"""
Gradio web app for Crop Recommendation System (compatible with gradio==3.50.0)

- Avoids using Gradio args that differ across versions.
- If model.pkl or model_meta.json don't exist, creates a small dummy sklearn pipeline
  and saves it so the Space will start and you can test the UI.
"""

import json
import joblib
import numpy as np
import pandas as pd
import traceback
from pathlib import Path

# sklearn imports (requirements pinned above)
from sklearn.ensemble import RandomForestClassifier
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split

import gradio as gr

MODEL_PATH = Path("model.pkl")
META_PATH = Path("model_meta.json")

# Default label classes for dummy model
_DEFAULT_LABELS = ["rice", "maize", "chickpea", "cotton", "wheat"]
_DEFAULT_NUMERIC_COLS = ["N", "P", "K", "temperature", "humidity", "ph", "rainfall"]

def build_and_save_dummy_model(model_path: Path, meta_path: Path):
    """
    Build a tiny RandomForest pipeline trained on synthetic data and save to disk.
    This ensures the Space starts even without a user model.
    """
    # create synthetic data (reasonable ranges similar to UI sliders)
    rng = np.random.RandomState(42)
    n_samples = 500

    # features: N,P,K,temperature,humidity,ph,rainfall
    X = pd.DataFrame({
        "N": rng.randint(0, 141, size=n_samples),
        "P": rng.randint(5, 146, size=n_samples),
        "K": rng.randint(5, 206, size=n_samples),
        "temperature": rng.uniform(8, 44, size=n_samples),
        "humidity": rng.randint(14, 101, size=n_samples),
        "ph": rng.uniform(3.5, 10, size=n_samples),
        "rainfall": rng.uniform(20, 300, size=n_samples)
    })

    # create target with some synthetic (but deterministic) rule-ish mapping
    # produce labels 0..len(_DEFAULT_LABELS)-1
    # Basic heuristic: high rainfall -> rice, high temp -> cotton/maize, etc.
    y = []
    for i, row in X.iterrows():
        if row["rainfall"] > 1500:
            y.append(0)  # rice
        elif row["temperature"] > 30 and row["rainfall"] < 800:
            y.append(3)  # cotton
        elif row["ph"] < 5.5:
            y.append(2)  # chickpea
        elif row["N"] > 80:
            y.append(1)  # maize
        else:
            y.append(4)  # wheat

    y = np.array(y)

    # simple pipeline
    pipeline = Pipeline([
        ("scaler", StandardScaler()),
        ("clf", RandomForestClassifier(n_estimators=50, random_state=42))
    ])

    pipeline.fit(X, y)

    # Save model and meta
    joblib.dump(pipeline, model_path)
    meta = {
        "numeric_cols": _DEFAULT_NUMERIC_COLS,
        # store label_classes as list for predictable indexing
        "label_classes": _DEFAULT_LABELS
    }
    with open(meta_path, "w", encoding="utf-8") as f:
        json.dump(meta, f, indent=2)

    return pipeline, meta

def load_model_and_meta():
    """
    Try to load model and meta from disk. If not present, build a dummy model and save it.
    Returns (model, meta, load_error_message_or_None)
    """
    load_error = None
    try:
        if not MODEL_PATH.exists() or not META_PATH.exists():
            # build & save dummy model so app can run
            model, meta = build_and_save_dummy_model(MODEL_PATH, META_PATH)
            return model, meta, None

        model = joblib.load(MODEL_PATH)
        with open(META_PATH, "r", encoding="utf-8") as f:
            meta = json.load(f)

        # Validate meta
        if "numeric_cols" not in meta or "label_classes" not in meta:
            raise KeyError("model_meta.json must contain 'numeric_cols' and 'label_classes' keys.")

        return model, meta, None
    except Exception as e:
        load_error = f"{type(e).__name__}: {e}\n\n{traceback.format_exc()}"
        # Create fallback dummy objects (in-memory) so UI can still run and show the error
        fallback_model, fallback_meta = build_and_save_dummy_model(MODEL_PATH, META_PATH)
        return fallback_model, fallback_meta, load_error

# Load or create model & meta
_model, _meta, _load_error = load_model_and_meta()

# Normalize label_classes into an indexable list
label_classes = _meta.get("label_classes", _DEFAULT_LABELS)
if isinstance(label_classes, dict):
    # If it's a dict mapping strings to labels, convert to list by sorting keys when possible
    try:
        # try to convert numeric-string keys to int index order
        items = sorted(label_classes.items(), key=lambda kv: int(kv[0]) if str(kv[0]).isdigit() else kv[0])
        label_list = [v for k, v in items]
    except Exception:
        # fallback: just take values
        label_list = list(label_classes.values())
    label_classes = label_list
elif isinstance(label_classes, list):
    label_classes = label_classes
else:
    # other types: force to list of strings
    label_classes = [str(x) for x in label_classes]

numeric_cols = _meta.get("numeric_cols", _DEFAULT_NUMERIC_COLS)

def predict_crop(N, P, K, temperature, humidity, ph, rainfall):
    """
    Predict crop recommendation and top-3 with confidences.

