Update app.py

app.py

@@ -1,7 +1,134 @@
+# app.py — Self-Sensing Concrete (Prediction + Literature Q&A)
+# --------------------------------------------------------------------------------------
+# Tab 1: Your Stress GF predictor UI (unchanged core logic)
+# Tab 2: Original Gradio ChatInterface template (HF Inference API)
+# --------------------------------------------------------------------------------------
+
+import os
+import joblib
+import numpy as np
+import pandas as pd
 import gradio as gr
 from huggingface_hub import InferenceClient
 
+# ========================= Predictor (your code) =========================
+CF_COL = "Conductive Filler Conc. (wt%)"
+TARGET_COL = "Stress GF (MPa-1)"
+
+MAIN_VARIABLES = [
+    "Filler 1 Type","Filler 1 Diameter (µm)","Filler 1 Length (mm)",
+    CF_COL,
+    "Filler 1 Dimensionality","Filler 2 Type","Filler 2 Diameter (µm)","Filler 2 Length (mm)",
+    "Filler 2 Dimensionality","Specimen Volume (mm3)","Probe Count","Probe Material",
+    "W/B","S/B","Gauge Length (mm)","Curing Condition","Number of Fillers",
+    "Drying Temperature (°C)","Drying Duration (hr)","Loading Rate (MPa/s)",
+    "Modulus of Elasticity (GPa)","Current Type","Applied Voltage (V)"
+]
+NUMERIC_COLS = {
+    "Filler 1 Diameter (µm)","Filler 1 Length (mm)", CF_COL,
+    "Filler 2 Diameter (µm)","Filler 2 Length (mm)","Specimen Volume (mm3)","Probe Count",
+    "W/B","S/B","Gauge Length (mm)","Number of Fillers",
+    "Drying Temperature (°C)","Drying Duration (hr)","Loading Rate (MPa/s)",
+    "Modulus of Elasticity (GPa)","Applied Voltage (V)"
+}
+CATEGORICAL_COLS = {
+    "Filler 1 Type","Filler 1 Dimensionality","Filler 2 Type","Filler 2 Dimensionality",
+    "Probe Material","Curing Condition","Current Type"
+}
+DIM_CHOICES = ["0D", "1D", "2D", "3D", "NA"]
+CURRENT_CHOICES = ["DC", "AC", "NA"]
+
+MODEL_CANDIDATES = [
+    "stress_gf_xgb.joblib",
+    "models/stress_gf_xgb.joblib",
+    "/home/user/app/stress_gf_xgb.joblib",
+]
+
+def _load_model_or_error():
+    for p in MODEL_CANDIDATES:
+        if os.path.exists(p):
+            try:
+                return joblib.load(p)
+            except Exception as e:
+                return f"Could not load model from {p}: {e}"
+    return ("Model file not found. Upload your trained pipeline as "
+            "`stress_gf_xgb.joblib` (or put it in `models/`).")
+
+def _coerce_to_row(form_dict: dict) -> pd.DataFrame:
+    row = {}
+    for col in MAIN_VARIABLES:
+        v = form_dict.get(col, None)
+        if col in NUMERIC_COLS:
+            if v in ("", None):
+                row[col] = np.nan
+            else:
+                try:
+                    row[col] = float(v)
+                except:
+                    row[col] = np.nan
+        else:
+            # treat "NA" as empty category so imputers/OneHot handle it
+            row[col] = "" if v in (None, "NA") else str(v).strip()
+    return pd.DataFrame([row], columns=MAIN_VARIABLES)
+
+def predict_fn(**kwargs):
+    mdl = _load_model_or_error()
+    if isinstance(mdl, str):
+        return mdl
+
+    X_new = _coerce_to_row(kwargs)
+    try:
+        y_log = mdl.predict(X_new)            # model predicts log1p(target)
+        y = float(np.expm1(y_log)[0])         # back to original scale MPa^-1
+        if -1e-10 < y < 0:                    # clip tiny negative noise
+            y = 0.0
+        return y
+    except Exception as e:
+        return f"Prediction error: {e}"
+
+EXAMPLE = {
+    "Filler 1 Type": "CNT",
+    "Filler 1 Dimensionality": "1D",
+    "Filler 1 Diameter (µm)": 0.02,
+    "Filler 1 Length (mm)": 1.2,
+    CF_COL: 0.5,
+    "Filler 2 Type": "",
+    "Filler 2 Dimensionality": "NA",
+    "Filler 2 Diameter (µm)": None,
+    "Filler 2 Length (mm)": None,
+    "Specimen Volume (mm3)": 1000,
+    "Probe Count": 2,
+    "Probe Material": "Copper",
+    "W/B": 0.4,
+    "S/B": 2.5,
+    "Gauge Length (mm)": 20,
+    "Curing Condition": "28d water, 20°C",
+    "Number of Fillers": 1,
+    "Drying Temperature (°C)": 60,
+    "Drying Duration (hr)": 24,
+    "Loading Rate (MPa/s)": 0.1,
+    "Modulus of Elasticity (GPa)": 25,
+    "Current Type": "DC",
+    "Applied Voltage (V)": 5.0,
+}
+
+def _fill_example():
+    return [EXAMPLE.get(k, None) for k in MAIN_VARIABLES]
 
