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README.md

@@ -1,12 +1,29 @@
----
-title: GradiotestApp
-emoji: 👀
-colorFrom: gray
-colorTo: purple
-sdk: gradio
-sdk_version: 5.49.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+
+# AI-Powered Differential Diagnosis (Educational)
+
+What this Space does
+- You enter at least 3 comma-separated symptoms.
+- The app compares your input to a dataset using TF-IDF similarity.
+- It returns a differential list with scaled scores (0..1), example matches, and structured reasoning outlines (deterministic).
+
+Files to upload to your Hugging Face Space
+- `app.py` (this file)
+- `requirements.txt`
+- `differential_diagnosis_dataset.csv` (your dataset)
+- (Optional) `README.md`
+
+Dataset assumptions
+- One column named `diagnosis` (lower/upper case both ok). 
+- Symptom columns contain the word `symptom`, `feature`, `sign`. If none found, all non-diagnosis columns are treated as symptoms.
+
+Run locally
+```bash
+pip install -r requirements.txt
+python app.py
+```
+Open http://127.0.0.1:7860
+
+Safety
+- This tool is for education and prototyping.
+- It is not a substitute for a clinician.
+- For urgent issues, contact emergency services.

app.py

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+
+import gradio as gr
+import pandas as pd
+import numpy as np
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.metrics.pairwise import cosine_similarity
+from typing import List, Tuple
+
+DATASET_PATH = "differential_diagnosis_dataset.csv"
+
+def load_dataset(path: str):
+    df = pd.read_csv(path)
+    # Identify diagnosis column (case-insensitive exact match or contains)
+    cols_lower = [c.lower() for c in df.columns]
+    if "diagnosis" in cols_lower:
+        diagnosis_col = df.columns[cols_lower.index("diagnosis")]
+    else:
+        diagnosis_col = None
+        for c in df.columns:
+            if "diagnos" in c.lower():
+                diagnosis_col = c
+                break
+        if diagnosis_col is None:
+            raise ValueError("No diagnosis column found. Please ensure a 'diagnosis' column exists.")
+    # Symptom columns heuristic
+    symptom_cols = [c for c in df.columns if ("symptom" in c.lower()) or ("feature" in c.lower()) or ("sign" in c.lower())]
+    if len(symptom_cols) == 0:
+        symptom_cols = [c for c in df.columns if c != diagnosis_col]
+    # Build a 'text' column by concatenating symptoms
+    def row_to_text(row):
+        parts = []
+        for c in symptom_cols:
+            val = row.get(c, "")
+            if pd.notna(val) and str(val).strip():
+                parts.append(str(val))
+        return ", ".join(parts)
+    df = df.copy()
+    df["_symptom_text"] = df.apply(row_to_text, axis=1)
+    return df, diagnosis_col, symptom_cols
+
+# Global fit (loaded at startup)
+try:
+    _df, _diagnosis_col, _symptom_cols = load_dataset(DATASET_PATH)
+    _corpus = _df["_symptom_text"].fillna("").astype(str).tolist()
+    _vectorizer = TfidfVectorizer(ngram_range=(1,2), min_df=1)
+    _X = _vectorizer.fit_transform(_corpus)
+except Exception as e:
+    _df, _diagnosis_col, _symptom_cols = None, None, None
+    _corpus, _vectorizer, _X = [], None, None
+    _startup_error = str(e)
+else:
+    _startup_error = ""
+
+def normalize_symptoms(text: str) -> str:
+    return ", ".join([t.strip().lower() for t in text.split(",") if t.strip()])
+
+def few_shot_prompt(symptoms: List[str], k_examples: int = 3) -> str:
+    if _X is None or _vectorizer is None:
+        return "Model not initialized."
+    query = ", ".join(symptoms)
+    qv = _vectorizer.transform([query])
+    sims = cosine_similarity(qv, _X).ravel()
+    top_idx = sims.argsort()[::-1][:k_examples]
+    examples = []
+    for i in top_idx:
+        examples.append(f"Symptoms: {{ {_df.loc[i, '_symptom_text']} }} -> Diagnosis: {{ {_df.loc[i, _diagnosis_col]} }}")
