ui.py

"""
Defines the Gradio user interface and manages the application's state
and event handling.

This module is responsible for the presentation layer of the application.
It creates the interactive components and orchestrates the analysis workflow
by calling functions from the data_processing module.
"""


import gradio as gr
import json
import concurrent.futures
import threading
from data_processing import (
    llm_generate_analysis_plan_with_history,
    execute_quantitative_query,
    execute_qualitative_query,
    llm_synthesize_enriched_report_stream,
    llm_generate_visualization_code,
    execute_viz_code_and_get_path,
    parse_suggestions_from_report
)

# Create a lock to protect the Solr client from concurrent access
solr_lock = threading.Lock()


def create_ui(llm_model, solr_client):
    """
    Builds the Gradio UI and wires up all the event handlers.

    Args:
        llm_model: The initialized Google Gemini model client.
        solr_client: The initialized pysolr client.
    """
    with gr.Blocks(theme=gr.themes.Soft(), css="footer {display: none !important}") as demo:
        state = gr.State()

        with gr.Row():
            with gr.Column(scale=4):
                gr.Markdown("# PharmaCircle AI Data Analyst")
            with gr.Column(scale=1):
                clear_button = gr.Button(
                    "🔄 Start New Analysis", variant="primary")

        gr.Markdown("Ask a question to begin your analysis. I will generate an analysis plan, retrieve quantitative and qualitative data, create a visualization, and write an enriched report.")

        with gr.Row():
            with gr.Column(scale=1):
                chatbot = gr.Chatbot(
                    label="Analysis Chat Log", height=700, show_copy_button=True)
                msg_textbox = gr.Textbox(
                    placeholder="Ask a question, e.g., 'Show me the top 5 companies by total deal value in 2023'", label="Your Question", interactive=True)

            with gr.Column(scale=2):
                with gr.Accordion("Dynamic Field Suggestions", open=False):
                    suggestions_display = gr.Markdown(
                        "Suggestions from the external API will appear here...", visible=True)
                with gr.Accordion("Generated Analysis Plan", open=False):
                    plan_display = gr.Markdown(
                        "Plan will appear here...", visible=True)
                with gr.Accordion("Retrieved Quantitative Data", open=False):
                    quantitative_url_display = gr.Markdown(
                        "Quantitative URL will appear here...", visible=False)
                    quantitative_data_display = gr.Markdown(
                        "Aggregate data will appear here...", visible=False)
                with gr.Accordion("Retrieved Qualitative Data (Examples)", open=False):
                    qualitative_url_display = gr.Markdown(
                        "Qualitative URL will appear here...", visible=False)
                    qualitative_data_display = gr.Markdown(
                        "Example data will appear here...", visible=False)
                plot_display = gr.Image(
                    label="Visualization", type="filepath", visible=False)
                report_display = gr.Markdown(
                    "Report will be streamed here...", visible=False)

        def process_analysis_flow(user_input, history, state):
            """
            Manages the conversation and yields UI updates.
            """
            if state is None:
                state = {'query_count': 0, 'last_suggestions': []}
            if history is None:
                history = []

            # Reset all displays at the beginning of a new flow
            yield (history, state, gr.update(value=None, visible=False), gr.update(value=None, visible=False), gr.update(value=None, visible=False), gr.update(value=None, visible=False), gr.update(value=None, visible=False), gr.update(value=None, visible=False), gr.update(value=None, visible=False), gr.update(value="Suggestions from the external API will appear here...", visible=False))

            query_context = user_input.strip()
            if not query_context:
                history.append((user_input, "Please enter a question to analyze."))
                yield (history, state, None, None, None, None, None, None, None, None)
                return

            history.append((user_input, f"Analyzing: '{query_context}'\n\n*Generating analysis plan...*"))
            yield (history, state, None, None, None, None, None, None, None, None)

            # Generate plan, get search field suggestions, and intent.
            analysis_plan, mapped_search_fields, core_name, intent = llm_generate_analysis_plan_with_history(llm_model, query_context, history)

            # Update and display search field suggestions in its own accordion
            if mapped_search_fields:
                suggestions_md = "**API Suggestions (with mappings applied):**\n" + "\n".join([f"- `{field['field_name']}`: `{field['field_value']}`" for field in mapped_search_fields])
                suggestions_display_update = gr.update(value=suggestions_md, visible=True)
            else:
                suggestions_display_update = gr.update(value="No suggestions were returned from the external API.", visible=True)

            if not analysis_plan:
                if intent and intent != 'search_list':
                    message = f"I am sorry, I can only perform analysis for 'search_list' type queries. Your query was identified as a '{intent}', which is not supported."
                else:
                    message = "I'm sorry, I couldn't generate a valid analysis plan. Please try rephrasing your question."
                history.append((None, message))
                yield (history, state, None, None, None, None, None, None, None, suggestions_display_update)
                return

            history.append((None, f"✅ Analysis plan generated for core: **`{core_name}`**"))
            plan_summary = f"""
        *   **Analysis Dimension:** `{analysis_plan.get('analysis_dimension')}`
        *   **Analysis Measure:** `{analysis_plan.get('analysis_measure')}`
        *   **Query Filter:** `{analysis_plan.get('query_filter')}`
        """
            history.append((None, plan_summary))
            formatted_plan = f"**Full Analysis Plan (Core: `{core_name}`):**\n```json\n{json.dumps(analysis_plan, indent=2)}\n```"
            yield (history, state, None, None, gr.update(value=formatted_plan, visible=True), None, None, None, None, suggestions_display_update)

            history.append((None, "*Executing queries for aggregates and examples...*"))
            yield (history, state, None, None, gr.update(value=formatted_plan, visible=True), None, None, None, None, suggestions_display_update)

