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()