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KevinIsInCoding Claude Sonnet 4.6 commited on May 19

Commit

95c1c84

2 Parent(s): e7279ce 49cbd8a

Merge feat/als-intake-and-api-fixes: ALS intake improvements and API fixes

- Add FVC % predicted, ALS subtype, and gene mutation fields to intake
- Add PI name and contact info (phone/email) to trial results
- Fix ClinicalTrials.gov API filter (aggFilters can't combine studyType + phase)
- Require ZIP code to always be confirmed by patient (never inferred)
- Restrict results to tool output only — no hallucinated hospitals
- Resolve all lingering merge conflict markers in pyproject.toml and uv.lock
- Consolidate README with HF Space YAML frontmatter and updated setup guide

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

Files changed (4) hide show

README.md +0 -106
clinical_trials_guru.py +67 -16
pyproject.toml +0 -3
uv.lock +102 -168

README.md CHANGED Viewed

@@ -1,104 +1,3 @@
-<<<<<<< HEAD
-# Beacon — Rare Disease Clinical Trial Finder
-Beacon is a conversational AI assistant that helps patients with rare diseases find relevant recruiting clinical trials near them. It conducts a warm intake interview, geocodes the patient's location, queries [ClinicalTrials.gov](https://clinicaltrials.gov) in real time, and produces a ranked report of the closest matching trials.
-## How it works
-1. **Intake agent** (Claude Sonnet) — interviews the patient conversationally to collect disease, age, symptom onset, location, and optional benchmark scores.
-2. **Research agent** (Claude Opus) — searches ClinicalTrials.gov via the official v2 API, retrying with synonyms or wider radii if results are sparse, then outputs a ranked trial report with eligibility notes and next steps.
-3. **LangGraph** orchestrates the two-node pipeline (intake → research).
-## Project setup
-### Prerequisites
-- Python 3.11+
-- [`uv`](https://docs.astral.sh/uv/) (recommended) or `pip`
-### 1. Clone the repo
-```bash
-git clone <repo-url>
-cd beacon
-```
-### 2. Install dependencies
-```bash
-uv sync
-```
-Or with pip:
-```bash
-pip install -e .
-```
-### 3. Configure API keys
-Copy the example env file and fill in your keys:
-```bash
-cp .env.example .env
-```
-Open `.env` and replace the placeholder values:
-```env
-# ⚠️  Replace with your actual API keys — never commit real keys to version control
-OPENAI_API_KEY=your_openai_api_key_here
-ANTHROPIC_API_KEY=your_anthropic_api_key_here
-```
-- Get your Anthropic key at <https://console.anthropic.com>
-- Get your OpenAI key at <https://platform.openai.com/api-keys>
-### 4. Run
-```bash
-uv run python main.py
-```
-Or if using a plain virtualenv:
-```bash
-python main.py
-```
-## Configuration
-| Environment variable | Default     | Description                                      |
-|----------------------|-------------|--------------------------------------------------|
-| `ANTHROPIC_API_KEY`  | *(required)*| Anthropic API key                                |
-| `OPENAI_API_KEY`     | *(optional)*| OpenAI API key (only needed for OpenAI provider) |
-| `LLM_PROVIDER`       | `anthropic` | LLM backend: `anthropic` or `openai`             |
-To switch to the OpenAI backend, set `LLM_PROVIDER=openai` in `.env`.
-## Project structure
-```
-beacon/
-├── main.py                  # Entry point
-├── clinical_trials_guru.py  # Intake + research agents, LangGraph pipeline
-├── llm.py                   # LLM provider abstraction (Anthropic / OpenAI)
-├── pyproject.toml
-├── .env                     # Local secrets — not committed
-└── .gitignore
-```
-## Dependencies
-| Package          | Purpose                              |
-|------------------|--------------------------------------|
-| `anthropic`      | Claude API client                    |
-| `openai`         | OpenAI API client                    |
-| `langgraph`      | Agent pipeline orchestration         |
-| `rich`           | Terminal UI (panels, markdown, etc.) |
-| `python-dotenv`  | `.env` file loading                  |
-| `httpx`          | HTTP client for ClinicalTrials.gov   |
-=======
 ---
 title: Beacon Trial Finder
 emoji: 📚
@@ -113,10 +12,6 @@ license: mit
 short_description: help patients with rare disease to find clinical trials
 ---
-<<<<<<< HEAD
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
->>>>>>> 2a6ee87 (initial commit)
-=======
 # Beacon — Rare Disease Clinical Trial Finder
 Beacon is a conversational AI assistant that helps patients with rare diseases find relevant recruiting clinical trials near them. It conducts a warm intake interview, geocodes the patient's location, queries [ClinicalTrials.gov](https://clinicaltrials.gov) in real time, and produces a ranked report of the closest matching trials.
@@ -215,4 +110,3 @@ beacon/
 ├── pyproject.toml
 └── .env                     # Local secrets — not committed
 ```
->>>>>>> f8d77e4 (Initial release: Beacon rare disease clinical trial finder)

 ---
 title: Beacon Trial Finder
 emoji: 📚
 short_description: help patients with rare disease to find clinical trials
 ---
 # Beacon — Rare Disease Clinical Trial Finder
 Beacon is a conversational AI assistant that helps patients with rare diseases find relevant recruiting clinical trials near them. It conducts a warm intake interview, geocodes the patient's location, queries [ClinicalTrials.gov](https://clinicaltrials.gov) in real time, and produces a ranked report of the closest matching trials.
 ├── pyproject.toml
 └── .env                     # Local secrets — not committed
 ```

