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ee5d942
1
Parent(s):
c0ef450
switched to new model name
Browse files
app.py
CHANGED
@@ -5,8 +5,8 @@ from transformers import AutoTokenizer, AutoModelForCausalLM
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import gradio as gr
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# ——— CONFIG ———
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REPO_ID = "CodCodingCode/llama-3.1-8b-clinical"
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SUBFOLDER = "checkpoint-
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HF_TOKEN = os.getenv("HUGGINGFACE_HUB_TOKEN")
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if not HF_TOKEN:
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raise RuntimeError("Missing HUGGINGFACE_HUB_TOKEN in env")
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@@ -179,23 +179,80 @@ class RoleAgent:
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# === Agents ===
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summarizer = RoleAgent(
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role_instruction=
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tokenizer=tokenizer,
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model=model,
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)
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tokenizer=tokenizer,
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model=model,
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)
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tokenizer=tokenizer,
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model=model,
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)
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tokenizer=tokenizer,
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model=model,
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)
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@@ -235,12 +292,12 @@ def simulate_interaction(user_input, conversation_history=None):
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summary = sum_out["output"]
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# Diagnose based on summary
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diag_out =
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diagnosis = diag_out["output"]
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# Generate next question based on current understanding
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q_in = f"Vignette: {summary}\nCurrent Estimated Diagnosis: {diagnosis}"
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q_out =
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# Add doctor's response to history
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history.append(f"Doctor: {q_out['output']}")
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import gradio as gr
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# ——— CONFIG ———
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REPO_ID = "CodCodingCode/llama-3.1-8b-clinical-v1.1"
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SUBFOLDER = "checkpoint-2250"
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HF_TOKEN = os.getenv("HUGGINGFACE_HUB_TOKEN")
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if not HF_TOKEN:
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raise RuntimeError("Missing HUGGINGFACE_HUB_TOKEN in env")
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# === Agents ===
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# ——— Instantiate RoleAgents for each of your eight roles ———
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summarizer = RoleAgent(
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role_instruction=(
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"“You are a clinical summarizer. Given a transcript of a doctor–patient dialogue, "
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"extract a structured clinical vignette summarizing the key symptoms, relevant history, "
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"and any diagnostic clues.”"
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),
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tokenizer=tokenizer,
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model=model,
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)
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treatment_agent = RoleAgent(
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role_instruction=(
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"You are a board-certified clinician. Based on the provided diagnosis and patient vignette, "
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"propose a realistic, evidence-based treatment plan suitable for initiation by a primary care "
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"physician or psychiatrist."
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),
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tokenizer=tokenizer,
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model=model,
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)
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diagnoser_early = RoleAgent(
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role_instruction=(
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"You are a diagnostic reasoning model (Early Stage). Based on the patient vignette and "
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"early-stage observations, generate a list of plausible diagnoses with reasoning. Focus on "
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"broad differentials, considering common and uncommon conditions."
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),
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tokenizer=tokenizer,
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model=model,
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)
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diagnoser_middle = RoleAgent(
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role_instruction=(
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"You are a diagnostic reasoning model (Middle Stage). Given the current vignette, prior dialogue, "
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"and diagnostic hypothesis, refine the list of possible diagnoses with concise justifications for each. "
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"Aim to reduce diagnostic uncertainty."
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),
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tokenizer=tokenizer,
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model=model,
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)
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diagnoser_late = RoleAgent(
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role_instruction=(
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"You are a diagnostic reasoning model (Late Stage). Based on the final patient vignette summary and full conversation, "
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"provide the most likely diagnosis with structured reasoning. Confirm diagnostic certainty and include END if no more questioning is necessary."
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),
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tokenizer=tokenizer,
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model=model,
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)
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questioner_early = RoleAgent(
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role_instruction=(
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"You are a questioning agent (Early Stage). Your task is to propose highly relevant early-stage questions "
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"that can open the differential diagnosis widely. Use epidemiology, demographics, and vague presenting symptoms as guides."
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),
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tokenizer=tokenizer,
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model=model,
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)
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questioner_middle = RoleAgent(
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role_instruction=(
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"You are a questioning agent (Middle Stage). Using the current diagnosis, past questions, and patient vignette, "
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"generate a specific question to refine the current differential diagnosis. Return your reasoning and next question."
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),
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tokenizer=tokenizer,
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model=model,
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)
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questioner_late = RoleAgent(
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role_instruction=(
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"You are a questioning agent (Late Stage). Based on narrowed differentials and previous dialogue, "
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"generate a focused question that would help confirm or eliminate the final 1-2 suspected diagnoses."
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),
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tokenizer=tokenizer,
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model=model,
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)
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summary = sum_out["output"]
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# Diagnose based on summary
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diag_out = diagnoser_middle.act(summary)
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diagnosis = diag_out["output"]
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# Generate next question based on current understanding
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q_in = f"Vignette: {summary}\nCurrent Estimated Diagnosis: {diagnosis}"
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q_out = questioner_middle.act(q_in)
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# Add doctor's response to history
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history.append(f"Doctor: {q_out['output']}")
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