import os
import pandas as pd
# import matplotlib.pyplot as plt
import gradio as gr
import numpy as np
import infer

# def predict_genus_dna(dnaSeqs):
#     genuses = []

#     # probs = dnamodel.predict_proba(dnaSeqs)
#     # preds = dnamodel.predict(dnaSeqs)
#     # topProb = np.argsort(probs, axis=1)[:,-3:]
#     # topClass = dnamodel.classes_[topProb]

#     # pred_df = pd.DataFrame(data=[topClass, topProb], columns= ['Genus', 'Probability'])

#     return genuses

# def predict_genus_dna_env(dnaSeqsEnv):
#     genuses = {}
#     probs = model.predict_proba(dnaSeqsEnv)
#     preds = model.predict(dnaSeqsEnv)
    
#     for i in range(len(dnaSeqsEnv)):
#         topProb = np.argsort(probs[i], axis=1)[:,-3:]
#         topClass = model.classes_[topProb]

#         sampleStr = dnaSeqsEnv['nucraw'][i]
#         genuses[sampleStr] = (topClass, topProb)

    # pred_df = pd.DataFrame(data=[top5class, top5prob], columns= ['Genus', 'Probability'])

    # return genuses

# def get_genus_image(genus):
#     # return a URL to genus image
#     return f"https://example.com/images/{genus}.jpg"

def get_genuses(dna_file, dnaenv_file):
    dna_df = pd.read_csv(dna_file.name)
    dnaenv_df = pd.read_csv(dnaenv_file.name)
    
    results = []
    
    # envdna_genuses = predict_genus_dna_env(dnaenv_df)
    # dna_genuses = predict_genus_dna(dna_df)
    # images = [get_genus_image(genus) for genus in top_5_genuses]

    genuses = infer.infer()
        
    results.append({
        "sequence": dna_df['nucraw'],
        # "predictions": pd.concat([dna_genuses, envdna_genuses], axis=0)
        'predictions': genuses
})
    
    return results

def display_results(results):
    display = []
    for result in results:
        # for i in range(len(result["predictions"])):
        #     display.append({
        #         "DNA Sequence": result["sequence"],
        #         "DNA Pred Genus": result['predictions'][i][0],
        #         "DNA Only Prob": result['predictions'][i][1],
        #         "DNA Env Pred Genus": result['predictions'][i][2],
        #         "DNA Env Prob": result['predictions'][i][3],
        #         # "Image": result["images"][i]
        #     })
        display.append({
            "DNA Sequence": result["sequence"],
            "DNA Pred Genus": result['predictions'][0]
        })
    return pd.DataFrame(display)

def gradio_interface(file):
    results = get_genuses(file)
    return display_results(results)

# Gradio interface
with gr.Blocks() as demo:
    with gr.Column():
        gr.Markdown("# DNA Identifier Tool")
        file_input = gr.File(label="Upload DNA CSV file", file_types=['csv'])
        output_table = gr.Dataframe(headers=["DNA", "Coord", "DNA Only Pred Genus", "DNA Only Prob", "DNA & Env Pred Genus", "DNA & Env Prob"])
        
        def update_output(file):
            result_df = gradio_interface(file)
            return result_df
        
        file_input.change(update_output, inputs=file_input, outputs=output_table)
        
demo.launch()


# with gr.Blocks() as demo:
#     with gr.Row():
#         word = gr.Textbox(label="word")
#         leng = gr.Number(label="leng")
#         output = gr.Textbox(label="Output")
#     with gr.Row():
#         run = gr.Button()

#     event = run.click(predict_genus, 
#                       [word, leng], 
#                       output, 
#                       batch=True, 
#                       max_batch_size=20)

# demo.launch()

# DB_USER = os.getenv("DB_USER")
# DB_PASSWORD = os.getenv("DB_PASSWORD")
# DB_HOST = os.getenv("DB_HOST")
# PORT = 8080
# DB_NAME = "bikeshare"

# connection_string = f"postgresql://{DB_USER}:{DB_PASSWORD}@{DB_HOST}?port={PORT}&dbname={DB_NAME}"

# def get_count_ride_type():
#     df = pd.read_sql(
#     """
#         SELECT COUNT(ride_id) as n, rideable_type
#         FROM rides
#         GROUP BY rideable_type
#         ORDER BY n DESC
#     """,
#     con=connection_string
#     )
#     fig_m, ax = plt.subplots()
#     ax.bar(x=df['rideable_type'], height=df['n'])
#     ax.set_title("Number of rides by bycycle type")
#     ax.set_ylabel("Number of Rides")
#     ax.set_xlabel("Bicycle Type")
#     return fig_m


# def get_most_popular_stations():

#     df = pd.read_sql(
#         """
#     SELECT COUNT(ride_id) as n, MAX(start_station_name) as station
#     FROM RIDES
#     WHERE start_station_name is NOT NULL
#     GROUP BY start_station_id
#     ORDER BY n DESC
#     LIMIT 5
#     """,
#     con=connection_string
#     )
#     fig_m, ax = plt.subplots()
#     ax.bar(x=df['station'], height=df['n'])
#     ax.set_title("Most popular stations")
#     ax.set_ylabel("Number of Rides")
#     ax.set_xlabel("Station Name")
#     ax.set_xticklabels(
#         df['station'], rotation=45, ha="right", rotation_mode="anchor"
#     )
#     ax.tick_params(axis="x", labelsize=8)
#     fig_m.tight_layout()
#     return fig_m


# with gr.Blocks() as demo:
#     with gr.Row():
#         bike_type = gr.Plot()
#         station = gr.Plot()

#     demo.load(get_count_ride_type, inputs=None, outputs=bike_type)
#     demo.load(get_most_popular_stations, inputs=None, outputs=station)

# def greet(name, intensity):
#     return "Hello, " + name + "!" * int(intensity)

# demo = gr.Interface(
#     fn=greet,
#     inputs=["text", "slider"],
#     outputs=["text"],
# )

demo.launch()