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Dockerfile (1)

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+# ✅ Force Python 3.11 to match local environment
+FROM python:3.11-slim
+
+WORKDIR /app
+COPY . /app
+
+# ✅ Clean upgrade and install lightweight dependencies first
+RUN apt-get update && apt-get install -y gcc g++ && rm -rf /var/lib/apt/lists/*
+
+# ✅ Install compatible libraries
+RUN pip install --upgrade pip
+RUN pip install --no-cache-dir \
+    gradio==5.49.1 \
+    xgboost==2.1.0 \
+    scikit-learn==1.7.2 \
+    pandas==2.3.3 \
+    numpy==2.3.4 \
+    joblib==1.4.2 \
+    openpyxl==3.1.5 \
+    tabulate==0.9.0
+
+EXPOSE 7860
+
+CMD ["python", "app.py"]
+
+

app (1).py

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+import gradio as gr 
+import pandas as pd 
+import numpy as np 
+from xgboost import XGBRegressor 
+import joblib 
+import os 
+import logging 
+
+# -----------------------------
+# 1️⃣ File paths
+# -----------------------------
+MODEL_PATH = "xgb_model_corn_final.json"
+SCALER_PATH = "minmax_scaler.pkl"
+EXCEL_PATH = "microalgae_pot_experiment_corrected_doses.xlsx"
+
+# Check files exist
+for path in [MODEL_PATH, SCALER_PATH, EXCEL_PATH]:
+    if not os.path.exists(path):
+        raise FileNotFoundError(f"File not found: {path}")
+
+# -----------------------------
+# 2️⃣ Load model, scaler, and data
+# -----------------------------
+xgb_model = XGBRegressor()
+xgb_model.load_model(MODEL_PATH)
+scaler = joblib.load(SCALER_PATH)
+df = pd.read_excel(EXCEL_PATH)
+
+# Only one strain exists
+strain_names = df['Microalgae_Strain'].unique().tolist()
+single_strain_value = 0  # training used LabelEncoder -> 0
+
+# -----------------------------
+# 3️⃣ Prediction function
+# -----------------------------
+def predict_dose(crop, microalgae_strain, soil_n, soil_p, soil_k, soil_ec, soil_moisture,
+                 chlorophyll, shoot_length, root_length, yield_g, relative_yield,
+                 actual_dose):
+    logs = []  # Capture all debug info
+
+    # 1️⃣ Validate inputs
+    required = [soil_n, soil_p, soil_k, soil_ec, soil_moisture,
+                chlorophyll, shoot_length, root_length, yield_g, relative_yield]
+    if any(v is None for v in required):
+        logs.append("[DEBUG] Missing numeric inputs!")
+        return "⚠️ Please fill all numeric input fields.", None, None, "\n".join(logs)
+    logs.append("[DEBUG] All inputs received.")
+
+    # 2️⃣ Encode strain
+    strain_encoded = single_strain_value
+    logs.append(f"[DEBUG] Using Microalgae_Strain encoded value: {strain_encoded}")
+
+    # 3️⃣ Create DataFrame
+    feature_cols = ['Soil_N_ppm','Soil_P_ppm','Soil_K_ppm','Soil_EC_dS_m',
+                    'Soil_Moisture_%','Chlorophyll_SPAD','Shoot_Length_cm',
+                    'Root_Length_cm','Yield_g_per_pot','Relative_Yield_%','Microalgae_Strain']
+    X_input_df = pd.DataFrame([[soil_n, soil_p, soil_k, soil_ec, soil_moisture,
+                                chlorophyll, shoot_length, root_length, yield_g,
+                                relative_yield, strain_encoded]],
+                              columns=feature_cols).astype(float)
+    logs.append(f"[DEBUG] Input DataFrame:\n{X_input_df}")
+
+    # 4️⃣ Scale
+    X_input_scaled = scaler.transform(X_input_df)
+    logs.append(f"[DEBUG] Scaled Input:\n{X_input_scaled}")
+
+    # 5️⃣ Predict
+    predicted_dose = xgb_model.predict(X_input_scaled)[0]
+    logs.append(f"[DEBUG] Predicted dose (raw): {predicted_dose}")
+
+    # 6️⃣ Compute error