+    return "\n".join(examples)
+
+def chain_of_reasoning(symptoms: List[str]) -> List[str]:
+    steps = []
+    steps.append("1) Parse input and standardize terms.")
+    steps.append("2) Match symptom pattern with dataset using TF-IDF similarity.")
+    steps.append("3) Collect top diagnoses and compute normalized scores.")
+    steps.append("4) Check for red-flag patterns (e.g., chest pain + shortness of breath).")
+    steps.append("5) Return differential list with triage flags. For care, consult a clinician.")
+    return steps
+
+def tree_of_hypotheses(symptoms: List[str], top_n: int = 5) -> List[str]:
+    text = ", ".join(symptoms)
+    buckets = {
+        "cardio": ["chest pain","palpitations","syncope","shortness of breath"],
+        "neuro": ["headache","weakness","numbness","confusion","seizure"],
+        "gi": ["abdominal pain","nausea","vomiting","diarrhea","constipation"],
+        "pulm": ["cough","wheezing","dyspnea","hemoptysis"],
+        "id": ["fever","chills","night sweats","fatigue"]
+    }
+    matched = []
+    for system, keys in buckets.items():
+        if any(k in text for k in keys):
+            matched.append(system)
+    if not matched:
+        matched = ["general"]
+    return [f"Hypothesis branch: {m}" for m in matched]
+
+def infer_differential(symptoms_text: str, top_k: int = 7) -> Tuple[str, list, list, list]:
+    if _startup_error:
+        return f"Startup error: {_startup_error}", [], [], []
+    cleaned = normalize_symptoms(symptoms_text)
+    tokens = [t for t in cleaned.split(",") if t]
+    if len(tokens) < 3:
+        return "Enter at least 3 symptoms (comma-separated).", [], [], []
+
+    query = ", ".join(tokens)
+    qv = _vectorizer.transform([query])
+    sims = cosine_similarity(qv, _X).ravel()
+    scores = {}
+    for i, s in enumerate(sims):
+        dx = str(_df.loc[i, _diagnosis_col])
+        scores[dx] = max(scores.get(dx, 0.0), float(s))
+
+    ranked = sorted(scores.items(), key=lambda x: x[1], reverse=True)[:top_k]
+    if ranked:
+        max_s = ranked[0][1] if ranked[0][1] > 0 else 1e-9
+        ranked = [(dx, round(score/max_s, 3)) for dx, score in ranked]
+
+    diffs = [f"• {dx} — score {score}" for dx, score in ranked]
+    fewshot = few_shot_prompt(tokens, k_examples=3)
+    chain = chain_of_reasoning(tokens)
+    tree = tree_of_hypotheses(tokens)
+
+    return "", diffs, chain, [fewshot] + tree
+
+disclaimer = (
+    "Educational use only. Not medical advice.\n"
+    "Do not use this tool for emergencies.\n"
+    "If symptoms are severe or worsening, seek licensed care immediately."
+)
+
+with gr.Blocks(title="AI-Powered Differential Diagnosis (Educational)") as demo:
+    gr.Markdown("# AI-Powered Differential Diagnosis (Educational)")
+    gr.Markdown(disclaimer)
+    if _startup_error:
+        gr.Markdown(f"**Startup error:** {_startup_error}")
+    with gr.Row():
+        inp = gr.Textbox(label="Enter symptoms (comma-separated). Minimum 3.", placeholder="fever, cough, shortness of breath")
+    with gr.Row():
+        btn = gr.Button("Analyze")
+        clr = gr.ClearButton([inp])
+    with gr.Row():
+        dx = gr.Markdown(label="Differential Diagnoses")
+    with gr.Row():
+        chain_box = gr.Markdown(label="Structured reasoning steps")
+    with gr.Row():
+        prompt_box = gr.Markdown(label="Few-shot examples + hypothesis branches")
+
+    def on_analyze(text):
+        err, diffs, chain, prompt = infer_differential(text)
+        if err:
+            return err, "", ""
+        dx_md = "\n".join(diffs) if diffs else "No matches."
+        chain_md = "\n".join(chain) if chain else ""
+        prompt_md = "\n".join(prompt) if prompt else ""
+        return dx_md, chain_md, prompt_md
+
+    btn.click(on_analyze, inputs=[inp], outputs=[dx, chain_box, prompt_box])
+
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0", server_port=7860)

