Create app.py

app.py

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+import os
+import gradio as gr
+import re
+from collections import Counter
+from llama_cpp import Llama
+from huggingface_hub import hf_hub_download
+
+# Configuration: set environment variables for model repository and file
+HF_REPO_ID = os.getenv("HF_REPO_ID", "QuantFactory/Meta-Llama-3-8B-Instruct-GGUF")
+HF_MODEL_BASENAME = os.getenv("HF_MODEL_BASENAME", "Meta-Llama-3-8B-Instruct.Q8_0.gguf")
+
+# Download or locate the quantized LLaMA model
+model_path = hf_hub_download(repo_id=HF_REPO_ID, filename=HF_MODEL_BASENAME)
+
+# Initialize LLaMA via llama_cpp
+llm = Llama(
+    model_path=model_path,
+    n_threads=int(os.getenv("LLAMA_THREADS", "4")),
+    n_batch=int(os.getenv("LLAMA_BATCH", "256")),
+    n_gpu_layers=int(os.getenv("LLAMA_GPU_LAYERS", "43")),
+    n_ctx=int(os.getenv("LLAMA_CTX", "8192"))
+)
+
+# Prompt templates from notebook
+system_prompt_few_shot = """
+SYSTEM:
+You are an AI medical assistant specializing in differential diagnosis.
+Generate the most likely list of diagnoses based on examples.
+
+USER: A 45-year-old male, fever, cough, fatigue.
+SYSTEM: [Flu, COVID-19, Pneumonia]
+
+USER: A 30-year-old female, severe abdominal pain, nausea.
+SYSTEM: [Appendicitis, Gallstones, Gastritis]
+
+USER: A 10-year-old female, wheezing.
+SYSTEM: [Asthma, Respiratory Infection]
+
+USER:
+"""
+system_prompt_cot = """
+SYSTEM:
+You are a medical expert performing differential diagnosis through step-by-step reasoning.
+Provide intermediate reasoning and final diagnoses.
+
+USER:
+"""
+system_prompt_tot = """
+SYSTEM:
+You are a medical expert using a tree-of-thought approach for differential diagnosis.
+Construct a reasoning tree then provide final diagnoses.
+
+USER:
+"""
+
+def lcpp_llm(prompt, max_tokens=2048, temperature=0, stop=["USER"]):
+    return llm(prompt=prompt, max_tokens=max_tokens, temperature=temperature, stop=stop)
+
+def extract_list(text):
+    match = re.search(r'\[(.*?)\]', text)
+    if match:
+        return [item.strip() for item in match.group(1).split(",")]
+    return []
+
+def determine_most_probable(few_shot, cot, tot):
+    counts = Counter(few_shot + cot + tot)
+    if not counts:
+        return "No Clear Diagnosis"
+    max_occ = max(counts.values())
+    for diag, cnt in counts.items():
+        if cnt == max_occ:
+            return diag
+
+def medical_diagnosis(symptoms: str):
+    try:
+        # Generate responses
+        resp_few = lcpp_llm(system_prompt_few_shot + symptoms)
+        resp_cot = lcpp_llm(system_prompt_cot + symptoms)
+        resp_tot = lcpp_llm(system_prompt_tot + symptoms)
+
+        # Extract text
+        text_few = resp_few['choices'][0]['text'].strip()
+        text_cot = resp_cot['choices'][0]['text'].strip()
+        text_tot = resp_tot['choices'][0]['text'].strip()
+
+        # Parse lists
+        few = extract_list(text_few)
+        cot = extract_list(text_cot)
+        tot = extract_list(text_tot)
+        most = determine_most_probable(few, cot, tot)
+
+        # Format Markdown output
+        return f"""
+### Differential Diagnosis Results
+
+**Few-Shot Diagnoses:** {', '.join(few) if few else 'No Diagnosis'}
+
+**Chain-of-Thought Diagnoses:** {', '.join(cot) if cot else 'No Diagnosis'}
+
+**Tree-of-Thought Diagnoses:** {', '.join(tot) if tot else 'No Diagnosis'}
+
+**Most Probable Diagnosis:** {most}
+"""
+    except Exception as e:
+        return f"Error: {e}"
+
+# Gradio app definition
+with gr.Blocks() as demo:
+    gr.Markdown("# Differential Diagnosis Explorer (Local LLaMA)")
+    cond = gr.Textbox(label="Patient Condition", placeholder="A 35-year-old male, fever, wheezing, nausea.")
+    out = gr.Markdown()
+    btn = gr.Button("Diagnose")
+    btn.click(fn=medical_diagnosis, inputs=cond, outputs=out)
+
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0", server_port=int(os.getenv('PORT', 7860)))