Create src/streamlit_app.py

src/streamlit_app.py

@@ -0,0 +1,237 @@
+# dashboard.py
+import os
+os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
+
+import streamlit as st
+import pandas as pd
+import torch
+import pickle
+import matplotlib.pyplot as plt
+import seaborn as sns
+from rdkit import Chem
+from rdkit.Chem import Descriptors
+
+# --------------------------------------------------
+# Page config
+# --------------------------------------------------
+st.set_page_config(
+    page_title="ICH-NOVA | Future-Grade Drug Discovery",
+    layout="wide",
+    initial_sidebar_state="expanded"
+)
+
+# --------------------------------------------------
+# Dark futuristic theme
+# --------------------------------------------------
+st.markdown("""
+<style>
+body { background-color: #0e0e0e; color: #d0d0d0; }
+h1, h2, h3 { color: #f0f0f0; }
+.stButton>button, .stDownloadButton>button {
+    background-color: #1a1a1a;
+    color: #d0d0d0;
+    border: 1px solid #555;
+}
+</style>
+""", unsafe_allow_html=True)
+
+st.title("🧬 ICH-NOVA")
+st.subheader("Regulatory-aware, self-evolving AI drug discovery")
+# --------------------------------------------------
+# Safe loaders
+# --------------------------------------------------
+@st.cache_data
+def load_embeddings():
+    try:
+        with open("data/processed/embeddings.pkl", "rb") as f:
+            return pickle.load(f)
+    except Exception:
+        return {}
+
+@st.cache_data
+def load_results():
+    try:
+        return pd.read_csv("final_candidates_rl.csv")
+    except Exception:
+        return pd.DataFrame()
+
+@st.cache_resource
+def load_stability_model():
+    from src.stability.stability_zone_iv import StabilityPredictor
+    model = StabilityPredictor(input_dim=256, hidden_dim=128)
+    model.load_state_dict(torch.load("models/stability_model.pt", map_location="cpu"))
+    model.eval()
+    return model
+
+embeddings_dict = load_embeddings()
+final_df = load_results()
+stability_model = load_stability_model()
+# --------------------------------------------------
+# TOP: Molecule of Interest (Primary Input)
+# --------------------------------------------------
+st.markdown("### 🧪 Molecule of Interest — Instant Zone-IV Evaluation")
+
+top_col1, top_col2 = st.columns([3, 1])
+
+with top_col1:
+    top_smiles = st.text_input(
+        "Enter SMILES string",
+        placeholder="e.g. CC(=O)OC1=CC=CC=C1C(=O)O"
+    )
+
+with top_col2:
+    top_predict = st.button("Predict Stability")
+
+if top_predict and top_smiles.strip():
+    mol = Chem.MolFromSmiles(top_smiles)
+    if mol:
+        mw = Descriptors.MolWt(mol)
+        emb = embeddings_dict.get(top_smiles, torch.randn(256)).unsqueeze(0)
+        with torch.no_grad():
+            stab = stability_model(emb).item()
+
+        res1, res2 = st.columns(2)
+        res1.metric("Zone-IV Shelf-Life (days)", f"{stab:.2f}")
+        res2.metric("Molecular Weight", f"{mw:.2f}")
+
+        st.success("Prediction completed successfully.")
+    else:
+        st.error("Invalid SMILES string. Please check your input.")