+    if actual_dose is not None:
+        abs_error = abs(predicted_dose - actual_dose)
+        logs.append(f"[DEBUG] Actual dose: {actual_dose}")
+        logs.append(f"[DEBUG] Absolute Error: {abs_error}")
+        return (f"🌱 **Predicted Dose:** {predicted_dose:.2f} g/pot",
+                f"📏 **Actual Dose:** {actual_dose:.2f} g/pot",
+                f"❌ **Absolute Error:** {abs_error:.2f} g/pot",
+                "\n".join(logs))
+    else:
+        logs.append("[DEBUG] Actual dose not provided.")
+        return (f"🌱 **Predicted Dose:** {predicted_dose:.2f} g/pot",
+                "📏 Actual Dose: Not provided",
+                "❌ Absolute Error: N/A",
+                "\n".join(logs))
+
+# -----------------------------
+# 4️⃣ Gradio Interface
+# -----------------------------
+with gr.Blocks(
+    theme=gr.themes.Soft(primary_hue="green", secondary_hue="lime", neutral_hue="gray"),
+    title="EcoGrowAI — Microalgae Dose Inference"
+) as demo:
+    gr.Markdown(
+        """
+        <div style='text-align:center'>
+        <h1 style='color:#00b16a; margin-bottom:8px;'>🌿 EcoGrowAI — Inference App</h1>
+        <p style='color:#555;'>Predict the optimal <b>microalgae dose (g/pot)</b> for Corn based on soil and growth parameters.</p>
+        </div>
+        <hr style='margin: 10px 0;'>
+        """
+    )
+    with gr.Row():
+        with gr.Column(scale=1):
+            gr.Markdown("### 🌾 Crop & Microalgae Selection")
+            crop = gr.Dropdown(["Corn"], label="Select Crop", value="Corn")
+            strain = gr.Dropdown(strain_names, label="Microalgae Strain", value=strain_names[0])
+            gr.Markdown("### 🌱 Soil Parameters")
+            soil_n = gr.Number(label="Soil N (ppm)")
+            soil_p = gr.Number(label="Soil P (ppm)")
+            soil_k = gr.Number(label="Soil K (ppm)")
+            soil_ec = gr.Number(label="Soil EC (dS/m)")
+            soil_moisture = gr.Number(label="Soil Moisture (%)")
+            gr.Markdown("### 🌾 Plant Growth Parameters")
+            chlorophyll = gr.Number(label="Chlorophyll (SPAD)")
+            shoot_length = gr.Number(label="Shoot Length (cm)")
+            root_length = gr.Number(label="Root Length (cm)")
+            yield_g = gr.Number(label="Yield (g/pot)")
+            relative_yield = gr.Number(label="Relative Yield (%)")
+            gr.Markdown("### 📏 Actual Dose (optional)")
+            actual_dose = gr.Number(label="Actual Dose (g/pot)")
+            predict_btn = gr.Button("🔍 Predict Dose", variant="primary")
+        with gr.Column(scale=1):
+            gr.Markdown("### 📊 Inference Result")
+            pred_box = gr.Markdown("Awaiting prediction...")
+            actual_box = gr.Markdown("")
+            abs_box = gr.Markdown("")
+            log_box = gr.Textbox(label="Debug Logs", lines=15)  # <-- Add this
+            predict_btn.click(
+                fn=predict_dose,
+                inputs=[crop, strain, soil_n, soil_p, soil_k, soil_ec, soil_moisture,
+                        chlorophyll, shoot_length, root_length, yield_g, relative_yield,
+                        actual_dose],
+                outputs=[pred_box, actual_box, abs_box, log_box]
+            )
+
+# -----------------------------
+# 5️⃣ Launch
+# -----------------------------
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0", server_port=7860)

minmax_scaler (1).pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:fc3eeaadab72cbf85ef6b7653110f9316e48e6fcdeb1fe871f133e2a44ade7b3
+size 1551