+def _clear_all():
+    cleared = []
+    for col in MAIN_VARIABLES:
+        if col in NUMERIC_COLS:
+            cleared.append(None)
+        elif col in {"Filler 1 Dimensionality","Filler 2 Dimensionality"}:
+            cleared.append("NA")
+        elif col == "Current Type":
+            cleared.append("NA")
+        else:
+            cleared.append("")
+    return cleared
+
+# ========================= Chat (original template) =========================
 def respond(
     message,
     history: list[dict[str, str]],
@@ -12,59 +139,152 @@ def respond(
     hf_token: gr.OAuthToken,
 ):
     """
-    For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
+    Unmodified template logic; you can swap the model below to your preferred one
+    (or rewrite to call your own RAG stack instead of Inference API).
     """
     client = InferenceClient(token=hf_token.token, model="openai/gpt-oss-20b")
-
     messages = [{"role": "system", "content": system_message}]
-
     messages.extend(history)
-
     messages.append({"role": "user", "content": message})
 
     response = ""
-
-    for message in client.chat_completion(
+    for msg in client.chat_completion(
         messages,
         max_tokens=max_tokens,
         stream=True,
         temperature=temperature,
         top_p=top_p,
     ):
-        choices = message.choices
+        choices = msg.choices
         token = ""
         if len(choices) and choices[0].delta.content:
             token = choices[0].delta.content
-
         response += token
         yield response
 
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-chatbot = gr.ChatInterface(
+chat_ui = gr.ChatInterface(
     respond,
     type="messages",
     additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
+        gr.Textbox(value="You are a materials-science assistant for self-sensing composites. Cite sources when possible.",
+                   label="System message"),
         gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
         gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
+        gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"),
     ],
 )
 
-with gr.Blocks() as demo:
+# ========================= Combined UI =========================
+CSS = """
+.gradio-container {background: linear-gradient(135deg,#0f172a 0%, #1f2937 60%, #334155 100%);}
+* {font-family: ui-sans-serif, system-ui, -apple-system, 'Segoe UI', Roboto, 'Helvetica Neue', Arial;}
+.card {background: rgba(255,255,255,0.04) !important; border: 1px solid rgba(255,255,255,0.08);}
+label.svelte-1ipelgc {color: #e5e7eb !important;}
+"""
+
+theme = gr.themes.Soft(primary_hue="blue", neutral_hue="slate").set(
+    body_background_fill="#0f172a",
+    body_text_color="#e5e7eb",
+    input_background_fill="#111827",
+    input_border_color="#1f2937",
+    button_primary_background_fill="#3b82f6",
+    button_primary_text_color="#ffffff",
+    button_secondary_background_fill="#111827",
+)
+
+with gr.Blocks(css=CSS, theme=theme, fill_height=True) as demo:
     with gr.Sidebar():
+        gr.Markdown("### Sign in for Inference API")
         gr.LoginButton()
-    chatbot.render()
 
