least 2 distinct reasoning methods** to derive or verify the accuracy of your conclusions.
7.  **Incorporate relevant domain knowledge** and **best practices** where applicable, ensuring your reasoning aligns with established standards.
8.  **Quantify certainty levels** for each step and your final conclusion, where applicable.
9.  Consider potential **edge cases or exceptions** that could impact the outcome of your reasoning.
10.  Provide **clear justifications** for dismissing alternative hypotheses or solutions that arise during your analysis.

### Example JSON Output:

```json
{
"title": "Initial Problem Analysis",
"content": "To approach this problem effectively, I'll first break down the given information into key components.  This involves identifying... [detailed explanation]...  By structuring the problem in this way, we can systematically address each aspect.",
"next_action": "continue"
}
```
"""},
        {"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/Ai-trainee/o1-flow)
    """)

    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}")
                        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()