First Commit

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
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+*.cbm filter=lfs diff=lfs merge=lfs -text
+Dataset.xlsx filter=lfs diff=lfs merge=lfs -text

Dataset.xlsx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:bdb613c74c01c0b60410f283e33d7ea5ffbed8aaef459fe41d11768a2821c781
+size 1016051

README.md

@@ -11,4 +11,116 @@ license: cc-by-nc-4.0
 short_description: ML Applications in Tissue Engineering
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+
+# 👬 MLATE V3: Multi-Tissue Scaffold Prediction Platform
+
+**MLATE V3** is a fully integrated, machine learning-powered platform for predicting, optimizing, and generating detailed fabrication procedures for 3D-(bio)printed scaffolds in tissue engineering. This app enables researchers to input a wide range of biomaterials, cell lines, and printing parameters, and receive optimized scaffold compositions along with step-by-step printing instructions generated via Google Gemini.
+
+> 📄 *Rafieyan et al. (preprint, 2025). MLATE V3: A fully integrated Multi-Tissue, machine learning platform for prediction, optimization and generating procedures for fabricating 3D-(bio)printing scaffolds for tissue engineering*
+
+---
+
+## 🚀 Features
+
+- 🔬 Predict scaffold quality based on printability and cell response
+- 🧪 Optimize biomaterial concentrations, cell densities, and printing parameters using Optuna
+- 🧠 Powered by two fine-tuned **CatBoostClassifier** models
+- 📋 Automatically generates fabrication protocols with Gemini API
+- 🔐 Enforces safe defaults and intelligent UI input validation
+- 🧱 Uses a real-world, curated dataset of **2847 samples** across **multiple tissues and cell lines**
+
+---
+
+## 📂 Dataset
+
+This project includes a publicly available dataset (`Dataset.xlsx`) containing:
+- 123 biomaterials
+- 175 cell lines
+- 7 printing parameters
+- Scaffold performance labels
+
+The dataset is available in the [Files and Versions](https://huggingface.co/spaces/your-username/your-space-name/blob/main/Dataset.xlsx) tab of this Space.
+
+You may also optionally add this to Hugging Face Datasets for broader access.
+
+---
+
+## ⚙️ How It Works
+
+1. **Input**: User selects biomaterials, cell line, and printing parameters with min/max/step values
+2. **Optimization**: Optuna runs 50 trials to maximize predicted scaffold quality (WSSQ)
+3. **Prediction**:
+    - Two CatBoost models are used to predict:
+        - `Printability` (3-class)
+        - `Cell Response` (5-class)
+    - Probabilistic predictions are mapped to expected scores
+4. **Scaffold Quality**: A weighted combination of printability and cell response
+5. **Procedure Generation**: A Gemini API prompt generates custom step-by-step fabrication instructions
+
+---
+
+## 💻 Running Locally
+
+Clone the repo and install dependencies:
+
+```bash
+git clone https://huggingface.co/spaces/your-username/MLATE-V3
+cd MLATE-V3
+
+# Create virtual environment (optional)
+python -m venv venv
+source venv/bin/activate  # or venv\Scripts\activate on Windows
+
+# Install dependencies
+pip install -r requirements.txt
+
+# Set your Gemini API key
+export GEMINI_API_KEY=your_key_here  # or set in .env
+
+# Run the app
+streamlit run app.py
+```
+
+---
+
+## 📜 License
+
+This project is licensed under the **Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)**.
+
+You are free to:
+- Share and adapt the code
+- Use the dataset for academic research
+
+But:
+- **Commercial use is prohibited**
+- **Citation is required** (see below)
+
+---
+
+## 📚 Citation
+
+If you use MLATE V3 or its dataset in your research, please cite:
+
+> Rafieyan et al. (preprint, 2025).  
