app.py

import gradio as gr
from modules.input_handler import validate_input
from modules.retriever import perform_search
from modules.context_enhancer import add_weather_context, add_space_weather_context
from modules.analyzer import analyze_with_model
from modules.formatter import format_output
from modules.citation import generate_citations
from modules.visualizer import render_output
from modules.server_cache import get_cached_result, cache_result
from modules.status_logger import log_request

def research_assistant(query):
    log_request("Research started", query=query)

    # Check cache first
    cached = get_cached_result(query)
    if cached:
        log_request("Cache hit", query=query)
        return cached

    # Input validation
    validated_query = validate_input(query)

    # Context enhancement
    weather_data = add_weather_context()
    space_weather_data = add_space_weather_context()

    # Web search
    search_results = perform_search(validated_query)

    # Combine context
    enriched_input = f"{validated_query}\n\nWeather: {weather_data}\nSpace Weather: {space_weather_data}\n\nSearch Results:\n{search_results}"

    # LLM Analysis
    analysis = analyze_with_model(enriched_input)

    # Formatting and citations
    formatted_output = format_output(analysis)
    citations = generate_citations(search_results)

    # Final output
    final_output = render_output(formatted_output, citations)

    # Cache result
    cache_result(query, final_output)

    log_request("Research completed", result_length=len(final_output))
    return final_output

# Gradio Interface
demo = gr.Interface(
    fn=research_assistant,
    inputs=gr.Textbox(label="Enter your research question"),
    outputs=gr.Markdown(label="Research Summary"),
    title="AI Research Assistant",
    description="An AI-powered research assistant that gathers and analyzes information with web search, weather, and space weather context.",
    allow_flagging="never"
)

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