+    gr.Markdown(
+        "<h1 style='margin:0'>Self-Sensing Materials Assistant</h1>"
+        "<p style='opacity:.9'>Predict stress gauge factor or ask literature questions.</p>"
+    )
+
+    with gr.Tabs():
+        # ---------------- Tab 1: Prediction ----------------
+        with gr.Tab("Predict Stress Gauge Factor"):
+            with gr.Row():
+                with gr.Column(scale=7):
+                    with gr.Accordion("Primary conductive filler", open=True, elem_classes=["card"]):
+                        f1_type   = gr.Textbox(label="Filler 1 Type", placeholder="e.g., CNT, Graphite, Steel fiber")
+                        f1_diam   = gr.Number(label="Filler 1 Diameter (µm)")
+                        f1_len    = gr.Number(label="Filler 1 Length (mm)")
+                        cf_conc   = gr.Number(label=f"{CF_COL}", info="Weight percent of total binder")
+                        f1_dim    = gr.Dropdown(DIM_CHOICES, value="NA", label="Filler 1 Dimensionality")
+
+                    with gr.Accordion("➕ Secondary filler (optional)", open=False, elem_classes=["card"]):
+                        f2_type   = gr.Textbox(label="Filler 2 Type", placeholder="Optional")
+                        f2_diam   = gr.Number(label="Filler 2 Diameter (µm)")
+                        f2_len    = gr.Number(label="Filler 2 Length (mm)")
+                        f2_dim    = gr.Dropdown(DIM_CHOICES, value="NA", label="Filler 2 Dimensionality")
+
+                    with gr.Accordion("Mix design & specimen", open=False, elem_classes=["card"]):
+                        spec_vol  = gr.Number(label="Specimen Volume (mm3)")
+                        probe_cnt = gr.Number(label="Probe Count")
+                        probe_mat = gr.Textbox(label="Probe Material", placeholder="e.g., Copper, Silver paste")
+                        wb        = gr.Number(label="W/B")
+                        sb        = gr.Number(label="S/B")
+                        gauge_len = gr.Number(label="Gauge Length (mm)")
+                        curing    = gr.Textbox(label="Curing Condition", placeholder="e.g., 28d water, 20°C")
+                        n_fillers = gr.Number(label="Number of Fillers")
+
+                    with gr.Accordion("Processing", open=False, elem_classes=["card"]):
+                        dry_temp  = gr.Number(label="Drying Temperature (°C)")
+                        dry_hrs   = gr.Number(label="Drying Duration (hr)")
+
+                    with gr.Accordion("Mechanical & electrical loading", open=False, elem_classes=["card"]):
+                        load_rate = gr.Number(label="Loading Rate (MPa/s)")
+                        E_mod     = gr.Number(label="Modulus of Elasticity (GPa)")
+                        current   = gr.Dropdown(CURRENT_CHOICES, value="NA", label="Current Type")
+                        voltage   = gr.Number(label="Applied Voltage (V)")
+
+                with gr.Column(scale=5):
+                    with gr.Group(elem_classes=["card"]):
+                        out_pred = gr.Number(label="Predicted Stress GF (MPa-1)", precision=6)
+                        with gr.Row():
+                            btn_pred  = gr.Button("Predict", variant="primary")
+                            btn_clear = gr.Button("Clear")
+                            btn_demo  = gr.Button("Fill Example")
+
+                    with gr.Accordion("About this model", open=False, elem_classes=["card"]):
+                        gr.Markdown(
+                            "- Pipeline: ColumnTransformer → (RobustScaler + OneHot) → XGBoost\n"
+                            "- Target: Stress GF (MPa⁻¹) on original scale (trained with log1p).\n"
+                            "- Missing values are safely imputed per-feature.\n"
+                            f"- Trained columns: `{', '.join(MAIN_VARIABLES)}`"
+                        )
+
+            # IMPORTANT: inputs order must match MAIN_VARIABLES exactly:
+            inputs_in_order = [
+                # MAIN_VARIABLES order:
+                # "Filler 1 Type","Filler 1 Diameter (µm)","Filler 1 Length (mm)", CF_COL,
+                # "Filler 1 Dimensionality","Filler 2 Type","Filler 2 Diameter (µm)","Filler 2 Length (mm)",
+                # "Filler 2 Dimensionality","Specimen Volume (mm3)","Probe Count","Probe Material",
+                # "W/B","S/B","Gauge Length (mm)","Curing Condition","Number of Fillers",
+                # "Drying Temperature (°C)","Drying Duration (hr)","Loading Rate (MPa/s)",
+                # "Modulus of Elasticity (GPa)","Current Type","Applied Voltage (V)"
+                f1_type, f1_diam, f1_len, cf_conc,
+                f1_dim,  f2_type, f2_diam, f2_len,
+                f2_dim,  spec_vol, probe_cnt, probe_mat,
+                wb, sb, gauge_len, curing, n_fillers,
+                dry_temp, dry_hrs, load_rate,
+                E_mod, current, voltage
+            ]
+
+            def _predict_wrapper(*vals):
+                data = {k: v for k, v in zip(MAIN_VARIABLES, vals)}
+                return predict_fn(**data)
+
+            btn_pred.click(_predict_wrapper, inputs=inputs_in_order, outputs=out_pred)
+            btn_clear.click(lambda: _clear_all(), inputs=None, outputs=inputs_in_order)
+            btn_demo.click(lambda: _fill_example(), inputs=None, outputs=inputs_in_order)
+
+        # ---------------- Tab 2: Literature Q&A (template) ----------------
+        with gr.Tab("Ask the Literature (Chat)"):
+            chat_ui.render()
 
+# ------------- Launch -------------
 if __name__ == "__main__":
-    demo.launch()
+    demo.queue().launch()