clinical_trials_guru.py CHANGED Viewed

@@ -123,7 +123,19 @@ REQUIRED:
 OPTIONAL (ask based on disease):
   • Disease-specific benchmark scores:
-      ALS → ALSFRS-R (0-48); MS → EDSS (0-10); Parkinson's → MDS-UPDRS III;
       Huntington's → TFC (0-13) + CAG repeats; SMA → HFMS + SMA type;
       Duchenne/Pompe → 6-Minute Walk Test; Friedreich's → SARA score
   • Preferred search radius in miles (default 100)
@@ -147,7 +159,7 @@ OPTIONAL (ask based on disease):
         but may be an option when no approved treatments remain.
       - Or both; or all phases (default if no preference)
-Ask naturally. Infer what you can. Once you have the required fields, call submit_profile.\
 """
 RESEARCH_SYSTEM = """\
@@ -166,13 +178,16 @@ Workflow:
    List the top 5 results per section ranked by site proximity.
    For EACH entry use exactly this format (repeat the block per entry):
-   📍 **[Closest hospital name]** — [City, State] ([X] mi)
    **Trial:** [Full title] ([Phase] — or "Expanded Access" for EAP)
    **Sponsor:** [Lead sponsor]
    **Summary:** [2–3 sentence plain-language description of what the trial/program is testing
                and why it may matter for this patient]
-   **Eligibility notes:** [Key inclusion/exclusion criteria relevant to this patient,
-                         including any red flags]
    **Link:** https://clinicaltrials.gov/study/[NCT_ID]
    ---
@@ -180,6 +195,11 @@ Workflow:
 4. After the results add a short "Next steps" section (bullet points).
    For EAP results, note that patients typically need a physician to submit the EAP request.
 Be accurate. Do not fabricate details. If data is missing, say so.\
 """
@@ -265,13 +285,19 @@ def search_trials_api(
     params: dict[str, str | int] = {
         "query.cond": condition,
         "filter.overallStatus": "AVAILABLE" if is_eap else "RECRUITING",
-        "filter.studyType": study_type,
         "filter.geo": f"distance({lat},{lon},{radius_miles}mi)",
         "pageSize": max_results,
         "format": "json",
     }
-    if phases and not is_eap:
         params["aggFilters"] = "phase:" + " ".join(phases)
     for attempt in range(3):
         try:
             resp = httpx.get(CTGOV_BASE, params=params, timeout=30)
@@ -296,18 +322,40 @@ def _flatten_and_rank(studies: list[dict], patient_lat: float, patient_lon: floa
         sponsor_mod = proto.get("sponsorCollaboratorsModule", {})
         design_mod = proto.get("designModule", {})
-        sites_with_dist: list[tuple[float, str]] = []
         for loc in contacts_mod.get("locations", []):
             geo = loc.get("geoPoint", {})
             if geo.get("lat") and geo.get("lon"):
                 d = haversine_miles(patient_lat, patient_lon, geo["lat"], geo["lon"])
-                label = (
-                    f"{loc.get('facility', '').strip()} — "
-                    f"{loc.get('city', '')}, "
-                    f"{loc.get('state', loc.get('country', ''))} "
-                    f"({d:.0f} mi)"
-                )
-                sites_with_dist.append((d, label))
         sites_with_dist.sort(key=lambda x: x[0])
         closest_dist = sites_with_dist[0][0] if sites_with_dist else None
@@ -316,12 +364,15 @@ def _flatten_and_rank(studies: list[dict], patient_lat: float, patient_lon: floa
             "title": id_mod.get("briefTitle", ""),
             "phase": ", ".join(design_mod.get("phases", [])) or "N/A",
             "sponsor": sponsor_mod.get("leadSponsor", {}).get("name", ""),
             "summary": desc_mod.get("briefSummary", "")[:500],
             "eligibility": elig_mod.get("eligibilityCriteria", "")[:1000],
             "min_age": elig_mod.get("minimumAge", ""),
             "max_age": elig_mod.get("maximumAge", ""),
             "closest_site_miles": round(closest_dist, 1) if closest_dist is not None else None,
-            "nearest_sites": [label for _, label in sites_with_dist[:5]],
         })
     result.sort(key=lambda x: x["closest_site_miles"] if x["closest_site_miles"] is not None else float("inf"))

