Update license to CC BY 4.0; remove obsolete test files and README sections

LICENSE

@@ -0,0 +1,396 @@
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README.md

@@ -7,7 +7,7 @@ sdk: gradio
 sdk_version: "5.12.0"
 app_file: app.py
 pinned: false
-license: apache-2.0
+license: cc-by-4.0
 tags:
   - medgemma
   - medical-imaging
@@ -52,9 +52,9 @@ The system runs a **4-agent pipeline** orchestrated by [LangGraph](https://langc
 | Agent | Role | Model | Key Design Choice |
 |:------|:-----|:------|:------------------|
 | **Diagnostician** | Independent image + clinical analysis | [MedGemma 4B-IT](https://huggingface.co/google/medgemma-1.5-4b-it) (multimodal) | **Blinded** — never sees the doctor's diagnosis. Tags each finding as `imaging`, `clinical`, or `both` to distinguish evidence sources. |
-| **Bias Detector** | Compare doctor vs. AI findings | [MedGemma](https://huggingface.co/google/medgemma-1.5-4b-it) 4B/27B + [MedSigLIP](https://huggingface.co/google/medsiglip-448) | Uses **zero-shot image classification** to verify radiological signs. Flags clinical red flags ignored by either assessment. |
-| **Devil's Advocate** | Adversarial challenge | [MedGemma](https://huggingface.co/google/medgemma-27b-text-it) 4B/27B | Deliberately contrarian — uses both imaging and clinical evidence to argue for **[must-not-miss diagnoses](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6775443/)** |
-| **Consultant** | Synthesize final report | [MedGemma](https://huggingface.co/google/medgemma-27b-text-it) 4B/27B | Writes as a **collegial consultant**: *"Have you considered..."* not *"You are wrong."* |
+| **Bias Detector** | Compare doctor vs. AI findings | [MedGemma 4B-IT](https://huggingface.co/google/medgemma-1.5-4b-it) + [MedSigLIP](https://huggingface.co/google/medsiglip-448) | Uses **zero-shot image classification** to verify radiological signs. Flags clinical red flags ignored by either assessment. |
+| **Devil's Advocate** | Adversarial challenge | [MedGemma 4B-IT](https://huggingface.co/google/medgemma-1.5-4b-it) | Deliberately contrarian — uses both imaging and clinical evidence to argue for **[must-not-miss diagnoses](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6775443/)** |
+| **Consultant** | Synthesize final report | [MedGemma 4B-IT](https://huggingface.co/google/medgemma-1.5-4b-it) or [27B Text-IT](https://huggingface.co/google/medgemma-27b-text-it) | Writes as a **collegial consultant**: *"Have you considered..."* not *"You are wrong."* Only this agent optionally upgrades to 27B for deeper reasoning. |
 
 ## Architecture
 
@@ -119,20 +119,13 @@ Three composite clinical scenarios covering the most dangerous diagnostic error
 | Model | Parameters | Role | Loading |
 |:------|:----------|:-----|:--------|
 | [MedGemma 1.5 4B-IT](https://huggingface.co/google/medgemma-1.5-4b-it) | 4B | Multimodal image+text analysis | 4-bit quantized (~4GB VRAM) or BF16 (~8GB) |
-| [MedGemma 27B Text-IT](https://huggingface.co/google/medgemma-27b-text-it) | 27B | Advanced clinical reasoning | BF16 (~54GB VRAM), A100 only |
+| [MedGemma 27B Text-IT](https://huggingface.co/google/medgemma-27b-text-it) | 27B | Consultant deep reasoning (optional) | BF16 (~54GB VRAM) |
 | [MedSigLIP-448](https://huggingface.co/google/medsiglip-448) | 0.9B | Zero-shot sign verification | FP32 (~3GB VRAM) |
 | [MedASR](https://huggingface.co/google/medasr) | 105M | Medical speech-to-text | FP32 (~0.5GB VRAM) |
 
