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from fastapi import FastAPI, HTTPException, BackgroundTasks
from fastapi.responses import Response
from pydantic import BaseModel
import torch
import open_clip
import numpy as np
import json
import os
import random
import requests
from fastapi.middleware.cors import CORSMiddleware
import base64
from datasets import load_dataset
from io import BytesIO
from PIL import Image, ImageDraw, ImageFont
from huggingface_hub import login
import google.generativeai as genai
from typing import Optional, List, Any, Dict, Union
# IMPORTANTE: Importamos LCMScheduler
from diffusers import StableDiffusionPipeline, LCMScheduler
app = FastAPI(title="Mirage Medical Search API")
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
# --- 1. CONFIGURACIÓN DE MODELOS ---
MODEL_NAME = 'hf-hub:luhuitong/CLIP-ViT-L-14-448px-MedICaT-ROCO'
HF_DATASET_ID = "mdwiratathya/ROCO-radiology"
SPLIT = "train"
device = "cpu"
# Variables Globales
model = None
tokenizer = None
embeddings = None # Image Embeddings (Visual)
text_embeddings = None # Caption Embeddings (Semántico - NUEVO)
metadata = None
dataset_stream = None
gemini_available = False
pipe = None
# Variables para el juego (Neural Training)
GAME_CACHE = []
# --- 2. AUTENTICACIÓN ---
try:
hf_token = os.environ.get('HF_TOKEN')
if hf_token:
login(token=hf_token)
google_key = os.environ.get('GOOGLE_API_KEY')
if google_key:
genai.configure(api_key=google_key)
gemini_available = True
except Exception as e:
print(f"Error auth: {e}")
# --- HELPER: PLACEHOLDER ---
def create_placeholder_image(text="Image Error"):
img = Image.new('RGB', (512, 512), color=(40, 40, 45))
d = ImageDraw.Draw(img)
try:
font = ImageFont.truetype("arial.ttf", 20)
except:
font = None
d.text((20, 200), f"No Image Available", fill=(255, 100, 100), font=font)
d.text((20, 230), f"{text}", fill=(200, 200, 200), font=font)
img_byte_arr = BytesIO()
img.save(img_byte_arr, format='JPEG')
return img_byte_arr.getvalue()
# --- 3. CARGA DE DATOS ---
@app.on_event("startup")
async def load_data():
global model, tokenizer, embeddings, text_embeddings, metadata, dataset_stream, pipe
print("--- INICIANDO MIRAGE BACKEND (v2.3 - Weighted Retrieval) ---")
# 1. CARGAR CLIP
try:
print("👁️ Cargando CLIP...")
model, _, _ = open_clip.create_model_and_transforms(MODEL_NAME, device=device)
tokenizer = open_clip.get_tokenizer(MODEL_NAME)
model.eval()
print("✅ CLIP cargado.")
except Exception as e:
print(f"❌ Error CLIP: {e}")
# 2. CARGAR METADATA (Prioridad a metadata_text.json si existe)
print("📦 Cargando Metadata...")
if os.path.exists("metadata_text.json"):
print(" -> Usando metadata_text.json")
with open("metadata_text.json", 'r') as f:
metadata = json.load(f)
elif os.path.exists("metadata.json"):
print(" -> Usando metadata.json (Fallback)")
with open("metadata.json", 'r') as f:
metadata = json.load(f)
else:
print("⚠️ NO SE ENCONTRÓ METADATA.")
metadata = [{"dataset_index": 0, "filename": "error", "caption": "Error"}]
# 3. CARGAR EMBEDDINGS DE IMAGEN
if os.path.exists("embeddings.npy"):
embeddings = np.load("embeddings.npy")
print(f"✅ Image Embeddings listos: {embeddings.shape[0]} registros.")
else:
print("⚠️ NO SE ENCONTRARON IMAGE EMBEDDINGS.")
