requirements.txt

gradio
numpy
torch

app.py

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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()