differential_diagnosis_dataset.csv

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+Patient_ID,Age,Gender,Symptom,Diagnosis_Category,Possible_Diagnosis
+e5196724-d926-4fd7-bc20-e76a16b3d8a6,10,Female,wheezing,Respiratory Issues,asthma
+1701efa5-1d30-4492-89c3-5f5f3174bf2e,24,Female,chest pain,Respiratory Issues,asthma
+d307040a-3519-4c22-b676-ad10f3da80b1,66,Female,shortness of breath,Respiratory Issues,pneumonia
+4ca668f9-077f-446e-aac5-8fb91d79b0b6,64,Female,rapid breathing,Respiratory Issues,pneumonia
+8365de28-a73a-425d-8dce-6d6ada05d9c1,60,Female,cough,Respiratory Issues,pneumonia
+95b09e49-3009-452b-bbb5-65bdf1d56c63,40,Male,chest pain,Respiratory Issues,asthma
+ecace22a-1ee3-4db1-817e-a56166396c20,31,Female,wheezing,Respiratory Issues,asthma
+d3e45a41-3a99-4ec1-a59c-f4964f73d32c,77,Male,wheezing,Respiratory Issues,pneumonia
+6d928508-bd79-4c8d-926e-32b33f07d8c9,79,Female,fatigue,Respiratory Issues,pneumonia
+dfcddeef-d7a9-4714-ad4d-c5ff84655dd1,37,Male,shortness of breath,Respiratory Issues,asthma
+fd8b1144-2b14-4bf1-86b2-0fa44aeaaa5f,30,Female,fatigue,Respiratory Issues,asthma
+03fde28a-cf69-418f-a6ba-7defea51ffe2,66,Male,wheezing,Respiratory Issues,pneumonia
+58b0fcbb-3eab-4a44-ab4e-69a046a63ccd,79,Female,chest pain,Respiratory Issues,pneumonia
+f572e2d8-e14f-4979-906b-de65c8444a3c,79,Female,fatigue,Respiratory Issues,pneumonia
+be61a7ac-5222-454b-9655-8140176b06f7,28,Female,chest pain,Respiratory Issues,asthma
+8ba5abc4-9770-4330-8651-2b4ad3ef8080,63,Male,chest pain,Respiratory Issues,pneumonia
+7146aa31-4996-4967-a85c-b6463f810365,82,Female,shortness of breath,Respiratory Issues,pneumonia
+f4fdafb5-e806-4fcd-a4a7-ec9db88bbb80,64,Male,shortness of breath,Respiratory Issues,pneumonia
+6c005266-5794-48d6-a3c9-52cf9ca230fb,10,Female,fatigue,Respiratory Issues,asthma
+b5b65186-dd5b-4bac-8cee-5f522fa2f6e4,70,Male,bluish lips or face,Respiratory Issues,pneumonia
+c4f4ede7-5902-4464-aeb5-1933e89d0fe2,67,Female,fatigue,Respiratory Issues,pneumonia
+d40c16da-7367-4650-ae38-7565412cfe10,63,Female,fatigue,Respiratory Issues,pneumonia
+dd04a96e-19e9-41d4-b069-3a5d91c97417,68,Male,wheezing,Respiratory Issues,pneumonia
+1764cc29-945c-4ca5-a8ff-e7f9d47ba4ca,30,Female,shortness of breath,Respiratory Issues,asthma
+5b57f6bc-0b45-4abe-9818-f6e323b5be1d,66,Female,fatigue,Respiratory Issues,pneumonia
+aa69f7e1-f091-4762-b943-8fbfd66830a8,16,Male,vomiting,Infectious Diseases,gastroenteritis
+5419bbc5-9955-4795-beca-2d08e5534ef6,19,Female,diarrhea,Infectious Diseases,influenza
+8006470f-3c05-4178-8365-427080011760,67,Female,fever,Infectious Diseases,gastroenteritis
+3777258d-94c7-4fd9-b6ae-f9fda3279c5a,51,Female,vomiting,Infectious Diseases,gastroenteritis
+c2312798-fc56-4eba-80db-42a4737e181c,76,Female,nausea,Infectious Diseases,gastroenteritis
+58f6889d-86e7-4cdd-871e-d7d5f7d19799,75,Male,fever,Infectious Diseases,gastroenteritis
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+4f88f312-5748-4f38-9f03-4852a8ae8756,65,Female,fever,Infectious Diseases,influenza