+> *MLATE V3: A fully integrated Multi-Tissue, machine learning platform for prediction, optimization and generating procedures for fabricating 3D-(bio)printing scaffolds for tissue engineering*  
+> *(Preprint link to be added after publication)*
+
+BibTeX:
+```bibtex
+@article{rafieyan2025mlate,
+  author  = {Rafieyan, Saeed and others},
+  title   = {MLATE V3: A fully integrated Multi-Tissue, machine learning platform for prediction, optimization and generating procedures for fabricating 3D-(bio)printing scaffolds for tissue engineering},
+  journal = {Preprint},
+  year    = {2025}
+}
+```
+
+---
+
+## ⚠️ Disclaimer
+
+This tool is intended for **research and academic use only**. While we strive for accuracy, the predictions and fabrication procedures are generated using machine learning and language models and may contain errors or inconsistencies. The authors are **not responsible for any unintended consequences** arising from use of this tool in experimental or clinical settings.
+
+---
+
+Developed by [Saeed Rafieyan](https://sraf.ir)

app.py

@@ -0,0 +1,616 @@
+import os
+import streamlit as st
+import pandas as pd
+import joblib
+import optuna
+import numpy as np
+import openai
+from catboost import CatBoostClassifier
+
+
+from google import genai
+from google.genai import types
+
+
+
+# keep a reference to the real number_input
+_original_number_input = st.number_input
+
+def safe_number_input(label, **kwargs):
+    """
+    Clamp `value` into [min_value, max_value] and warn if we had to adjust.
+    Then call the real st.number_input with the clamped default.
+    """
+    min_value = kwargs.get("min_value", float("-inf"))
+    max_value = kwargs.get("max_value", float("inf"))
+    value     = kwargs.get("value", min_value)
+    # clamp
+    clamped = min(max(value, min_value), max_value)
+    if clamped != value:
+        st.warning(
+            f"⚠️ Default for “{label}” ({value}) was outside "
+            f"[{min_value}, {max_value}]; using {clamped} instead."
+        )
+    kwargs["value"] = clamped
+    return _original_number_input(label, **kwargs)
+
+# override streamlit's number_input globally
+st.number_input = safe_number_input
+
+
+
+# directly use your Gemini key
+gemini_key = os.getenv("GEMINI_API_KEY")
+if not gemini_key:
+    st.error("🚨 Gemini API key not found. Please set GEMINI_API_KEY in your Space settings.")
+    st.stop()
+
+client = genai.Client(api_key=gemini_key)
+
+
+# ─── Scaffold Quality Function ───────────────────────────────────────────────────
+def scaffold_quality_combined(printability, cell_response,
+                              weight_printability=0.3, weight_cell_response=0.7):
+    if printability == 0:
+        return 0.0
+    if cell_response == 1:
+        return 100 * (printability / 3.0)
+    norm_p = printability / 3.0
+    norm_c = (cell_response - 1) / 4.0
+    hm = (weight_printability + weight_cell_response) / (
+        (weight_printability / norm_p) +
+        (weight_cell_response / norm_c)
+    )
+    mc = (norm_p**weight_printability) * (norm_c**weight_cell_response)
+    return 100 * ((hm + mc) / 2.0)
+
+# ─── Constants ────────────────────────────────────────────────────────────────────
+BIOMATERIAL_OPTIONS = [
+    "Alginate (%w/v)",
+    "PVA-HA (%w/v)",
+    "CaSO4 (%w/v)",
+    "Na2HPO4 (%w/v)",
