app.py

import gradio as gr
import numpy as np

# A placeholder function to simulate the Authencoder model's prediction.
# Replace this with your actual model's prediction function.
# This function demonstrates how to take all the inputs and return an output.
# You will need to load your model here and perform the actual inference.
def predict_quality(
    herb_name,
    temperature,
    humidity,
    storage_time,
    light_exposure,
    soil_ph,
    soil_moisture,
    soil_nitrogen,
    soil_phosphorus,
    soil_potassium,
    soil_organic_carbon,
    heavy_metal_pb,
    heavy_metal_as,
    heavy_metal_hg,
    heavy_metal_cd,
    aflatoxin_total,
    pesticide_residue_total,
    moisture_content,
    essential_oil,
    chlorophyll_index,
    leaf_spots_count,
    discoloration_index,
    total_bacterial_count,
    total_fungal_count,
    e_coli_present,
    salmonella_present,
    dna_marker_authenticity
):
    """
    This function simulates a model's prediction based on the input parameters.
    In a real application, you would load and use your Authencoder model here.

    Args:
        All the parameters from the Gradio form.

    Returns:
        A string with a simulated quality prediction.
    """
    # Placeholder logic:
    # This is a dummy response. Replace this with your model's actual
    # inference code. For example:
    #
    # from transformers import pipeline
    # model = pipeline("your-model-task", model="your-model-id")
    # result = model(your_processed_input_data)
    #
    # For this example, we'll just check a few parameters to give a meaningful
    # placeholder output.

    quality_score = np.random.uniform(70, 100)
    
    # Check for critical parameters
    if e_coli_present == "Yes" or salmonella_present == "Yes":
        return f"Warning: E. coli or Salmonella detected. Quality is 'Unsafe'."

    if heavy_metal_cd > 0.5 or heavy_metal_hg > 0.5:
        return f"Warning: High heavy metal content. Quality is 'Poor'."
        
    if moisture_content > 10 or total_bacterial_count > 1000:
        quality_score -= 20
        
    if dna_marker_authenticity == "No":
        return f"Warning: Authenticity not confirmed by DNA marker. Quality is 'Unverified'."

    return f"Based on the provided data, the quality of {herb_name} is 'Good' with a score of {quality_score:.2f}/100."

# Create the Gradio Interface
with gr.Blocks(title="Authencoder Herb Quality Assessment") as app:
    gr.Markdown(
        """
        # Authencoder: Herb Quality Assessment
        This application simulates a system for assessing the quality and authenticity of herbs based on various parameters.
        **Note**: This is a demo. Please replace the `predict_quality` function with your actual model's inference logic.
        """
    )
    with gr.Row():
        with gr.Column():
            gr.Markdown("### Herb Details and Environmental Factors")
            herb_name = gr.Textbox(label="Herb Name", placeholder="e.g., Turmeric, Ginseng")
            temperature = gr.Slider(minimum=-10, maximum=50, step=0.1, label="Temperature ($^\circ C$)", value=25)
            humidity = gr.Slider(minimum=0, maximum=100, step=0.1, label="Humidity (%)", value=60)
            storage_time = gr.Number(label="Storage Time (Days)", value=30)
            light_exposure = gr.Slider(minimum=0, maximum=24, step=0.1, label="Light Exposure (hours per day)", value=8)

        with gr.Column():
            gr.Markdown("### Soil and Chemical Analysis")
            soil_ph = gr.Slider(minimum=0, maximum=14, step=0.1, label="Soil pH", value=6.5)
            soil_moisture = gr.Slider(minimum=0, maximum=100, step=0.1, label="Soil Moisture (%)", value=50)
            soil_nitrogen = gr.Number(label="Soil Nitrogen (mg/kg)", value=100)
            soil_phosphorus = gr.Number(label="Soil Phosphorus (mg/kg)", value=50)
            soil_potassium = gr.Number(label="Soil Potassium (mg/kg)", value=200)
            soil_organic_carbon = gr.Number(label="Soil Organic Carbon (%)", value=2.5)
            
        with gr.Column():
            gr.Markdown("### Heavy Metals, Toxins, and Pesticides")
            heavy_metal_pb = gr.Number(label="Heavy Metal Pb (ppm)", value=0.1)
            heavy_metal_as = gr.Number(label="Heavy Metal As (ppm)", value=0.05)
            heavy_metal_hg = gr.Number(label="Heavy Metal Hg (ppm)", value=0.01)
            heavy_metal_cd = gr.Number(label="Heavy Metal Cd (ppm)", value=0.01)
            aflatoxin_total = gr.Number(label="Aflatoxin Total (ppb)", value=0.2)
            pesticide_residue_total = gr.Number(label="Pesticide Residue Total (ppm)", value=0.03)

    with gr.Row():
        with gr.Column():
            gr.Markdown("### Physical and Biological Traits")
            moisture_content = gr.Slider(minimum=0, maximum=100, step=0.1, label="Moisture Content (%)", value=8)
            essential_oil = gr.Slider(minimum=0, maximum=100, step=0.1, label="Essential Oil (%)", value=2)
            chlorophyll_index = gr.Number(label="Chlorophyll Index", value=0.7)
            leaf_spots_count = gr.Number(label="Leaf Spots Count", value=5)
            discoloration_index = gr.Slider(minimum=0, maximum=100, step=0.1, label="Discoloration Index (%)", value=15)
            
        with gr.Column():
            gr.Markdown("### Microbial and Authenticity Checks")
            total_bacterial_count = gr.Number(label="Total Bacterial Count (CFU/g)", value=500)
            total_fungal_count = gr.Number(label="Total Fungal Count (CFU/g)", value=100)
            e_coli_present = gr.Radio(choices=["Yes", "No"], label="E. coli Present", value="No")
            salmonella_present = gr.Radio(choices=["Yes", "No"], label="Salmonella Present", value="No")
            dna_marker_authenticity = gr.Radio(choices=["Yes", "No"], label="DNA Marker Authenticity", value="Yes")

    submit_btn = gr.Button("Assess Quality", variant="primary")
    output_text = gr.Text(label="Assessment Result")

    submit_btn.click(
        fn=predict_quality,
        inputs=[
            herb_name,
            temperature,
            humidity,
            storage_time,
            light_exposure,
            soil_ph,
            soil_moisture,
            soil_nitrogen,
            soil_phosphorus,
            soil_potassium,
            soil_organic_carbon,
            heavy_metal_pb,
            heavy_metal_as,
            heavy_metal_hg,
            heavy_metal_cd,
            aflatoxin_total,
            pesticide_residue_total,
            moisture_content,
            essential_oil,
            chlorophyll_index,
            leaf_spots_count,
            discoloration_index,
            total_bacterial_count,
            total_fungal_count,
            e_coli_present,
            salmonella_present,
            dna_marker_authenticity
        ],
        outputs=output_text
    )

app.launch()