Remove GitHub and HF links

src/report_assistant.py

@@ -1,94 +1,93 @@
-"""
-Tri-Netra — Report Assistant
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-Generates a structured prompt template for a medical LLM to draft a brief,
-3-sentence professional diagnostic note based on MRI analysis findings.
-
-Author : Anannya Vyas
-Email  : vyasanannya@gmail.com
-GitHub : https://github.com/Anannya-Vyas/Tri-Netra-AI
-"""
-
-from __future__ import annotations
-
-
-def generate_diagnostic_prompt(
-    prediction_pct: float,
-    tumor_type: str | None = None,
-    timestamp: str | None = None,
-) -> str:
-    """Build an LLM prompt that requests a concise radiology-style diagnostic note.
-
-    Parameters
-    ----------
-    prediction_pct : float
-        Classification confidence as a percentage (0–100).
-    tumor_type : str or None
-        Tumor subtype identified by segmentation (e.g. "glioma",
-        "meningioma", "pituitary").  Pass ``None`` or an empty string
-        when segmentation was not performed or no tumor was detected.
-    tumor_type : str or None
-        Timestamp of the inference run (ISO-8601 or any human-readable
-        format).  Included in the findings block so the note is traceable.
-
-    Returns
-    -------
-    str
-        A ready-to-send prompt string for any medical-capable LLM.
-    """
-
-    # ── Build the structured findings block ──────────────────────────
-    tumor_label = tumor_type.strip() if tumor_type else "Not segmented / No tumor detected"
-    ts_label = timestamp.strip() if timestamp else "N/A"
-
-    confidence_descriptor = (
-        "high" if prediction_pct >= 85
-        else "moderate" if prediction_pct >= 50
-        else "low"
-    )
-
-    findings_block = (
-        f"  - Classification confidence : {prediction_pct:.1f}% ({confidence_descriptor})\n"
-        f"  - Tumor type (segmentation) : {tumor_label}\n"
-        f"  - Analysis timestamp        : {ts_label}"
-    )
-
-    # -- Assemble the full prompt -------------------------------------
-    prompt = (
-        "You are a board-certified neuroradiologist assistant AI.\n"
-        "Based on the automated MRI analysis findings below, draft a\n"
-        "professional diagnostic note for a radiologist's review.\n"
-        "\n"
-        "RULES:\n"
-        "  1. Write exactly THREE concise sentences.\n"
-        "  2. Sentence 1: State the primary finding (tumor presence/absence\n"
-        "     and type, if available).\n"
-        "  3. Sentence 2: Note the model's confidence level and any\n"
-        "     clinical implication that warrants attention.\n"
-        "  4. Sentence 3: Recommend a follow-up action (e.g. biopsy,\n"
-        "     additional imaging, clinical correlation).\n"
-        "  5. Use formal medical language appropriate for a radiology report.\n"
-        "  6. Do NOT fabricate patient demographics or history.\n"
-        "\n"
-        "--- AUTOMATED FINDINGS ---------------------------\n"
-        f"{findings_block}\n"
-        "--------------------------------------------------\n"
-        "\n"
-        "Diagnostic Note:"
-    )
-
-    return prompt
-
-
-# ── CLI demo ─────────────────────────────────────────────────────────
-if __name__ == "__main__":
-    sample_prompt = generate_diagnostic_prompt(
-        prediction_pct=92.4,
-        tumor_type="Glioma (High-Grade)",
-        timestamp="2026-06-26T23:30:00+05:30",
-    )
-    print("=" * 60)
-    print("SAMPLE LLM PROMPT")
-    print("=" * 60)
-    print(sample_prompt)
-    print("=" * 60)
+"""
+Tri-Netra — Report Assistant
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Generates a structured prompt template for a medical LLM to draft a brief,
+3-sentence professional diagnostic note based on MRI analysis findings.
+
+Author : Anannya Vyas
+Email  : vyasanannya@gmail.com
+"""
+
+from __future__ import annotations
+
+
+def generate_diagnostic_prompt(
+    prediction_pct: float,
+    tumor_type: str | None = None,
+    timestamp: str | None = None,
+) -> str:
+    """Build an LLM prompt that requests a concise radiology-style diagnostic note.
+
+    Parameters
+    ----------
+    prediction_pct : float
+        Classification confidence as a percentage (0–100).
+    tumor_type : str or None
+        Tumor subtype identified by segmentation (e.g. "glioma",
+        "meningioma", "pituitary").  Pass ``None`` or an empty string
+        when segmentation was not performed or no tumor was detected.
+    tumor_type : str or None
+        Timestamp of the inference run (ISO-8601 or any human-readable
+        format).  Included in the findings block so the note is traceable.
+
+    Returns
+    -------
+    str
+        A ready-to-send prompt string for any medical-capable LLM.
+    """
+
+    # ── Build the structured findings block ──────────────────────────
+    tumor_label = tumor_type.strip() if tumor_type else "Not segmented / No tumor detected"
+    ts_label = timestamp.strip() if timestamp else "N/A"
+
+    confidence_descriptor = (
+        "high" if prediction_pct >= 85
+        else "moderate" if prediction_pct >= 50
+        else "low"
+    )
+
+    findings_block = (
+        f"  - Classification confidence : {prediction_pct:.1f}% ({confidence_descriptor})\n"
+        f"  - Tumor type (segmentation) : {tumor_label}\n"
+        f"  - Analysis timestamp        : {ts_label}"
+    )
+
+    # -- Assemble the full prompt -------------------------------------
+    prompt = (
+        "You are a board-certified neuroradiologist assistant AI.\n"
+        "Based on the automated MRI analysis findings below, draft a\n"
+        "professional diagnostic note for a radiologist's review.\n"
+        "\n"
+        "RULES:\n"
+        "  1. Write exactly THREE concise sentences.\n"
+        "  2. Sentence 1: State the primary finding (tumor presence/absence\n"
+        "     and type, if available).\n"
+        "  3. Sentence 2: Note the model's confidence level and any\n"
+        "     clinical implication that warrants attention.\n"
+        "  4. Sentence 3: Recommend a follow-up action (e.g. biopsy,\n"
+        "     additional imaging, clinical correlation).\n"
+        "  5. Use formal medical language appropriate for a radiology report.\n"
+        "  6. Do NOT fabricate patient demographics or history.\n"
+        "\n"
+        "--- AUTOMATED FINDINGS ---------------------------\n"
+        f"{findings_block}\n"
+        "--------------------------------------------------\n"
+        "\n"
+        "Diagnostic Note:"
+    )
+
+    return prompt
+
+
+# ── CLI demo ─────────────────────────────────────────────────────────
+if __name__ == "__main__":
+    sample_prompt = generate_diagnostic_prompt(
+        prediction_pct=92.4,
+        tumor_type="Glioma (High-Grade)",
+        timestamp="2026-06-26T23:30:00+05:30",
+    )
+    print("=" * 60)
+    print("SAMPLE LLM PROMPT")
+    print("=" * 60)
+    print(sample_prompt)
+    print("=" * 60)