+    "Gelatin (%w/v)",
+    "GelMA (%w/v)",
+    "laponite (%w/v)",
+    "graphene oxide (%w/v)",
+    "hydroxyapatite (%w/v)",
+    "Hyaluronic_Acid (%w/v)",
+    "hyaluronan metacrylate (%w/v)",
+    "NorHA (%w/v)",
+    "Fibroin/Fibrinogen (%w/v)",
+    "Pluronic P-123 (%w/v)",
+    "Collagen (%w/v)",
+    "Chitosan (%w/v)",
+    "CS-AEMA (%w/v)",
+    "RGD (mM)",
+    "TCP (%w/v)",
+    "Gellan (%w/v)",
+    "bioactive glass (%w/v)",
+    "Nano/Methycellulose (%w/v)",
+    "PEGTA (%w/v)",
+    "PEGMA (%w/v)",
+    "PEGDA (%w/v)",
+    "Agarose (%w/v)",
+    " hyaluronic acid+ Ph moieties (%w/v)",
+    "matrigel (%w/v)",
+    "CaCl2(mM)",
+    "NaCl(mM)",
+    "BaCl2(mM)",
+    "SrCl2(mM)",
+    "CaCO3 (mM)",
+    "Genipin (%w/v)",
+    "PVA (%wt)",
+    "trans-glutaminase (%w/v)",
+    "alginate lyase (U/ml)",
+    "D-glucose (%w/v)",
+    "PLGA (%w/v)",
+    "vascular tissued-derived dECM (%w/v)",
+    "PEG-8-SH (mM)",
+    "Alginate dialdehyde (%w/v)",
+    "Alginate sulfate (%w/v)",
+    "RGD-modified alginate (%w/v)",
+    "poly(N-isopropylacrylamide) grafted hyaluronan (%w/v)",
+    "chondroitin sulfate methacrylate (%w/v)",
+    "PCL (%w/v)",
+    "alginate methacrylate (%w/v)",
+    "HRP (U/ml)",
+    "Pluronic F127 (%w/v)/Lutrol F127 (%w/v)",
+    "Irgacure 2959 (%w/v)",
+    "Eosin Y (%w/v)",
+    "Ruthenium (mM)",
+    "sodium persulfate (SPS) (mM)",
+    "HEPES (mM)",
+    "LAP (%w/v)",
+    "glutaraldehyde (%w/v)",
+    "PBS  (M)",
+    "glycerol (%w/v)",
+    "cECM (%w/v)",
+    "gel-fu(%w/v)",
+    "Rose Bengal (%w/v)",
+    "Vitamin B2(%w/v)",
+    "VEGF(%w/v)",
+    "Polypyrrole:PSS(%w/v)",
+    "boratebioactiveglass(%w/v)",
+    "astaxanthin(%w/v)",
+    "PRP (%v/v)",
+    "methacrylated collagen (%w/v)",
+    "α-Toc (µM)",
+    "ascorbic acid (mM)",
+    "Liver dECM(%w/v)",
+    "galactosylated alginate (%w/v)",
+    "SC-PEG(%w/v)",
+    "SFMA-L(%w/v)",
+    "SFMA-M(%w/v)",
+    "SFMA-H(%w/v)",
+    "KdECMMA(%w/v)",
+    "BA silk fibronin (%w/v)",
+    "Carrageenan(%v)",
+    "Carbopol ETD 2020 NF (%w/v)",
+    "Carbopol  Ultrez 10 NF(%w/v)",
+    "Carbopol  NF-980(%w/v)",
+    "FBS (%v/v)",
+    "MeTro (%w/v)",
+    "Triethanolamine (%v/v)",
+    "PEG-Fibrinogen (%w/v)",
+    "polyethylene glycol dimethacrylate (%w/v)",
+    "aprotinin (µg/ml)",
+    "gold nanorod (mg/mL)",
+    "egg white (w/v)",
+    "1-Vinyl-2-Pyrrolidione (v/v)",
+    "carboxyl functionalized carbon nanotubes  (%w/v)",
+    "polyHIPE  (%w/v)",
+    "β-D galactose (mM)",
+    "hydrogen peroxide (H2O2) (%v/v)",
+    "lactic acid v/v",
+    "NorCol (%w/v)",
+    "DDT (%w/v)",
+    "ammonium persulfate (mM)",
+    "diTyr-RGD (mM)",
+    "PHEG-Tyr (%w/v)",
+    "MMP2-degradable peptide (%w/v)",
+    "KdECM (%w/v)",
+    "EDC (mg)",
+    "NHS (mg)",
+    "VA086 (%w/v)",
+    "PGS (%w/v)",
+    "thiolated HA (%w/v)",
+    "boron nitride nanotubes (%w/v)",
+    "PEDOT:PSS (ul)",
+    "KCl (mM)",
+    "skeletal muscle ECM methacrylate (%w/v)",
+    "PEO (%w/v)",
+    "Carbon dots (mg/ml)",
+    "Laminin (ug/ml)",
+    "DF-PEG (%w/v)",
+    "omenta ECM (%w/v)",
+    "thrombin (unit/ml)",
+    "Carbon nanotube (CNT) (w/v)",
+    "Phytagel(%v)",
+    "Laponite-XLG  (%w/w)"
+]
+
+CELL_LINE_OPTIONS = [
+    "chondrocyteyte",
+    "HepG2",
+    "bMSCs",
+    "HUVECs",