 OPTIONAL (ask based on disease):
   • Disease-specific benchmark scores:
+      ALS → ALSFRS-R (0-48) + FVC % predicted (0-100%) + ALS subtype;
+              FVC (Forced Vital Capacity) measures how much air a person can forcibly exhale —
+              it reflects respiratory muscle strength. In ALS it is expressed as a percentage
+              of the value expected for someone of the same age/height/sex (e.g. "72%").
+              Many trials require FVC ≥ 50% or ≥ 60% for enrollment. If the patient has had
+              recent pulmonary function testing, ask for their FVC % predicted.
+              ALS subtype: ask whether the patient has sporadic ALS (no family history, ~90–95%
+              of cases) or familial/genetic ALS (inherited; ~5–10% of cases). If familial, ask
+              which gene mutation is involved if they know it (common ones: SOD1, C9orf72, FUS,
+              TDP-43). This affects trial eligibility — many gene-targeted trials require a
+              confirmed mutation. The patient may skip if unknown. Store as e.g.
+              {"ALS subtype": "sporadic"} or {"ALS subtype": "familial", "ALS gene": "SOD1"}.
+          MS → EDSS (0-10); Parkinson's → MDS-UPDRS III;
       Huntington's → TFC (0-13) + CAG repeats; SMA → HFMS + SMA type;
       Duchenne/Pompe → 6-Minute Walk Test; Friedreich's → SARA score
   • Preferred search radius in miles (default 100)
         but may be an option when no approved treatments remain.
       - Or both; or all phases (default if no preference)
+Ask naturally. You may infer disease synonyms and convert dates to months, but never infer or skip the ZIP/postal code — always ask the patient for it directly. Once you have every required field confirmed by the patient, call submit_profile.\
 """
 RESEARCH_SYSTEM = """\
    List the top 5 results per section ranked by site proximity.
    For EACH entry use exactly this format (repeat the block per entry):
+   📍 **[Closest hospital/facility name]** — [City, State] ([X] mi)
    **Trial:** [Full title] ([Phase] — or "Expanded Access" for EAP)
    **Sponsor:** [Lead sponsor]
+   **Principal Investigator:** [Name — or "Not listed" if absent]
+   **Contact:** [Phone number] | [Email address] (use "Not listed" for any missing field)
    **Summary:** [2–3 sentence plain-language description of what the trial/program is testing
                and why it may matter for this patient]
+   **Qualification criteria:** [Key inclusion AND exclusion criteria relevant to this patient,
+                               including age range, functional score thresholds, FVC cutoffs,
+                               and any red flags. Be specific — use exact numbers from the data.]
    **Link:** https://clinicaltrials.gov/study/[NCT_ID]
    ---
 4. After the results add a short "Next steps" section (bullet points).
    For EAP results, note that patients typically need a physician to submit the EAP request.
+IMPORTANT: Only report trials returned by the search_clinical_trials tool. Do NOT suggest,
+list, or recommend any hospitals, centers, or trials that were not in the tool results —
+even well-known institutions. If no results are found, say so clearly and suggest the patient
+ask their neurologist or contact the ALS Association for a referral.
 Be accurate. Do not fabricate details. If data is missing, say so.\
 """
     params: dict[str, str | int] = {
         "query.cond": condition,
         "filter.overallStatus": "AVAILABLE" if is_eap else "RECRUITING",
         "filter.geo": f"distance({lat},{lon},{radius_miles}mi)",