            # --- DYNAMIC CORE SWITCH (Thread-safe) ---
            with solr_lock:
                original_solr_url = solr_client.url
                # Correctly construct the new URL by replacing the last component (the core name)
                base_url = original_solr_url.rsplit('/', 1)[0]
                new_url = f"{base_url}/{core_name}"
                solr_client.url = new_url
                print(f"[INFO] Switched Solr client to core: {core_name} at URL: {solr_client.url}")

                # Execute queries in parallel
                aggregate_data, quantitative_url = None, None
                example_data, qualitative_url = None, None
                try:
                    with concurrent.futures.ThreadPoolExecutor() as executor:
                        future_agg = executor.submit(execute_quantitative_query, solr_client, analysis_plan)
                        future_ex = executor.submit(execute_qualitative_query, solr_client, analysis_plan)
                        aggregate_data, quantitative_url = future_agg.result()
                        example_data, qualitative_url = future_ex.result()
                finally:
                    # --- IMPORTANT: Reset client to default URL ---
                    solr_client.url = original_solr_url
                    print(f"[INFO] Reset Solr client to default URL: {original_solr_url}")

            if not aggregate_data or aggregate_data.get('count', 0) == 0:
                history.append((None, f"No data was found for your query in the '{core_name}' core. Please try a different question."))
                yield (history, state, None, None, gr.update(value=formatted_plan, visible=True), None, None, None, None, suggestions_display_update)
                return

            # Display retrieved data
            quantitative_url_update = gr.update(value=f"**Solr URL:** [{quantitative_url}]({quantitative_url})", visible=True)
            qualitative_url_update = gr.update(value=f"**Solr URL:** [{qualitative_url}]({qualitative_url})", visible=True)
            formatted_agg_data = f"**Quantitative (Aggregate) Data:**\n```json\n{json.dumps(aggregate_data, indent=2)}\n```"
            formatted_qual_data = f"**Qualitative (Example) Data:**\n```json\n{json.dumps(example_data, indent=2)}\n```"
            qual_data_display_update = gr.update(value=formatted_qual_data, visible=True)
            yield (history, state, None, None, gr.update(value=formatted_plan, visible=True), quantitative_url_update, gr.update(value=formatted_agg_data, visible=True), qualitative_url_update, qual_data_display_update, suggestions_display_update)

            history.append((None, "✅ Data retrieved. Generating visualization and final report..."))
            yield (history, state, None, None, gr.update(value=formatted_plan, visible=True), quantitative_url_update, gr.update(value=formatted_agg_data, visible=True), qualitative_url_update, qual_data_display_update, suggestions_display_update)

            # Generate viz and report
            with concurrent.futures.ThreadPoolExecutor() as executor:
                viz_future = executor.submit(llm_generate_visualization_code, llm_model, query_context, aggregate_data)

                report_text = ""
                stream_history = history[:]
                for chunk in llm_synthesize_enriched_report_stream(llm_model, query_context, aggregate_data, example_data, analysis_plan):
                    report_text += chunk
                    yield (stream_history, state, None, gr.update(value=report_text, visible=True), gr.update(value=formatted_plan, visible=True), quantitative_url_update, gr.update(value=formatted_agg_data, visible=True), qualitative_url_update, qual_data_display_update, suggestions_display_update)

                history.append((None, report_text))

                viz_code = viz_future.result()
                plot_path = execute_viz_code_and_get_path(viz_code, aggregate_data)
                output_plot = gr.update(value=plot_path, visible=True) if plot_path else gr.update(visible=False)
                if not plot_path:
                    history.append((None, "*I was unable to generate a plot for this data.*\n"))

                yield (history, state, output_plot, gr.update(value=report_text), gr.update(value=formatted_plan, visible=True), quantitative_url_update, gr.update(value=formatted_agg_data, visible=True), qualitative_url_update, qual_data_display_update, suggestions_display_update)

            state['query_count'] += 1
            state['last_suggestions'] = parse_suggestions_from_report(report_text)
            next_prompt = "Analysis complete. What would you like to explore next?"
            history.append((None, next_prompt))
            yield (history, state, output_plot, gr.update(value=report_text), gr.update(value=formatted_plan, visible=True), quantitative_url_update, gr.update(value=formatted_agg_data, visible=True), qualitative_url_update, qual_data_display_update, suggestions_display_update)

        def reset_all():
            """Resets the entire UI for a new analysis session."""
            return (
                [],
                None,
                "",
                gr.update(value=None, visible=False),
                gr.update(value=None, visible=False),
                gr.update(value=None, visible=False),
                gr.update(value=None, visible=False),
                gr.update(value=None, visible=False),
                gr.update(value=None, visible=False),
                gr.update(value=None, visible=False),
                gr.update(value=None, visible=False)
            )

        msg_textbox.submit(
            fn=process_analysis_flow,
            inputs=[msg_textbox, chatbot, state],
            outputs=[chatbot, state, plot_display, report_display, plan_display, quantitative_url_display,
                     quantitative_data_display, qualitative_url_display, qualitative_data_display, suggestions_display],
        ).then(
            lambda: gr.update(value=""),
            None,
            [msg_textbox],
            queue=False,
        )

        clear_button.click(
            fn=reset_all,
            inputs=None,
            outputs=[chatbot, state, msg_textbox, plot_display, report_display, plan_display, quantitative_url_display,
                     quantitative_data_display, qualitative_url_display, qualitative_data_display, suggestions_display],
            queue=False
        )

    return demo