+    "NIH3T3",
+    "MESCs",
+    "hiPSC-CMs /ATCCs",
+    "CPCs",
+    "L929",
+    "Myoblast cells",
+    "hiPSCs",
+    "HepaRG",
+    "hESCs",
+    "10T1/2",
+    "Cardiac progenitor cells",
+    "NSCLC PDX",
+    "RAMECs",
+    "hASCs",
+    "HAVIC",
+    "Primary mouse hepatocyte",
+    "PTECs",
+    "human nasoseptal chondrocytes",
+    "PDX",
+    "HPFs",
+    "U87-MG",
+    "ESCs",
+    "HASSMC",
+    "dermal fibroblasts",
+    "MC3T3-E1",
+    "Schwann cells",
+    "hiPSC-CMs and HS-27A",
+    "Saos-2 ",
+    "SU3",
+    "hTMSCs",
+    "HACs",
+    "HADMSCs",
+    "HeLa",
+    "human primary kidney cells",
+    "myoblasts",
+    "MSCs",
+    "human primary kidneycells",
+    "293FT",
+    "HEK 293FT",
+    "Wnt3a-293FT",
+    "RSC96/HUVECs",
+    "human adipogenic mesenchymal stem cells",
+    "HEPG2/ECs",
+    "HUVECs/MSCs",
+    "RHECs",
+    "Human non-small cell lung cancer line Calu-3 (Calu-3)",
+    "HL-1",
+    "mouse cardiac cells",
+    "IMR-90",
+    "EPCs",
+    "MRC5",
+    "rMSC",
+    "basil plant cell",
+    "hNCs",
+    "A549",
+    "human induced pluripotent stem cell-derived cardiomyocytes",
+    "bMSCs/hACs",
+    "EA.hy 926 cells",
+    "HepG2/C3A",
+    "human epithelial lung carcinoma cells",
+    "Human cardiac fibroblasts",
+    "hTERT-MSC",
+    "cardiomyocytes",
+    "Huh7",
+    "NRCMs",
+    "HCF",
+    "Wnt reporter-293FT",
+    "neonatal rat ventricular CFs",
+    "human coronary artery endothelial cells",
+    "hiPSC-CM / fibroblasts",
+    "primary mouse hepatocyte",
+    "NIH3T3/ HUVECs",
+    "murine macrophage-like cell line",
+    "Endothelial cells",
+    "Human aortic VIC",
+    "sADSC",
+    "HUVECs/H9C2",
+    "neonatal rat ventricular cardiomyocytes",
+    "MG-63",
+    "Neonatal mouse cardiomyocytes (NMVCMs)",
+    "human hepatic stellate cell line",
+    "HEK-293",
+    "aHSC",
+    "MFCs",
+    "fibroblasts",
+    "HNDF",
+    "cardiomyocyte/MSCs",
+    "ADSCs",
+    "HCASMCs",
+    "cardiomyocyte",
+    "hCPCs",
+    "Human CM /adult human fibroblasts ",
+    "primary rat hepatocyte",
+    "human cardiac progenitor cells",
+    "SMC",
+    "Human MSCs",
+    "ACPCs",
+    "Huh7/HepaRG",
+    "Human umbilical vein endothelial cells",
+    "ATDC5",
+    "hESC-derived HLCs",
+    "NIH 3T3",
+    "n neonatal mouse ventricular cardiomyocytes",
+    "CFs/CMs/HUVECs",
+    "MRC-5",
+    "VIC",
+    "eHep",
+    "hUVECs/NIH3T3",
+    "MC3T3",
+    "HLC",
+    "hepatoma",
+    "FB",
+    "A549 GFP+",
+    "HPAAF",
+    "PMHs",
+    "HUVSMCs",
+    "Human CPCs",
+    "Fibroblasts/THP-1",
+    "rat ventricular cardiomyocytes",
+    "iCMs/iCFs/iECs",
+    "HUVEC/HHSC",
+    "Human CPCs / MSCs",
+    "HepaRG/LX-2 ",
+    "iCMs/iCFs/iECs/iCMFs",
+    "LX-2 ",
+    "SMMC-7721",
+    "Hepatoblast- single cell/iESC/iMSC",
+    "iCMFs",
+    "Hepatoblast- spheroid/iESC/iMSC",
+    "hBM-MSCs",
+    "BMSCs",
+    "HUVECs and HHSCs",
+    "Intrahepatic cholangiocarcinoma (ICC)",
+    "VICs",
+    "CMs/CFs",
+    "human neonatal dermal fi broblasts",
+    "10T1/2 fibroblast-laden cells",
+    "human cardiac fibroblasts",
+    "neonatal rat ventricular CMs",
+    "HUVECs and hiPSC-CS",
+    "iPSC-derived CM",
+    "Human Umbilical Vein Endothelial Cells + iPSC-derived CM",
+    "rabbit bone marrow mesenchymal stem cells",