embeddings = np.zeros((1, 768))
# 4. CARGAR EMBEDDINGS DE TEXTO (NUEVO)
# Buscamos el archivo generado por tu script 'embeddings_text.py'
if os.path.exists("embeddings_text.npy"):
text_embeddings = np.load("embeddings_text.npy")
print(f"✅ Text Embeddings listos: {text_embeddings.shape[0]} registros.")
else:
print("⚠️ NO SE ENCONTRARON TEXT EMBEDDINGS (embeddings_text.npy).")
text_embeddings = None
# 5. CARGAR DATASET
try:
print("📦 Cargando Dataset en RAM (1-2 mins)...")
dataset_stream = load_dataset(HF_DATASET_ID, split=SPLIT, streaming=False)
print(f"✅ Dataset listo. Total: {len(dataset_stream)}")
except Exception as e:
print(f"❌ Error dataset: {e}")
dataset_stream = None
# 6. CARGAR STABLE DIFFUSION
print("🎨 Cargando modelo generativo (LCM Mode)...")
try:
model_id = "Nihirc/Prompt2MedImage"
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float32)
print("⚡ Inyectando pesos LCM-LoRA...")
pipe.load_lora_weights("latent-consistency/lcm-lora-sdv1-5")
pipe.fuse_lora()
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config, solver_order=2)
pipe.safety_checker = None
pipe.requires_safety_checker = False
if device == "cpu":
pipe = pipe.to("cpu")
pipe.enable_attention_slicing()
else:
pipe = pipe.to("cuda")
print("✅ Generador LCM listo.")
except Exception as e:
print(f"❌ Error Generador: {e}")
# Inicializar Cache del Juego
try:
print("🎮 Precalentando Cache del Juego...")
populate_game_cache(20)
print(f"✅ Juego listo con {len(GAME_CACHE)} preguntas.")
except Exception as e:
print(f"⚠️ No se pudo inicializar el juego: {e}")
# --- 4. FUNCIONES CORE ---
def translate_to_english(text, source_lang):
if not text or not source_lang: return text
clean_lang = source_lang.lower().strip()
if clean_lang in ['en', 'english', 'inglés', 'ingles']: return text
if not gemini_available: return text
try:
model_llm = genai.GenerativeModel('gemini-2.5-flash')
prompt = f"Translate the following medical text from {source_lang} to English. Maintain strict medical terminology accuracy. Only output the translated text, nothing else.\n\nText: {text}"
response = model_llm.generate_content(prompt)
return response.text.strip()
except Exception as e:
print(f"❌ Translation Error: {e}")
return text
def calculate_vector(text, add=None, sub=None):
with torch.no_grad():
text_tokens = tokenizer([text]).to(device)
vec = model.encode_text(text_tokens)
vec /= vec.norm(dim=-1, keepdim=True)
if add and add.strip():
add_vec = model.encode_text(tokenizer([add]).to(device))
add_vec /= add_vec.norm(dim=-1, keepdim=True)
vec = vec + add_vec
if sub and sub.strip():
sub_vec = model.encode_text(tokenizer([sub]).to(device))
sub_vec /= sub_vec.norm(dim=-1, keepdim=True)
vec = vec - sub_vec
vec /= vec.norm(dim=-1, keepdim=True)
return vec
def get_retrieval_and_context(query_vector, top_k, alpha=1.0):
"""
Realiza el retrieval ponderado.