         "pageSize": max_results,
         "format": "json",
     }
+    # aggFilters accepts only one value; studyType and phase can't be combined.
+    # RECRUITING status already excludes EAPs, so studyType:int is only needed
+    # when no phase filter is applied.
+    if is_eap:
+        params["aggFilters"] = "studyType:exp"
+    elif phases:
         params["aggFilters"] = "phase:" + " ".join(phases)
+    else:
+        params["aggFilters"] = "studyType:int"
     for attempt in range(3):
         try:
             resp = httpx.get(CTGOV_BASE, params=params, timeout=30)
         sponsor_mod = proto.get("sponsorCollaboratorsModule", {})
         design_mod = proto.get("designModule", {})
+        # Central (overall) contacts
+        central_contacts = contacts_mod.get("centralContacts", [])
+        central_phone = next((c.get("phone", "") for c in central_contacts if c.get("phone")), "")
+        central_email = next((c.get("email", "") for c in central_contacts if c.get("email")), "")
+        # Principal investigator from overallOfficials
+        officials = contacts_mod.get("overallOfficials", [])
+        pi = next(
+            (o.get("name", "") for o in officials if o.get("role") == "PRINCIPAL_INVESTIGATOR"),
+            officials[0].get("name", "") if officials else "",
+        )
+        sites_with_dist: list[tuple[float, dict]] = []
         for loc in contacts_mod.get("locations", []):
             geo = loc.get("geoPoint", {})
             if geo.get("lat") and geo.get("lon"):
                 d = haversine_miles(patient_lat, patient_lon, geo["lat"], geo["lon"])
+                loc_contacts = loc.get("contacts", [])
+                loc_phone = next((c.get("phone", "") for c in loc_contacts if c.get("phone")), "")
+                loc_email = next((c.get("email", "") for c in loc_contacts if c.get("email")), "")
+                sites_with_dist.append((d, {
+                    "label": (
+                        f"{loc.get('facility', '').strip()} — "
+                        f"{loc.get('city', '')}, "
+                        f"{loc.get('state', loc.get('country', ''))} "
+                        f"({d:.0f} mi)"
+                    ),
+                    "facility": loc.get("facility", "").strip(),
+                    "city": loc.get("city", ""),
+                    "state": loc.get("state", loc.get("country", "")),
+                    "distance_miles": round(d, 1),
+                    "phone": loc_phone or central_phone,
+                    "email": loc_email or central_email,
+                }))
         sites_with_dist.sort(key=lambda x: x[0])
         closest_dist = sites_with_dist[0][0] if sites_with_dist else None
             "title": id_mod.get("briefTitle", ""),
             "phase": ", ".join(design_mod.get("phases", [])) or "N/A",
             "sponsor": sponsor_mod.get("leadSponsor", {}).get("name", ""),
+            "principal_investigator": pi,
+            "contact_phone": central_phone,
+            "contact_email": central_email,
             "summary": desc_mod.get("briefSummary", "")[:500],
             "eligibility": elig_mod.get("eligibilityCriteria", "")[:1000],
             "min_age": elig_mod.get("minimumAge", ""),
             "max_age": elig_mod.get("maximumAge", ""),
             "closest_site_miles": round(closest_dist, 1) if closest_dist is not None else None,
+            "nearest_sites": [info for _, info in sites_with_dist[:5]],
         })
     result.sort(key=lambda x: x["closest_site_miles"] if x["closest_site_miles"] is not None else float("inf"))