+    "Neonatal rat cardiomyocytes",
+    "NIH 3T3 mouse fibroblasts",
+    "Human CPCs & MSCs",
+    "NSCLC PDX/CAFs",
+    "hiPSCs-derived HLCs",
+    "U87",
+    "75% hepatoblast cells, 20% iEC and 5% iMSC",
+    "SMCs",
+    "A549/95-D cells",
+    "NCI-H441",
+    "pancreatic cancer cell",
+    "prostate cancer stem cell",
+    "human primary parathyroid cells ",
+    "primary human hepatocytes",
+    "CPCs / MSCs",
+    "CPCs ",
+    "cardiac fibroblasts",
+    "iPSCs-derived cardiomyocytes",
+    "cardiomyocytes/ fibroblasts",
+    "hiPSC-CM",
+    "iPSCs/HUVECs",
+    "iPSC-CMs",
+    "iPSCs",
+    "H1395",
+    "PC9",
+    "H1650",
+    "HULEC-5a",
+    "NCI-H1703"
+]
+
+PRINT_PARAM_NAMES = [
+    "Physical Crosslinking Duration (s)",
+    "Photo Crosslinking Duration (s)",
+    "Extrusion Pressure (kPa)",
+    "Nozzle Movement Speed (mm/s)",
+    "Nozzle Diameter (µm)",
+    "Syringe Temperature (°C)",
+    "Substrate Temperature (°C)",
+]
+
+# ─── Load Encoder, Scalers, Models ────────────────────────────────────────────────
+@st.cache_resource
+def load_encoder():
+    return joblib.load('cell_line_encoder.joblib')
+
+@st.cache_resource
+def load_scalers():
+    return (
+        joblib.load('scaler_printability.joblib'),
+        joblib.load('scaler_cell_response.joblib')
+    )
+
+@st.cache_resource
+def load_models():
+    m_p = CatBoostClassifier(); m_p.load_model('catboost_printability.cbm')
+    m_c = CatBoostClassifier(); m_c.load_model('catboost_cell_response.cbm')
+    return m_p, m_c
+
+encoder          = load_encoder()
+scaler_print, scaler_cell = load_scalers()
+model_print,   model_cell = load_models()
+
+feature_order_print = list(scaler_print.feature_names_in_)
+feature_order_cell  = list(scaler_cell.feature_names_in_)
+
+# ─── Session State Initialization ────────────────────────────────────────────────
+if 'bio_rows' not in st.session_state:
+    st.session_state.bio_rows = [{
+        'mat': BIOMATERIAL_OPTIONS[0],
+        'min': 0.0, 'max': 10.0, 'step': 0.1
+    }]
+
+if 'density_range' not in st.session_state:
+    st.session_state.density_range = {'min': 0.0, 'max': 10.0, 'step': 0.1}
+
+if 'pp_ranges' not in st.session_state:
+    st.session_state.pp_ranges = {
+        name: {'min': 0.0, 'max': 10.0, 'step': 3.0}
+        for name in PRINT_PARAM_NAMES
+    }
+
+# ─── App Layout ──────────────────────────────────────────────────────────────────
+st.title("🧬 MLATE: Machine Learning Applications in Tissue Engieering")
+st.markdown(
+    "<p style='font-size:0.8em; color:grey;'>"
+    "An integrated platform for predicting the quality of 3D (bio)printed scaffolds "
+    "in tissue engineering. For more details, please refer to and cite our paper: "
+    "<a href='https://doi.org/xxx' target='_blank'>https://doi.org/xxx</a>"
+    "</p>",
+    unsafe_allow_html=True
+)
+
+# ─── Biomaterials Section ────────────────────────────────────────────────────────
+st.subheader("Biomaterials (enter range for each)")
+if st.button("➕ Add Biomaterial"):
+    used = {r['mat'] for r in st.session_state.bio_rows}
+    available = [m for m in BIOMATERIAL_OPTIONS if m not in used]
+    if available:
+        st.session_state.bio_rows.append({
+            'mat': available[0], 'min': 0.0, 'max': 10.0, 'step': 0.1
+        })
+    st.rerun()
+