alpha = 1.0 -> Solo similitud visual (Imagen vs Texto Query)
alpha = 0.0 -> Solo similitud semántica (Caption vs Texto Query)
alpha = 0.5 -> Híbrido
"""
query_vec_np = query_vector.cpu().numpy()
# Aseguramos que usamos el mínimo común de elementos para evitar errores de dimensión
n_imgs = embeddings.shape[0]
n_txts = text_embeddings.shape[0] if text_embeddings is not None else 0
# Si tenemos ambos, el límite es el menor de los dos para alinear índices
if text_embeddings is not None:
limit = min(n_imgs, n_txts)
# Slicing seguro: usamos solo hasta el 'limit'
current_embeddings = embeddings[:limit]
current_text_embeddings = text_embeddings[:limit]
else:
limit = n_imgs
current_embeddings = embeddings
current_text_embeddings = None
# 1. Similitud Visual (Query vs Image Embeddings)
# query_vec_np es (1, 768), embeddings es (N, 768) -> resultado (N,)
sim_img = (query_vec_np @ current_embeddings.T).squeeze()
# 2. Similitud Semántica (Query vs Caption Embeddings)
sim_txt = np.zeros_like(sim_img)
if current_text_embeddings is not None:
sim_txt = (query_vec_np @ current_text_embeddings.T).squeeze()
# 3. Combinación Ponderada
# Si alpha es 1.0, sim_txt se ignora. Si alpha es 0, sim_img se ignora.
final_scores = (alpha * sim_img) + ((1.0 - alpha) * sim_txt)
# Ordenar índices (descendente)
best_indices = final_scores.argsort()[-top_k:][::-1]
real_matches = []
retrieved_captions = []
for idx in best_indices:
idx = int(idx)
# Validación de seguridad por si metadata es más corta
if idx >= len(metadata): continue
meta = metadata[idx]
safe_index = meta.get('dataset_index', idx)
real_matches.append({
"url": f"/image/{safe_index}",
"score": float(final_scores[idx]),
"filename": meta.get("filename", "img"),
"caption": meta.get("caption", ""),
"index": safe_index
})
cap = meta.get("caption", "")
if cap and len(cap) > 5:
retrieved_captions.append(cap)
return real_matches, retrieved_captions
def generate_llm_prompt(captions, user_text):
if not gemini_available or not captions:
return user_text + ". " + (captions[0] if captions else "")
try:
llm = genai.GenerativeModel('gemini-2.5-flash')
prompt = f"Summarize these medical findings into a concise radiology description based on the query '{user_text}': {', '.join(captions[:3])}"
res = llm.generate_content(prompt)
return res.text.strip()
except:
return user_text
def generate_synthetic_image(prompt, steps=5, guidance=1.5):
global pipe
if pipe is None: return None
try:
NEGATIVE_PROMPT = "painting, artistic, drawing, illustration, blur, low quality, distorted, abstract, text, watermark, grid, noise, glitch"
image = pipe(prompt[:77], height=512, width=512, num_inference_steps=steps, guidance_scale=guidance, negative_prompt=NEGATIVE_PROMPT).images[0]
draw = ImageDraw.Draw(image)
text = "Created by MIRAGE OS"
try: font = ImageFont.load_default()
except: font = None
bbox = draw.textbbox((0, 0), text, font=font)
text_w, text_h = bbox[2] - bbox[0], bbox[3] - bbox[1]
draw.text((image.width - text_w - 20, image.height - text_h - 15), text, fill=(255, 225, 210), font=font)
buffered = BytesIO()
image.save(buffered, format="JPEG")
img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
return f"data:image/jpeg;base64,{img_str}"
except Exception as e:
print(f"Error Gen Image: {e}")
return None
def fetch_image_from_stream(index):
if dataset_stream is None: return None
try:
idx = int(index)
return dataset_stream[idx]['image']
except Exception: return None
# --- FUNCIONES DE CACHE DEL JUEGO ---
def populate_game_cache(count=15):
global GAME_CACHE
if dataset_stream is None: return
new_questions = []
try:
total_items = len(dataset_stream)
for _ in range(count):
mode = random.choice(["text_to_image", "image_to_text"])
indices = random.sample(range(total_items), 4)
target_idx = indices[0]
random.shuffle(indices)
target_data = dataset_stream[target_idx]
