import gradio as gr
import json
from llm.ollama_llm import query_ollama
from llm.rag_pipeline import retrieve_context
from logger import get_logger

logger = get_logger(__name__)

# Function to load latest sensor data
def get_latest_sensor_data(path="data/farm_data_log.json", num_entries=3):
    try:
        with open(path, "r") as f:
            data = json.load(f)
            return data[-num_entries:] if data else []
    except FileNotFoundError:
        logger.error(f"Sensor data file {path} not found.")
        return []
    except json.JSONDecodeError as e:
        logger.error(f"Invalid JSON in {path}: {e}")
        return []

# Global query history
query_history = []

def process_query(user_query):
    """Handles user query and returns response + updated history."""
    if not user_query.strip():
        return "Please enter a question.", "\n".join(format_history())

    logger.info("User query: %s", user_query)
    try:
        # Prepare sensor data
        sensor_data_entries = get_latest_sensor_data()
        combined_sensor_data = {
            entry["timestamp"]: {
                "soil": entry["soil"],
                "water": entry["water"],
                "environment": entry["environment"]
            }
            for entry in sensor_data_entries
        }
        # Retrieve context and query LLM
        rag_context = retrieve_context(user_query)
        response = query_ollama(user_query, combined_sensor_data, rag_context)
        logger.info("--- FARM ASSISTANT RESPONSE ---")

        # Add to query history
        query_history.append((user_query, response))

        return response, format_history()
    except Exception as e:
        logger.error(f"Query processing failed: {e}")
        return "Error: Could not process query. Please try again.", "\n".join(format_history())

def format_history():
    """Format query history as text for display."""
    lines = []
    for i, (q, r) in enumerate(query_history[-5:], start=1):
        lines.append(f"### Query {i}\n**Q:** {q}\n**A:** {r}\n")
    return "\n\n".join(lines)

def clear_history():
    query_history.clear()
    return "", ""  # Clears both output panels

# Show latest sensor data as markdown
def display_sensor_data():
    sensor_data_entries = get_latest_sensor_data()
    if not sensor_data_entries:
        return "No sensor data available."

    latest_entry = sensor_data_entries[-1]
    text = f"""
**Latest Reading: {latest_entry['timestamp']}**

### Soil
- Moisture: {latest_entry['soil']['moisture']}
- pH: {latest_entry['soil']['pH']}
- Temperature: {latest_entry['soil']['temperature']}

### Water
- pH: {latest_entry['water']['pH']}
- Turbidity: {latest_entry['water']['turbidity']}
- Temperature: {latest_entry['water']['temperature']}

### Environment
- Humidity: {latest_entry['environment']['humidity']}
- Temperature: {latest_entry['environment']['temperature']}
- Rainfall: {latest_entry['environment']['rainfall']}
"""
    return text

# Gradio UI
with gr.Blocks(theme=gr.themes.Soft(primary_hue="green")) as demo:
    gr.Markdown("# 🌾 AgriEdge: Smart Farm Assistant")
    gr.Markdown("Ask about your farm's conditions and get tailored advice based on sensor data.")

    with gr.Tab("Ask Assistant"):
        query = gr.Textbox(
            label="Enter your farm-related question",
            placeholder="e.g., What should I do about soil moisture?"
        )
        submit_btn = gr.Button("Submit Query")
        clear_btn = gr.Button("Clear History")

        response_box = gr.Markdown()
        history_box = gr.Markdown()

        submit_btn.click(process_query, inputs=query, outputs=[response_box, history_box])
        clear_btn.click(clear_history, inputs=None, outputs=[response_box, history_box])

    with gr.Tab("Recent Sensor Data"):
        sensor_md = gr.Markdown(display_sensor_data())

demo.launch()