app.py

import gradio as gr
from duckduckgo_search import DDGS
from collections import deque
import time
import random

def get_llm_response(prompt, model, max_retries=3):
    for attempt in range(max_retries):
        try:
            return DDGS().chat(prompt, model=model)
        except Exception as e:
            if attempt < max_retries - 1:
                print(f"Error occurred: {e}. Retrying in {2**attempt} seconds...")
                time.sleep(2**attempt + random.random())
            else:
                print(f"Max retries reached. Error: {e}")
                return f"<error>Unable to get response from {model} after {max_retries} attempts.</error>"

def process_message(message, history, analysis_prompt, rethinking_prompt, refinement_prompt):
    conversation_history = deque(maxlen=5)
    for h in history:
        conversation_history.append(f"User: {h[0]}\nEcho-Refraction: {h[1]}")
    
    context = "\n".join(conversation_history)
    
    gpt4o_prompt = f"{analysis_prompt}\n\nConversation history:\n{context}\n\nUser query: {message}\n\nPlease analyze this query and respond accordingly."
    gpt4o_response = get_llm_response(gpt4o_prompt, "gpt-4o-mini")
    yield f"Analysis: {gpt4o_response}"

    if "<error>" in gpt4o_response:
        return

    llama_prompt = f"{rethinking_prompt}\n\nConversation history:\n{context}\n\nOriginal user query: {message}\n\nInitial response: {gpt4o_response}\n\nPlease review and suggest improvements or confirm if satisfactory."
    llama_response = get_llm_response(llama_prompt, "gpt-4o-mini")
    yield f"Analysis: {gpt4o_response}\nRethinking: {llama_response}"

    if "<error>" in llama_response:
        return gpt4o_response

    if "done" not in llama_response.lower():
        final_gpt4o_prompt = f"{refinement_prompt}\n\nConversation history:\n{context}\n\nOriginal user query: {message}\n\nInitial response: {gpt4o_response}\n\nSuggestion: {llama_response}\n\nPlease provide a final response considering the suggestion."
        final_response = get_llm_response(final_gpt4o_prompt, "gpt-4o-mini")
        yield f"Analysis: {gpt4o_response}\nRethinking: {llama_response}\nFinal Response: {final_response}"
        return final_response
    else:
        return gpt4o_response

def respond(message, history, analysis_prompt, rethinking_prompt, refinement_prompt):
    response = ""
    for chunk in process_message(message, history, analysis_prompt, rethinking_prompt, refinement_prompt):
        response = chunk
        yield response
    
    # Extract the final response from the last chunk
    final_response = response.split("Final Response: ")[-1] if "Final Response: " in response else response
    return final_response

analysis_prompt = """
You are Echo-Refraction, an AI assistant tasked with analyzing user queries. Your role is to:
1. Carefully examine the user's input for clarity, completeness, and potential ambiguities.
2. Identify if the query needs refinement or additional information.
3. If refinement is needed, suggest specific improvements or ask clarifying questions.
4. If the query is clear, respond with "Query is clear and ready for processing."
5. Provide a brief explanation of your analysis in all cases.
Enclose your response in <analyzing> tags.
"""

rethinking_prompt = """
You are Echo-Refraction, an advanced AI model responsible for critically evaluating and improving responses. Your task is to:
1. Carefully review the original user query and the initial response.
2. Analyze the response for accuracy, relevance, completeness, and potential improvements.
3. Consider perspectives or approaches that might enhance the response.
4. If you identify areas for improvement:
   a. Clearly explain what aspects need refinement and why.
   b. Provide specific suggestions for how the response could be enhanced.
   c. If necessary, propose additional information or context that could be included.
5. If the initial response is satisfactory and you have no suggestions for improvement, respond with "Done."
Enclose your response in <rethinking> tags.
"""

refinement_prompt = """
You are Echo-Refraction, an AI assistant tasked with providing a final, refined response to the user. Your role is to:
1. Review the original user query, your initial response, and the suggestions provided.
2. Consider the feedback and suggestions for improvement.
3. Integrate the suggested improvements into your response, ensuring that:
   a. The information is accurate and up-to-date.
   b. The response is comprehensive and addresses all aspects of the user's query.
   c. The language is clear, concise, and appropriate for the user's level of understanding.
4. If you disagree with any suggestions, provide a brief explanation of why you chose not to incorporate them.
5. Deliver a final response that represents the best possible answer to the user's query.
Enclose your response in <output> tags.
"""

demo = gr.ChatInterface(
    respond,
    additional_inputs=[
        gr.Textbox(value=analysis_prompt, label="Analysis Prompt", lines=10),
        gr.Textbox(value=rethinking_prompt, label="Rethinking Prompt", lines=10),
        gr.Textbox(value=refinement_prompt, label="Refinement Prompt", lines=10),
    ],
    title="Echo-Refraction AI Assistant",
    description="Chat with Echo-Refraction, an AI assistant that analyzes, rethinks, and refines responses.",
    examples=[
        ["How many 'r' are there in the word 'strawberry'"],
        ["Explain the concept of quantum entanglement."],
        ["How does photosynthesis work?"],
    ],
    cache_examples=False,
)

if __name__ == "__main__":
    demo.launch()