-### Hardware Profiles
+### Hardware
 
-| Environment | GPU | Configuration | VRAM Usage |
-|:------------|:----|:-------------|:-----------|
-| **Local dev** | RTX 4070 12GB | 4B 4-bit + MedSigLIP + MedASR | ~7.5 GB |
-| **School HPC** | A100 80GB | 4B BF16 + **27B BF16** + MedSigLIP + MedASR | ~66 GB |
-| **HF Space** | T4 16GB | 4B 4-bit + MedSigLIP + MedASR | ~7.5 GB |
-| **Kaggle** | T4 16GB | 4B 4-bit + MedSigLIP | ~7 GB |
-
-All models load locally via [Transformers](https://huggingface.co/docs/transformers) with optional [4-bit quantization](https://huggingface.co/docs/bitsandbytes) — **zero API costs, fully offline-capable**.
+The full pipeline (4B 4-bit + MedSigLIP + MedASR) requires **~8 GB VRAM** and runs on any CUDA GPU with 12GB+ memory. All models load locally via [Transformers](https://huggingface.co/docs/transformers) with [4-bit quantization](https://huggingface.co/docs/bitsandbytes) — **zero API costs, fully offline-capable**.
 
 ### Key Technical Decisions
 
@@ -144,7 +137,7 @@ All models load locally via [Transformers](https://huggingface.co/docs/transform
 
 - **Thinking token stripping**: MedGemma wraps internal reasoning in `<unused94>...<unused95>` tags ([model card](https://huggingface.co/google/medgemma-27b-text-it#thinking-mode)). These are stripped via regex before display.
 
-- **Adaptive model routing**: `generate_text()` automatically routes to 27B when `USE_27B=true`, else falls back to 4B. `generate_with_image()` always uses 4B (only model with vision).
+- **Adaptive model routing**: The first three agents (Diagnostician, Bias Detector, Devil's Advocate) always use 4B-IT for multimodal image+text analysis. Only the Consultant (text-only synthesis) optionally upgrades to 27B when `USE_27B=true` for deeper clinical reasoning. `generate_with_image()` always uses 4B (only model with vision).
 
 - **Collegial tone**: The Consultant is prompted to write as a consulting colleague, not a critic. Research shows physicians respond better to [collaborative challenge than confrontation](https://pubmed.ncbi.nlm.nih.gov/28493811/).
 
@@ -162,7 +155,7 @@ All models load locally via [Transformers](https://huggingface.co/docs/transform
 
 ```bash
 # Clone the repository
-git clone https://huggingface.co/spaces/YOUR_USERNAME/diagnostic-devils-advocate
+git clone https://github.com/sypsyp97/diagnostic-devils-advocate
 cd diagnostic-devils-advocate
 
 # Install dependencies
@@ -191,7 +184,7 @@ The app launches at `http://localhost:7860`.
 
 | Variable | Default | Description |
 |:---------|:--------|:------------|
-| `USE_27B` | `false` | Enable 27B model for text-only agents |
+| `USE_27B` | `false` | Enable 27B model for the Consultant agent |
 | `QUANTIZE_4B` | `true` | 4-bit quantize the 4B model |
 | `ENABLE_MEDASR` | `true` | Enable voice input via MedASR |
 | `HF_TOKEN` | — | Hugging Face token (or use `huggingface-cli login`) |
@@ -211,7 +204,7 @@ diagnostic-devils-advocate/
 │   ├── state.py                  # LangGraph TypedDict state definitions
 │   ├── prompts.py                # All agent prompt templates
 │   ├── graph.py                  # LangGraph StateGraph pipeline
-│   ├── output_parser.py          # JSON parsing with json_repair
+│   ├── output_parser.py          # JSON parsing with json_repair + llm-output-parser
 │   ├── diagnostician.py          # Agent 1: Blinded image + clinical analysis
 │   ├── bias_detector.py          # Agent 2: Bias detection + MedSigLIP
 │   ├── devil_advocate.py         # Agent 3: Adversarial challenge
@@ -228,20 +221,9 @@ diagnostic-devils-advocate/
 │   ├── callbacks.py              # UI event handlers & pipeline integration
 │   └── css.py                    # Custom styling (responsive design)
 │
-├── data/
-│   └── demo_cases/               # 3 composite clinical scenarios
-│       └── SOURCES.md            # Full literature citations
-│
-└── tests/
-    ├── test_smoke.py             # Import & build verification
-    ├── test_output_parser.py     # JSON repair tests
-    └── test_pipeline_mock.py     # Integration tests with mocked models
-```
-
-## Testing
-
-```bash
-python -m pytest tests/ -v
+└── data/
+    └── demo_cases/               # 3 composite clinical scenarios
+        └── SOURCES.md            # Full literature citations
 ```
 