pyproject.toml CHANGED Viewed

@@ -10,8 +10,5 @@ dependencies = [
     "openai>=1.0.0",
     "python-dotenv>=1.2.2",
     "rich>=13.0.0",
-<<<<<<< HEAD
     "gradio>=4.0.0",
-=======
->>>>>>> f8d77e4 (Initial release: Beacon rare disease clinical trial finder)
 ]

     "openai>=1.0.0",
     "python-dotenv>=1.2.2",
     "rich>=13.0.0",
     "gradio>=4.0.0",
 ]

uv.lock CHANGED Viewed

@@ -1,7 +1,6 @@
 version = 1
 revision = 3
 requires-python = ">=3.11"
-<<<<<<< HEAD
 resolution-markers = [
     "python_full_version >= '3.14' and sys_platform == 'win32'",
     "python_full_version >= '3.14' and sys_platform == 'emscripten'",
@@ -22,8 +21,6 @@ sdist = { url = "https://files.pythonhosted.org/packages/57/ba/046ceea2734456098
 wheels = [
     { url = "https://files.pythonhosted.org/packages/1e/d3/26bf1008eb3d2daa8ef4cacc7f3bfdc11818d111f7e2d0201bc6e3b49d45/annotated_doc-0.0.4-py3-none-any.whl", hash = "sha256:571ac1dc6991c450b25a9c2d84a3705e2ae7a53467b5d111c24fa8baabbed320", size = 5303, upload-time = "2025-11-10T22:07:40.673Z" },
 ]
-=======
->>>>>>> f8d77e4 (Initial release: Beacon rare disease clinical trial finder)
 [[package]]
 name = "annotated-types"
@@ -36,7 +33,7 @@ wheels = [
 [[package]]
 name = "anthropic"
-version = "0.101.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "anyio" },
@@ -48,9 +45,9 @@ dependencies = [
     { name = "sniffio" },
     { name = "typing-extensions" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/b5/cb/9d0123243e749ac3a579972b2c398971bce1dc57bcc4efb08066df610360/anthropic-0.101.0.tar.gz", hash = "sha256:1116a6a87c55757e0fbe3e1ba40804fbd04de7963601a6dd6b539a889f18de3e", size = 758603, upload-time = "2026-05-11T15:46:33.944Z" }
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 ]
 [[package]]
@@ -67,7 +64,6 @@ wheels = [
 ]
 [[package]]
-<<<<<<< HEAD
 name = "audioop-lts"
 version = "0.2.2"
 source = { registry = "https://pypi.org/simple" }
@@ -124,17 +120,12 @@ wheels = [
 ]
 [[package]]
-=======
->>>>>>> f8d77e4 (Initial release: Beacon rare disease clinical trial finder)
 name = "beacon"
 version = "0.1.0"
 source = { virtual = "." }
 dependencies = [
     { name = "anthropic" },
-<<<<<<< HEAD
     { name = "gradio" },
-=======
->>>>>>> f8d77e4 (Initial release: Beacon rare disease clinical trial finder)
     { name = "langgraph" },
     { name = "openai" },
     { name = "python-dotenv" },
@@ -144,10 +135,7 @@ dependencies = [
 [package.metadata]
 requires-dist = [
     { name = "anthropic", specifier = ">=0.50.0" },
-<<<<<<< HEAD
     { name = "gradio", specifier = ">=4.0.0" },
-=======
->>>>>>> f8d77e4 (Initial release: Beacon rare disease clinical trial finder)
     { name = "langgraph", specifier = ">=1.2.0" },
     { name = "openai", specifier = ">=1.0.0" },
     { name = "python-dotenv", specifier = ">=1.2.2" },
@@ -155,7 +143,6 @@ requires-dist = [
 ]
 [[package]]
-<<<<<<< HEAD
 name = "brotli"
 version = "1.2.0"
 source = { registry = "https://pypi.org/simple" }
@@ -204,8 +191,6 @@ wheels = [
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 [[package]]
-=======
->>>>>>> f8d77e4 (Initial release: Beacon rare disease clinical trial finder)
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 version = "2026.4.22"
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@@ -304,21 +289,18 @@ wheels = [
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-<<<<<<< HEAD
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 [[package]]
-=======
->>>>>>> f8d77e4 (Initial release: Beacon rare disease clinical trial finder)
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@@ -346,7 +328,6 @@ wheels = [
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 [[package]]
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 version = "0.136.1"
 source = { registry = "https://pypi.org/simple" }
@@ -445,8 +426,6 @@ wheels = [
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-=======
->>>>>>> f8d77e4 (Initial release: Beacon rare disease clinical trial finder)
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@@ -456,7 +435,6 @@ wheels = [
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 [[package]]
-<<<<<<< HEAD
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 version = "0.4.1"
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@@ -502,8 +480,6 @@ wheels = [
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 [[package]]
-=======
->>>>>>> f8d77e4 (Initial release: Beacon rare disease clinical trial finder)
 name = "httpcore"
 version = "1.0.9"
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@@ -532,9 +508,8 @@ wheels = [
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 [[package]]
-<<<<<<< HEAD
 name = "huggingface-hub"
-version = "1.14.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
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@@ -547,24 +522,21 @@ dependencies = [
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 [[package]]
-=======
->>>>>>> f8d77e4 (Initial release: Beacon rare disease clinical trial finder)
 name = "idna"
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