+for i, row in enumerate(st.session_state.bio_rows):
+    used_except_current = {
+        r['mat'] for idx, r in enumerate(st.session_state.bio_rows) if idx != i
+    }
+    options = [m for m in BIOMATERIAL_OPTIONS if m not in used_except_current]
+
+    c1, c2, c3, c4, c5 = st.columns([2, 1, 1, 1, 0.3])
+    mat = c1.selectbox(
+        "", options,
+        index=options.index(row['mat']) if row['mat'] in options else 0,
+        key=f"bio_mat_{i}"
+    )
+    st.session_state.bio_rows[i]['mat'] = mat
+
+    mn = c2.number_input(
+        "Min", min_value=0.0, max_value=row['max'],
+        value=row['min'], step=row['step'], key=f"bio_min_{i}"
+    )
+    # ← min_value for Max is now the step; default value at least step
+    mx = c3.number_input(
+        "Max", min_value=row['step'], max_value=100.0,
+        value=max(row['max'], row['step']), step=row['step'],
+        key=f"bio_max_{i}"
+    )
+    st.session_state.bio_rows[i].update(min=mn, max=mx)
+
+    st.session_state.bio_rows[i]['step'] = c4.number_input(
+        "Step", min_value=0.0,
+        max_value=(mx - mn) if mx > mn else 0.1,
+        value=row['step'], step=0.1, key=f"bio_step_{i}"
+    )
+
+    if c5.button("❌", key=f"rem_{i}"):
+        st.session_state.bio_rows.pop(i)
+        st.rerun()
+
+st.markdown("---")
+
+# ─── Cell Line & Density Section ────────────────────────────────────────────────
+st.subheader("Cell Line & Density")
+col1, col2, col3, col4 = st.columns([2,1,1,1])
+cell_line = col1.selectbox("Cell Line", CELL_LINE_OPTIONS)
+
+dr = st.session_state.density_range
+dmin = col2.number_input(
+    "Min Density", min_value=0.0, max_value=dr['max'],
+    value=dr['min'], step=dr['step'], key="cd_min"
+)
+# ← ensure Max Density cannot go below the step
+dmax = col3.number_input(
+    "Max Density", min_value=dr['step'], max_value=1000.0,
+    value=max(dr['max'], dr['step']), step=dr['step'], key="cd_max"
+)
+dr.update(min=dmin, max=dmax)
+
+dr['step'] = col4.number_input(
+    "Step", min_value=0.0,
+    max_value=(dmax - dmin) if dmax > dmin else 0.1,
+    value=dr['step'], step=0.1, key="cd_step"
+)
+
+st.markdown("---")
+
+# ─── Printing Parameters Section ────────────────────────────────────────────────
+st.subheader("Printing Parameters (enter range)")
+for name in PRINT_PARAM_NAMES:
+    pmin  = st.session_state.pp_ranges[name]['min']
+    pmax  = st.session_state.pp_ranges[name]['max']
+    pstep = st.session_state.pp_ranges[name]['step']
+
+    c1, c2, c3, c4 = st.columns([2,1,1,1])
+    c1.write(name)
+    pmin = c2.number_input(
+        "Min", min_value=0.0, max_value=pmax,
+        value=pmin, step=pstep, key=f"pp_min_{name}"
+    )
+    # ← enforce Max ≥ step
+    pmax = c3.number_input(
+        "Max", min_value=pstep, max_value=10000.0,
+        value=max(pmax, pstep), step=pstep, key=f"pp_max_{name}"
+    )
+    pstep = c4.number_input(
+        "Step", min_value=0.0,
+        max_value=(pmax - pmin) if pmax > pmin else 1.0,
+        value=pstep, step=max(1e-3, pstep/10),
+        key=f"pp_step_{name}"
+    )
+    st.session_state.pp_ranges[name].update(min=pmin, max=pmax, step=pstep)
+
+st.markdown("---")
+
+# ─── Optuna Optimize & Display ──────────────────────────────────────────────────
+if st.button("🛠️ Optimize WSSQ"):
+    with st.spinner("Running Optuna…"):
