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healthtechbrasil commited on
Commit
0614e7d
·
1 Parent(s): 3879eed

API FastAPI com MedGemma

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Files changed (3) hide show
  1. app.py +25 -64
  2. index.html +16 -0
  3. questions.json +0 -0
app.py CHANGED
@@ -1,64 +1,25 @@
1
- import gradio as gr
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- from huggingface_hub import InferenceClient
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-
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- """
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- For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
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- """
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- client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
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-
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-
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- def respond(
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- message,
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- history: list[tuple[str, str]],
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- system_message,
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- max_tokens,
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- temperature,
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- top_p,
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- ):
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- messages = [{"role": "system", "content": system_message}]
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-
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- for val in history:
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- if val[0]:
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- messages.append({"role": "user", "content": val[0]})
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- if val[1]:
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- messages.append({"role": "assistant", "content": val[1]})
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-
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- messages.append({"role": "user", "content": message})
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-
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- response = ""
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-
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- for message in client.chat_completion(
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- messages,
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- max_tokens=max_tokens,
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- stream=True,
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- temperature=temperature,
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- top_p=top_p,
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- ):
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- token = message.choices[0].delta.content
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-
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- response += token
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- yield response
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-
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-
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- """
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- For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
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- """
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- demo = gr.ChatInterface(
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- respond,
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- additional_inputs=[
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- gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
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- gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
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- gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
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- gr.Slider(
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- minimum=0.1,
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- maximum=1.0,
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- value=0.95,
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- step=0.05,
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- label="Top-p (nucleus sampling)",
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- ),
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- ],
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- )
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-
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-
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- if __name__ == "__main__":
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- demo.launch()
 
1
+ from fastapi import FastAPI
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+ from transformers import pipeline
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+ import json
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+
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+ app = FastAPI()
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+ medgemma = pipeline("text-generation", model="./fine_tuned_medgemma", tokenizer="./fine_tuned_medgemma")
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+
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+ # Carregar JSON como base
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+ with open("questions.json", "r") as f:
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+ questions_base = json.load(f)
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+
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+ @app.get("/generate")
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+ async def generate_question(theme: str, difficulty: str):
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+ prompt = f"Baseado nas questões da USP, gere uma questão de residência médica sobre {theme}, nível {difficulty}, com 4 opções (A, B, C, D) no estilo do JSON fornecido."
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+ response = medgemma(prompt, max_new_tokens=300, temperature=0.7, top_p=0.95)[0]['generated_text']
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+ return {"question": response}
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+
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+ @app.get("/simulado")
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+ async def get_simulado(num_questions: int = 5):
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+ simulado = []
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+ for _ in range(num_questions):
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+ prompt = "Gere uma questão no estilo do JSON da USP sobre medicina, nível médio, com 4 opções."
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+ question = medgemma(prompt, max_new_tokens=300)[0]['generated_text']
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+ simulado.append({"question": question, "options": ["A)", "B)", "C)", "D)"]}) # Placeholder
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+ return {"simulado": simulado}
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
index.html ADDED
@@ -0,0 +1,16 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ <!DOCTYPE html>
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+ <html lang="pt-br">
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+ <head><title>Simulado USP</title></head>
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+ <body>
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+ <h1>Simulado</h1>
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+ <button onclick="loadSimulado()">Carregar</button>
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+ <div id="questions"></div>
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+ <script>
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+ async function loadSimulado() {
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+ const response = await fetch('/simulado?num_questions=5');
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+ const data = await response.json();
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+ document.getElementById('questions').innerHTML = data.simulado.map(q => `<p>${q.question}</p><ul><li>A)</li><li>B)</li><li>C)</li><li>D)</li></ul>`).join('');
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+ }
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+ </script>
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+ </body>
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+ </html>
questions.json ADDED
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