Initial commit: runtime LFS snapshot download approach

.gitignore

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+model
+venv
+checkpoint
+
+all_diagnosing_doctor_outputs.json
+all_patient_followups.json
+all_questioning_doctor_outputs.json
+all_summarizer_outputs.json
+all_treatment_outputs.json

app.py

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+import os
+import torch
+from huggingface_hub import snapshot_download
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import gradio as gr
+
+# ——— CONFIG ———  
+REPO_ID   = "CodCodingCode/llama-3.1-8b-clinical"  
+SUBFOLDER = "checkpoint-45000"  
+HF_TOKEN  = os.environ["HUGGINGFACE_HUB_TOKEN"]  # set in Settings→Secrets  
+
+# ——— SNAPSHOT & LOAD ———  
+# This will grab all .json and .safetensors under checkpoint-45000:
+local_dir = snapshot_download(
+    repo_id=REPO_ID,
+    subfolder=SUBFOLDER,
+    token=HF_TOKEN,
+    allow_patterns=["*.json", "*.safetensors"],
+)
+
+# Now point at that folder:
+MODEL_DIR = local_dir  # e.g. ~/.cache/huggingface/…/checkpoint-45000
+
+# Load tokenizer & model from the real files you just pulled:
+tokenizer = AutoTokenizer.from_pretrained(
+    MODEL_DIR,
+    use_fast=False,
+    trust_remote_code=True,
+)
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_DIR,
+    device_map="auto",
+    torch_dtype=torch.float16,
+    trust_remote_code=True,
+)
+model.eval()
+
+
+
+# === Role Agent with instruction/input/output format ===
+class RoleAgent:
+    def __init__(self, role_instruction):
+        self.role_instruction = role_instruction
+
+    def act(self, input_text):
+        prompt = (
+            f"Instruction: {self.role_instruction}\n"
+            f"Input: {input_text}\n"
+            f"Output:"
+        )
+        inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+        outputs = model.generate(
+            **inputs,
+            max_new_tokens=256,
+            do_sample=True,
+            temperature=0.7,
+            pad_token_id=tokenizer.eos_token_id,
+        )
+        response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+        # extract THINKING / ANSWER if present
+        thinking, answer = "", response
+        if "THINKING:" in response and "ANSWER:" in response and "END" in response:
+            block = response.split("THINKING:")[1].split("END")[0]
+            thinking = block.split("ANSWER:")[0].strip()
+            answer = block.split("ANSWER:")[1].strip()
+
+        return {
+            "instruction": f"You are {self.role_instruction}.",
+            "input": input_text,
+            "thinking": thinking,
+            "output": answer,
+        }
+
+
+# === Agents ===
+summarizer = RoleAgent(
+    "You are a clinical summarizer trained to extract structured vignettes from doctor–patient dialogues."
+)
+diagnoser = RoleAgent(
+    "You are a board-certified diagnostician that diagnoses patients."
+)
+questioner = RoleAgent("You are a physician asking questions to diagnose a patient.")
+
+treatment_agent = RoleAgent(
+    "You are a board-certified clinician. Based on the diagnosis and patient vignette provided below, suggest a concise treatment plan that could realistically be initiated by a primary care physician or psychiatrist."
+)
+
+
+# === Inference State ===
+conversation_history = []
+summary = ""
+diagnosis = ""
+
+
+# === Gradio Inference ===
+def simulate_interaction(user_input, iterations=1):
+    history = [f"Doctor: What brings you in today?", f"Patient: {user_input}"]
+    summary, diagnosis = "", ""
+
+    for i in range(iterations):
+        # Summarize
+        sum_in = "\n".join(history) + f"\nPrevious Vignette: {summary}"
+        sum_out = summarizer.act(sum_in)
+        summary = sum_out["output"]
+
+        # Diagnose
+        diag_out = diagnoser.act(summary)
+        diagnosis = diag_out["output"]
+
+        # Question
+        q_in = f"Vignette: {summary}\nCurrent Estimated Diagnosis: {diag_out['thinking']} {diagnosis}"
+        q_out = questioner.act(q_in)
+        history.append(f"Doctor: {q_out['output']}")
+        history.append("Patient: (awaiting response)")
+
+        # Treatment
+        treatment_out = treatment_agent.act(
+            f"Diagnosis: {diagnosis}\nVignette: {summary}"
+        )
+
+        return {
+            "summary": sum_out,
+            "diagnosis": diag_out,
+            "question": q_out,
+            "treatment": treatment_out,
+            "conversation": "\n".join(history),
+        }
+
+
+# === Gradio UI ===
+def ui_fn(user_input):
+    res = simulate_interaction(user_input)
+    return f"""📋 Vignette Summary:
+💭 THINKING: {res['summary']['thinking']}
+ANSWER: {res['summary']['output']}
+
+🩺 Diagnosis:
+💭 THINKING: {res['diagnosis']['thinking']}
+ANSWER: {res['diagnosis']['output']}
+T
+❓ Follow-up Question:
+💭 THINKING: {res['question']['thinking']}
+ANSWER: {res['question']['output']}
+
+💊 Treatment Plan:
+{res['treatment']['output']}
+
+💬 Conversation:
+{res['conversation']}
+"""
+
+
+demo = gr.Interface(
+    fn=ui_fn,
+    inputs=gr.Textbox(label="Patient Response"),
+    outputs=gr.Textbox(label="Doctor Simulation Output"),
+    title="🧠 AI Doctor Multi-Agent Reasoning",
+)
+
+if __name__ == "__main__":
+    demo.launch(share=True)

download.py

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+from huggingface_hub import hf_hub_download
+import os
+
+# ——— CONFIG ———
+REPO_ID = "CodCodingCode/llama-3.1-8b-clinical"
+SUBDIR = "checkpoint-45000"
+HF_TOKEN = os.getenv("HUGGINGFACE_HUB_TOKEN")  # make sure you set this in Secrets
+
+# Ensure output directory exists
+os.makedirs(SUBDIR, exist_ok=True)
+
+# List of shards to download
+shards = [
+    "model-00001-of-00004.safetensors",
+    "model-00002-of-00004.safetensors",
+    "model-00003-of-00004.safetensors",
+    "model-00004-of-00004.safetensors",
+    "model.safetensors.index.json",  # the index file
+]
+
+for fname in shards:
+    local_path = hf_hub_download(
+        repo_id=REPO_ID,
+        filename=f"{SUBDIR}/{fname}",
+        token=HF_TOKEN,
+        local_dir=".",  # download into the Space root
+        local_dir_use_symlinks=False,  # ensure actual file copy
+    )
+    print(f"Downloaded {fname} to {local_path}")

requirements.txt

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+huggingface_hub==0.25.2
+transformers>=4.38.0
+torch>=2.1.0
+peft>=0.9.0
+accelerate>=0.24.0
+bitsandbytes
+safetensors
+gradio