Update app.py

app.py

@@ -89,12 +89,12 @@ def predict_dose(crop, microalgae_strain, soil_n, soil_p, soil_k, soil_ec, soil_
 # -----------------------------
 with gr.Blocks(
     theme=gr.themes.Soft(primary_hue="green", secondary_hue="lime", neutral_hue="gray"),
-    title="EcoGrowAI — Microalgae Dose Inference"
+    title="Microalgae Dose Prediction"
 ) as demo:
     gr.Markdown(
         """
         <div style='text-align:center'>
-        <h1 style='color:#00b16a; margin-bottom:8px;'>EcoGrowAI — Inference App</h1>
+        <h1 style='color:#00b16a; margin-bottom:8px;'>Microalgae — 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;'>
@@ -128,11 +128,18 @@ with gr.Blocks(
             log_box = gr.Textbox(label="Debug Logs", lines=15)# <-- Add this
             gr.Markdown(
                 """
-                ### 💡 Tips:
-                - Upload multiple files for batch processing
-                - For images: ensure text is clear and well-lit
-                - For PDFs: both text-based and scanned PDFs work
-                - The AI will analyze visual content even if text extraction fails
+                ### Input Tips:
+                - Select Crop and the used microalgae strain.
+                - Enter values for N, P, K, EC, and Moisture.
+                - Fill in Chlorophyll (SPAD), Shoot/Root length, Yield, and Relative Yield.
+                - (Optional) Add Actual Dose if you want to compare results.
+                - Click “Predict Dose” to get results.
+                ### Output Tips: 
+                <div>
+                <p style="margin:6px 0;"><strong>Predicted Dose:</strong> Model’s suggested microalgae dose (g/pot).</p>
+                <p style="margin:6px 0;"><strong>Absolute Error:</strong> Shows how close the prediction is to your actual dose.</p>
+                <p style="margin:6px 0;"><strong>Debug Logs:</strong> Displays detailed input and model processing info.</p>
+                </div>
                 """
             )
             predict_btn.click(