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| 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() | |