 ## Disclaimer

tests/test_output_parser.py

@@ -1,24 +0,0 @@
-import sys
-import os
-
-sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))
-
-from agents.output_parser import parse_json_response
-
-
-def test_parse_json_response_returns_dict():
-    parsed = parse_json_response('{"challenges":[{"claim":"x","counter_evidence":"y"}]}')
-    assert parsed["challenges"][0]["claim"] == "x"
-
-
-def test_parse_json_response_coerces_top_level_list_of_strings():
-    parsed = parse_json_response('["CT angiogram","D-dimer"]')
-    assert parsed["items"] == ["CT angiogram", "D-dimer"]
-
-
-def test_parse_json_response_infers_container_key_for_da_items():
-    parsed = parse_json_response(
-        '[{"diagnosis":"Aortic dissection","why_dangerous":"High mortality","supporting_signs":"Pain radiating to back","rule_out_test":"CTA chest"}]'
-    )
-    assert parsed["must_not_miss"][0]["diagnosis"] == "Aortic dissection"
-

tests/test_pipeline_mock.py

@@ -1,124 +0,0 @@
-"""End-to-end pipeline test with mocked model calls (no GPU required)."""
-
-import os
-import sys
-from unittest.mock import patch
-
-from PIL import Image
-
-sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))
-
-
-def test_pipeline_end_to_end_with_mocks():
-    from agents.graph import run_pipeline
-    from agents.prompts import (
-        DIAGNOSTICIAN_SYSTEM,
-        BIAS_DETECTOR_SYSTEM,
-        DEVIL_ADVOCATE_SYSTEM,
-        CONSULTANT_SYSTEM,
-    )
-
-    dummy_image = Image.new("RGB", (512, 512), color="gray")
-
-    diagnostician_json = """
-    {
-      "findings": [
-        {"finding": "Pneumothorax", "source": "imaging", "description": "Left apical pleural line with absent peripheral markings."},
-        {"finding": "Rib fracture", "source": "imaging", "description": "Possible fracture of the left 5th rib."},
-        {"finding": "Tachycardia", "source": "clinical", "description": "HR 104, consistent with pain or hemodynamic compromise."}
-      ],
-      "differential_diagnoses": [
-        {"diagnosis": "Pneumothorax", "reasoning": "Visible pleural line on imaging combined with tachycardia and dyspnea from clinical context."}
-      ]
-    }
-    """.strip()
-
-    bias_detector_json = """
-    {
-      "discrepancy_summary": "Doctor focused on rib pain; image suggests pneumothorax.",
-      "identified_biases": [
-        {"type": "Anchoring", "evidence": "Trauma mechanism overweighted", "severity": "HIGH"}
-      ],
-      "missed_findings": ["Pneumothorax"],
-      "agreement_points": ["Rib pain consistent with trauma"]
-    }
-    """.strip()
-
-    devil_advocate_json = """
-    {
-      "challenges": [
-        {"claim": "Rib contusion explains symptoms", "counter_evidence": "Dyspnea can reflect pneumothorax severity."}
-      ],
-      "must_not_miss": [
-        {
-          "diagnosis": "Tension pneumothorax",
-          "why_dangerous": "Rapid hemodynamic collapse if untreated",
-          "supporting_signs": "Worsening dyspnea and pleural line",
-          "rule_out_test": "Bedside ultrasound or repeat upright CXR"
-        }
-      ],
-      "recommended_workup": ["Repeat upright chest radiograph", "Point-of-care ultrasound"]
-    }
-    """.strip()
-
-    consultant_json = """
-    {
-      "consultation_note": "Have you considered pneumothorax given the pleural line?\\n\\nI would re-image upright and consider bedside ultrasound.",
-      "alternative_diagnoses": [
-        {
-          "diagnosis": "Pneumothorax",