+1033ae85-0031-4ffa-b5c6-f481ad43ba35,55,Male,sore throat,Infectious Diseases,gastroenteritis
+08304a9f-04ce-44ca-90ce-7a245b9ee191,34,Female,rash,Infectious Diseases,influenza
+544607c3-80ef-42f1-813d-93dfc8151e3b,56,Male,sore throat,Infectious Diseases,influenza
+de247b6e-e866-415e-b90e-acde0e6f3458,11,Male,sore throat,Infectious Diseases,influenza
+bb2fb19a-e7fe-4d60-a0d6-087f08431eb3,87,Female,headache,Infectious Diseases,influenza
+4042f763-f4c1-4fcf-9893-c9294aa61cac,85,Female,muscle aches,Infectious Diseases,influenza
+192c7cd0-1eca-4359-bc2e-92302c1956a5,67,Male,chills,Infectious Diseases,gastroenteritis
+58fa335f-5fe5-4035-bb99-9604ea9bba19,33,Female,sore throat,Infectious Diseases,gastroenteritis
+910d50d7-4006-4f1f-9f48-5a85e27435f8,31,Male,sore throat,Infectious Diseases,influenza
+c8220203-44a8-422d-9caf-8fdfb74455af,66,Female,rash,Infectious Diseases,influenza
+e7d07198-7366-40f0-90d1-676924f50e77,57,Male,rash,Infectious Diseases,influenza
+ada45045-55f9-45c4-b7e8-4c32cf90884b,85,Female,palpitations,Cardiovascular Problems,myocardial infarction
+952c6399-a5e7-40cc-ad16-e9bdc46f2a33,88,Female,shortness of breath,Cardiovascular Problems,angina
+f42a478a-0f04-4bf5-9b3e-d86eb164e734,61,Female,dizziness,Cardiovascular Problems,angina
+6f4446c1-45f1-4a19-90a5-c661e4196d43,83,Female,chest pain,Cardiovascular Problems,angina
+9e4c5458-76ba-458d-a8ae-78c0bf38b62a,63,Male,chest pain,Cardiovascular Problems,myocardial infarction
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+784e08aa-c8e9-4fc6-bcb9-42dd0ceadcba,41,Male,swelling in legs or ankles,Cardiovascular Problems,angina
+62ed4a15-9733-47e6-a607-e7c233dc6d95,49,Male,palpitations,Cardiovascular Problems,myocardial infarction
+6086656f-d4d1-463a-ada1-a32e9030dac5,52,Male,fatigue,Cardiovascular Problems,angina
+0c142286-e290-41bc-81ed-4419d9c3d529,83,Female,sweating,Cardiovascular Problems,myocardial infarction
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+c09871c5-f11c-4897-b32d-371d07914b5b,75,Male,swelling in legs or ankles,Cardiovascular Problems,myocardial infarction
+494be90d-318f-46f9-9e18-8423d8a5bd05,50,Male,nausea,Cardiovascular Problems,myocardial infarction
+300c29a2-90f7-485d-a00e-786ee55248bb,81,Female,nausea,Cardiovascular Problems,angina
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+20d4fe89-cc41-47e2-bd46-76492be4a93c,71,Male,warmth around joints,Musculoskeletal Disorders,gout
+d4c969e7-a2f8-4fc3-baa4-a941a9ccdaa4,85,Male,swelling,Musculoskeletal Disorders,gout
+55fe91cb-56ce-4c56-a149-d4ff0cb20246,74,Male,joint pain,Musculoskeletal Disorders,gout
+3ec18364-956d-4dc5-9b7f-09524e057812,69,Female,limited range of motion,Musculoskeletal Disorders,gout
+8d1523b1-89e7-45ba-82ff-d815a6d125ae,86,Female,muscle weakness,Musculoskeletal Disorders,arthritis
+929d3c33-3b20-4a7c-83d3-b780d5200ff4,81,Female,limited range of motion,Musculoskeletal Disorders,gout
+be19c30d-eee0-4dfe-8a06-01f41122f8a8,47,Male,swelling,Musculoskeletal Disorders,gout

requirements.txt

@@ -0,0 +1,5 @@
+
+gradio>=4.44.0
+pandas>=2.0.0
+scikit-learn>=1.3.0
+numpy>=1.24.0