    Returns: (recommended_crop_str, confidence_str, top3_dict)
    """
    if _load_error:
        # Informative response so UI shows the saved error
        return "Model load warning", "N/A", {"warning": _load_error}

    try:
        input_df = pd.DataFrame({
            "N": [N],
            "P": [P],
            "K": [K],
            "temperature": [temperature],
            "humidity": [humidity],
            "ph": [ph],
            "rainfall": [rainfall]
        })

        # Predict
        pred_enc = _model.predict(input_df)[0]

        # probabilities: handle models without predict_proba gracefully
        if hasattr(_model, "predict_proba"):
            probs = _model.predict_proba(input_df)[0]
        else:
            # if pipeline ends with classifier without predict_proba, give uniform small values
            n_labels = len(label_classes)
            probs = np.zeros(n_labels)
            probs[pred_enc] = 1.0

        # Map encoded prediction to label name robustly
        try:
            recommended_crop = label_classes[int(pred_enc)]
        except Exception:
            # fallback: if label_classes contains strings of ints or mapping
            if str(pred_enc) in label_classes:
                recommended_crop = str(pred_enc)
            else:
                # last resort
                recommended_crop = str(pred_enc)

        # Confidence lookup: if pred_enc is valid index
        try:
            confidence = probs[int(pred_enc)]
        except Exception:
            confidence = float(np.max(probs)) if len(probs) > 0 else 0.0

        # Top-3
        top_idx = np.argsort(probs)[::-1][:3]
        top3 = {}
        for rank, idx in enumerate(top_idx, 1):
            label = label_classes[idx] if idx < len(label_classes) else f"label_{idx}"
            top3[f"{rank}. {label}"] = f"{probs[idx]:.2%}"

        return recommended_crop, f"{confidence:.2%}", top3

    except Exception as e:
        err = f"{type(e).__name__}: {e}\n\n{traceback.format_exc()}"
        return "Prediction failed", "N/A", {"error": err}

# Build Gradio UI (compatible usage)
with gr.Blocks(title="Crop Recommendation System") as demo:
    gr.Markdown("# 🌾 Crop Recommendation System")
    gr.Markdown("Enter soil and weather parameters to get an AI-powered crop recommendation with confidence scores.")

    if _load_error:
        gr.Markdown("**⚠️ Warning: There was an issue loading your provided model. A fallback/dummy model is in use.**")
        # show a short truncated error message in a code block for diagnosis
        truncated = _load_error if len(_load_error) < 3000 else _load_error[:3000] + "\n\n...[truncated]"
        gr.Code(truncated, language="text")

    with gr.Row():
        with gr.Column(scale=1):
            gr.Markdown("### Soil Parameters")
            N = gr.Slider(label="Nitrogen (N)", minimum=0, maximum=140, value=90, step=1)
            P = gr.Slider(label="Phosphorus (P)", minimum=5, maximum=145, value=42, step=1)
            K = gr.Slider(label="Potassium (K)", minimum=5, maximum=205, value=43, step=1)

        with gr.Column(scale=1):
            gr.Markdown("### Weather Parameters")
            temperature = gr.Slider(label="Temperature (°C)", minimum=8, maximum=44, value=21, step=0.1)
            humidity = gr.Slider(label="Humidity (%)", minimum=14, maximum=100, value=82, step=1)
            ph = gr.Slider(label="Soil pH", minimum=3.5, maximum=10, value=6.5, step=0.1)
            rainfall = gr.Slider(label="Annual Rainfall (mm)", minimum=20, maximum=3000, value=203, step=10)

    predict_btn = gr.Button("🔍 Get Crop Recommendation")

    with gr.Row():
        with gr.Column(scale=2):
            recommended = gr.Textbox(label="🌾 Recommended Crop", interactive=False)
            confidence = gr.Textbox(label="✅ Confidence", interactive=False)

        with gr.Column(scale=1):
            # gr.JSON in 3.50.0 does NOT accept interactive param - keep it simple
            top_3 = gr.JSON(label="📈 Top 3 Recommendations")

    predict_btn.click(
        fn=predict_crop,
        inputs=[N, P, K, temperature, humidity, ph, rainfall],
        outputs=[recommended, confidence, top_3]
    )

    gr.Markdown("""
    ---
    ### Parameter Ranges (based on training data)
    - **Nitrogen (N)**: 0-140 kg/ha
    - **Phosphorus (P)**: 5-145 kg/ha
    - **Potassium (K)**: 5-205 kg/ha
    - **Temperature**: 8-44°C
    - **Humidity**: 14-100%
    - **pH**: 3.5-10
    - **Rainfall**: 20-3000 mm/year
    """)

if __name__ == "__main__":
    # Launch without sending a theme to avoid version issues in older Gradio
    demo.launch(share=False)