+
+st.markdown("---")
+
+
+
+# --------------------------------------------------
+# Normalize dataframe (CRITICAL FIX)
+# --------------------------------------------------
+required_cols = [
+    "iteration", "smiles", "binding_score",
+    "synthesis_score", "stability_pred",
+    "reward", "admet_pass"
+]
+
+for col in required_cols:
+    if col not in final_df.columns:
+        final_df[col] = None
+
+# If admet_pass missing → infer safely
+final_df["admet_pass"] = final_df["admet_pass"].fillna(
+    final_df["stability_pred"].notna()
+)
+
+# --------------------------------------------------
+# ADMET summary
+# --------------------------------------------------
+st.subheader("🧪 ADMET Filtering Summary")
+
+total = len(final_df)
+passed = int(final_df["admet_pass"].sum())
+failed = total - passed
+
+c1, c2, c3 = st.columns(3)
+c1.metric("Total Molecules", total)
+c2.metric("Passed ADMET", passed)
+c3.metric("Rejected", failed)
+
+# --------------------------------------------------
+# Sidebar: Molecule explorer
+# --------------------------------------------------
+st.sidebar.header("🧪 Molecule Explorer")
+user_smiles = st.sidebar.text_input("Enter SMILES")
+
+if st.sidebar.button("Predict"):
+    if user_smiles.strip():
+        mol = Chem.MolFromSmiles(user_smiles)
+        if mol:
+            mw = Descriptors.MolWt(mol)
+            emb = embeddings_dict.get(user_smiles, torch.randn(256)).unsqueeze(0)
+            with torch.no_grad():
+                stab = stability_model(emb).item()
+            st.sidebar.success(f"Zone-IV Shelf-life: {stab:.2f} days")
+            st.sidebar.info(f"Molecular Weight: {mw:.2f}")
+        else:
+            st.sidebar.error("Invalid SMILES")
+
+# --------------------------------------------------
+# Molecule table
+# --------------------------------------------------
+st.header("Generated Molecules")
+st.dataframe(
+    final_df.sort_values("admet_pass", ascending=False),
+    use_container_width=True
+)
+
+st.download_button(
+    "📥 Download CSV",
+    final_df.to_csv(index=False),
+    "ICH_NOVA_results.csv",
+    "text/csv"
+)
+
+# --------------------------------------------------
+# Visualizations (SAFE)
+# --------------------------------------------------
+st.header("Visual Analysis")
+
+numeric_df = final_df.dropna(subset=["reward", "binding_score", "stability_pred"])
+
+# Reward vs Iteration
+if not numeric_df.empty:
+    st.subheader("Average RL Reward per Iteration")
+    fig, ax = plt.subplots()
+    numeric_df.groupby("iteration")["reward"].mean().plot(ax=ax)
+    ax.set_xlabel("Iteration")
+    ax.set_ylabel("Reward")
+    st.pyplot(fig)
+
+    st.subheader("Binding vs Stability")
+    fig, ax = plt.subplots()
+    sns.scatterplot(
+        data=numeric_df,
+        x="stability_pred",
+        y="binding_score",
+        hue="iteration",
+        ax=ax
+    )
+    st.pyplot(fig)
+
+    st.subheader("Reward vs Stability")
+    fig, ax = plt.subplots()
+    sc = ax.scatter(
+        numeric_df["stability_pred"],
+        numeric_df["binding_score"],
+        c=numeric_df["reward"],
+        cmap="viridis"
+    )
+    plt.colorbar(sc, ax=ax)
+    st.pyplot(fig)
+else:
+    st.warning("Not enough numeric data for plots yet.")
+
+# --------------------------------------------------
+# Ablation (optional & safe)
+# --------------------------------------------------
+st.header("Module Ablation Analysis")
+
+try:
+    abl = pd.read_csv("ablation_results.csv")
+    num = abl.select_dtypes(include="number")
+    if not num.empty:
+        plot_df = num.melt(var_name="Module", value_name="Reward")
+        fig, ax = plt.subplots()
+        sns.boxplot(data=plot_df, x="Module", y="Reward", ax=ax)
+        st.pyplot(fig)
+    else:
+        st.info("Ablation file contains no numeric data.")
+except Exception:
+    st.info("Ablation results not available.")
+
+# --------------------------------------------------
+# Footer
+# --------------------------------------------------
+st.markdown("---")
+st.markdown(
+    "<center style='color:#888'>ICH-NOVA | Self-evolving AI for real-world drug discovery</center>",
+    unsafe_allow_html=True
+)