+        def objective(trial):
+            bi_vals = {
+                r['mat']: trial.suggest_float(
+                    f"bio__{r['mat']}", r['min'], r['max'], step=r['step']
+                )
+                for r in st.session_state.bio_rows
+            }
+            for m in BIOMATERIAL_OPTIONS:
+                bi_vals.setdefault(m, 0.0)
+
+            cd = 0.0 if cell_line=="NoCellCultured" else trial.suggest_float(
+                "cell_density", dr['min'], dr['max'], step=dr['step']
+            )
+
+            pp_vals = {
+                name: trial.suggest_float(
+                    f"pp__{name}",
+                    st.session_state.pp_ranges[name]['min'],
+                    st.session_state.pp_ranges[name]['max'],
+                    step=st.session_state.pp_ranges[name]['step']
+                )
+                for name in PRINT_PARAM_NAMES
+            }
+
+            feat = {**bi_vals, **pp_vals}
+            feat["Cell Density (cells/mL)"] = cd
+            feat.update(
+                encoder.transform(pd.DataFrame({"Cell Line":[cell_line]}))
+                       .iloc[0].to_dict()
+            )
+
+            X = pd.DataFrame([feat])
+            Xp = X.reindex(columns=feature_order_print, fill_value=0.0)
+            Xc = X.reindex(columns=feature_order_cell,  fill_value=0.0)
+
+            p_proba = model_print.predict_proba(scaler_print.transform(Xp))[0]
+            c_proba = model_cell .predict_proba(scaler_cell .transform(Xc))[0]
+            exp_p = float(np.dot(p_proba, model_print.classes_.astype(float)))
+            exp_c = float(np.dot(c_proba, model_cell .classes_.astype(float)))
+
+            return scaffold_quality_combined(exp_p, exp_c)
+
+        study = optuna.create_study(direction="maximize")
+        study.optimize(objective, n_trials=50)
+        best = study.best_trial
+
+    st.success(f"🏆 Best WSSQ: **{best.value:.3f}**")
+
+    best_df = pd.Series(best.params, name="value") \
+                .rename_axis("parameter") \
+                .to_frame()
+    st.table(best_df)
+
+    # ─── Fabrication Procedure via GPT ───────────────────────────────────────────────
+    st.markdown("## 🏭 Fabrication Procedure")
+    with st.spinner("Generating fabrication procedure…"):
+        density = best.params.get("cell_density", 0.0)
+        prompt = (
+            "Based on the following optimized scaffold parameters AND your selected cell line:\n\n"
+            f"• Cell line: {cell_line}\n"
+            "Optimized scaffold parameters:\n"
+            f"{best.params}\n\n"
+            "Write a detailed, step-by-step fabrication procedure for 3D (bio)printing "
+            "this scaffold — including materials, equipment, printing settings, and post‑processing."
+        )
+
+        resp = client.models.generate_content(
+            model="gemini-2.0-flash-001",
+            contents=prompt,
+            config=types.GenerateContentConfig(
+                system_instruction=(
+                    "You are a biomedical engineering professor with tissue engineering experties. "
+                    "you know how to print 3d (bio)printed scaffolds in labs."
+                )
+            )
+        )
+
+        procedure = resp.text
+        st.markdown(procedure)

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