-          "urgency": "high",
-          "evidence": "Pleural line and absent peripheral markings",
-          "next_step": "Repeat upright CXR or POCUS"
-        }
-      ],
-      "immediate_actions": ["Repeat upright CXR", "POCUS"],
-      "confidence_note": "Based on a single image; clinical correlation required."
-    }
-    """.strip()
-
-    def fake_generate_with_image(_prompt: str, _image, system_prompt: str = "") -> str:
-        if system_prompt == DIAGNOSTICIAN_SYSTEM:
-            return diagnostician_json
-        if system_prompt == BIAS_DETECTOR_SYSTEM:
-            return bias_detector_json
-        if system_prompt.startswith(DEVIL_ADVOCATE_SYSTEM):
-            return devil_advocate_json
-        raise AssertionError(f"Unexpected system_prompt (with image): {system_prompt!r}")
-
-    def fake_generate_text(_prompt: str, system_prompt: str = "") -> str:
-        if system_prompt == CONSULTANT_SYSTEM:
-            return consultant_json
-        raise AssertionError(f"Unexpected system_prompt: {system_prompt!r}")
-
-    with patch("models.medgemma_client.generate_with_image", side_effect=fake_generate_with_image), patch(
-        "models.medgemma_client.generate_text",
-        side_effect=fake_generate_text,
-    ), patch(
-        "models.medsiglip_client.verify_findings",
-        return_value=[{"sign": "pneumothorax", "confidence": "likely present"}],
-    ):
-        result = run_pipeline(
-            image=dummy_image,
-            doctor_diagnosis="Rib contusion",
-            clinical_context="32M, trauma, left chest pain, mild dyspnea.",
-            modality="CXR",
-        )
-
-    assert result.get("error") is None
-
-    diag = result.get("diagnostician_output") or {}
-    assert diag.get("findings_list"), "Diagnostician findings_list missing"
-    assert diag.get("analysis"), "Diagnostician analysis missing"
-
-    bias = result.get("bias_detector_output") or {}
-    assert bias.get("discrepancy_summary")
-    assert bias.get("identified_biases"), "Bias detector identified_biases missing"
-
-    da = result.get("devils_advocate_output") or {}
-    assert da.get("must_not_miss"), "Devil's advocate must_not_miss missing"
-    assert all(isinstance(x, str) for x in da.get("recommended_workup", []))
-
-    ref = result.get("consultant_output") or {}
-    assert ref.get("consultation_note")
-    assert isinstance(ref.get("alternative_diagnoses"), list)

tests/test_smoke.py

@@ -1,35 +0,0 @@
-"""Quick smoke tests: imports, graph build, demo loading, utils."""
-
-import os
-import sys
-
-from PIL import Image
-
-sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))
-
-
-def test_smoke_graph_builds():
-    from agents.graph import build_graph
-
-    graph = build_graph()
-    assert graph is not None
-
-
-def test_smoke_demo_loader_returns_expected_shape():
-    from ui.callbacks import load_demo
-
-    img, diagnosis, context, modality = load_demo("Case 1: Missed Pneumothorax")
-    assert isinstance(diagnosis, str) and diagnosis
-    assert isinstance(context, str) and context
-    assert modality in {"CXR", "CT", "Other"}
-    assert img is None or hasattr(img, "size")
-
-
-def test_utils_strip_and_resize():
-    from models.utils import resize_for_medgemma, strip_thinking_tokens
-
-    assert strip_thinking_tokens("<unused94>t<unused95>Real answer") == "Real answer"
-    big = Image.new("RGB", (2000, 2000), color="gray")
-    resized = resize_for_medgemma(big)
-    assert max(resized.size) <= 896
-