Update src/streamlit_app.py

src/streamlit_app.py

@@ -112,125 +112,6 @@ def predict_prob(x: np.ndarray) -> float:
         prob = float(np.ravel(pred)[0])
     return max(0.0, min(1.0, prob))
 
-# -----------------------------
-
-# -----------------------------
-# -----------------------------
-# Grad-CAM (robust for nested ResNet backbone)
-# -----------------------------
-@st.cache_resource
-def get_backbone_and_last_conv():
-    backbone_name = "resnet50"   # your model has this layer name
-    backbone = model.get_layer(backbone_name)
-
-    last_conv = None
-    for lyr in reversed(backbone.layers):
-        if isinstance(lyr, tf.keras.layers.Conv2D):
-            last_conv = lyr.name
-            break
-
-    if last_conv is None:
-        raise ValueError("No Conv2D layer found inside resnet50 backbone.")
-
-    # model that outputs the conv feature map
-    conv_model = keras.Model(
-        inputs=model.inputs,
-        outputs=backbone.get_layer(last_conv).output
-    )
-
-    return backbone_name, last_conv, conv_model
-
-def make_gradcam_heatmap(img_batch: np.ndarray) -> np.ndarray:
-    _, last_conv, conv_model = get_backbone_and_last_conv()
-
-    x = tf.convert_to_tensor(img_batch, dtype=tf.float32)
-
-    with tf.GradientTape() as tape:
-        conv_out = conv_model(x, training=False)   # (1, h, w, ch)
-
-        preds = model(x, training=False)
-        if isinstance(preds, (list, tuple)):
-            prob = preds[-1]
-        else:
-            prob = preds
-
-        loss = prob[:, 0]  # binary prob
-
-    grads = tape.gradient(loss, conv_out)
-    if grads is None:
-        raise ValueError(
-            f"Gradients are None (cannot compute Grad-CAM). Last conv was: {last_conv}"
-        )
-
-    pooled_grads = tf.reduce_mean(grads, axis=(0, 1, 2))
-    conv_out = conv_out[0]
-
-    heatmap = tf.reduce_sum(conv_out * pooled_grads, axis=-1)
-    heatmap = tf.maximum(heatmap, 0.0)
-
-    denom = tf.reduce_max(heatmap) + 1e-8
-    heatmap = heatmap / denom
-
-    return heatmap.numpy()
-
-
-# -----------------------------
-# PDF generation (fix unicode issues)
-# -----------------------------
-# -----------------------------
-# PDF generator (robust wrapping)
-# -----------------------------
-def safe_text(s: str, max_len: int = 220) -> str:
-    if s is None:
-        return ""
-    s = str(s)
-
-    # replace unicode dashes/quotes
-    s = s.replace("–", "-").replace("—", "-").replace("’", "'").replace("“", '"').replace("”", '"')
-
-    # add break opportunities for long tokens (UUIDs, filenames)
-    s = s.replace("-", "- ").replace("_", "_ ").replace("/", "/ ")
-
-    # latin-1 safe
-    s = s.encode("latin-1", "replace").decode("latin-1")
-
-    # trim
-    if len(s) > max_len:
-        s = s[:max_len] + "..."
-    return s
-
-def build_pdf_report(df_ok: pd.DataFrame, threshold: float, model_version: str) -> bytes:
-    pdf = FPDF()
-    pdf.set_auto_page_break(auto=True, margin=12)
-    pdf.add_page()
-
-    pdf.set_font("Helvetica", size=12)
-
-    # effective width (prevents “not enough horizontal space”)
-    w = pdf.w - pdf.l_margin - pdf.r_margin
-
-    pdf.cell(0, 8, safe_text("Pneumonia Detection Report"), ln=True)
-    pdf.set_font("Helvetica", size=10)
-    pdf.cell(0, 6, safe_text(f"Generated: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}"), ln=True)
-    pdf.cell(0, 6, safe_text(f"Model Version: {model_version}"), ln=True)
-    pdf.cell(0, 6, safe_text(f"Decision Threshold: {threshold:.2f}"), ln=True)
-    pdf.ln(4)
-
-    for _, row in df_ok.iterrows():
-        lines = [
-            f"File: {row['file_name']}",
-            f"Probability: {row['probability']*100:.2f}%",
-            f"Confidence: {row['confidence_level']}",
-            f"Prediction: {row['prediction']}",
-        ]
-        for line in lines:
-            pdf.multi_cell(w, 6, safe_text(line))
-        pdf.ln(2)
-
-    out = pdf.output(dest="S")
-    if isinstance(out, str):
-        out = out.encode("latin-1", "ignore")
-    return out
 
 
 # -----------------------------