target_caption = target_data['caption']
options = []
for idx in indices:
item = dataset_stream[idx]
options.append({
"id": idx,
"content": f"/image/{idx}" if mode == "text_to_image" else item['caption']
})
question = {
"mode": mode,
"prompt": target_caption if mode == "text_to_image" else f"/image/{target_idx}",
"correct_id": target_idx,
"options": options
}
new_questions.append(question)
GAME_CACHE.extend(new_questions)
print(f"🎮 Game Cache Refilled. Total items: {len(GAME_CACHE)}")
except Exception as e:
print(f"❌ Error refilling game cache: {e}")
# --- ENDPOINTS ---
@app.get("/")
def root(): return {"status": "online"}
@app.get("/image/{index}")
def get_image(index: str):
if index in ["None", "", "undefined"]: return Response(content=create_placeholder_image("Invalid"), media_type="image/jpeg")
if dataset_stream is None: return Response(content=create_placeholder_image("Loading..."), media_type="image/jpeg")
try:
idx_int = int(float(index))
if idx_int < 0 or idx_int >= len(dataset_stream): return Response(content=create_placeholder_image("Out of Bounds"), media_type="image/jpeg")
img = fetch_image_from_stream(idx_int)
if img:
if img.mode != 'RGB': img = img.convert('RGB')
b = BytesIO()
img.save(b, format='JPEG')
return Response(content=b.getvalue(), media_type="image/jpeg")
except Exception: pass
return Response(content=create_placeholder_image("Error"), media_type="image/jpeg")
# --- MODELOS PYDANTIC ---
class GenerationRequest(BaseModel):
original_text: str
sub_concept: Optional[str] = None
add_concept: Optional[str] = None
top_k: int = 3
gen_text: bool = True
gen_image: bool = True
guidance_scale: float = 1.5
num_inference_steps: int = 5
language: Optional[str] = "en"
alpha: Optional[float] = 1.0
class ChatRequest(BaseModel):
message: str
history: Optional[str] = ""
context: Optional[Dict[str, Any]] = None
class RateRequest(BaseModel):
query: str
image_index: int
caption: Optional[str] = ""
# --- ENDPOINT CHATBOT (Async es correcto aquí porque llama a Gemini IO) ---
@app.post("/chat_medical")
async def chat_medical(req: ChatRequest):
if not gemini_available:
raise HTTPException(status_code=503, detail="Gemini AI not configured")
try:
top_image_data = None
context_str = ""
if req.context:
context_str = f"""\n--- CURRENT SYSTEM CONTEXT ---\nUser is viewing search results for: "{req.context.get('query', 'Unknown')}"\nRETRIEVED EVIDENCE:\n"""
for i, match in enumerate(req.context.get('matches', [])[:3]):
context_str += f"\n{i+1}. Findings: {match.get('caption', 'No details')} (Relevance: {match.get('score', 0):.2f})"
if req.context.get('synthetic_prompt'):
context_str += f"\n\nGENERATED SYNTHESIS:\n{req.context.get('synthetic_prompt')}"
context_str += "\n--- END CONTEXT ---\n"
top_img_url = req.context.get('top_match_image', None)
if top_img_url and "image/" in str(top_img_url):
try:
img_id = int(str(top_img_url).split("/")[-1])
top_image_data = fetch_image_from_stream(img_id)
if top_image_data: print(f"📎 Attached context image ID: {img_id}")
except Exception as e: print(f"⚠️ Could not attach context image: {e}")
MEDICAL_SYSTEM_PROMPT = f"""
ROLE: Elite Senior MD & Radiologist.
KNOWLEDGE: Global Medical Atlases, Clinical Guidelines.
TASK: Answer medical queries.
CONTEXT AWARENESS: { "User has provided specific search results (and potentially an image) to discuss." if req.context else "General inquiry." }
STRICT RULES:
1. OUTPUT: English only. Professional, clinical tone.
2. ACCURACY: Reference standard medical consensus.
3. BREVITY: Concise and direct.
4. IMAGE ANALYSIS: If an image is provided, prioritizing analyzing it relative to the query.
"""
model_llm = genai.GenerativeModel('gemini-2.5-flash', system_instruction=MEDICAL_SYSTEM_PROMPT)
final_text_prompt = f"{context_str}\nPrevious conversation:\n{req.history}\n\nCurrent User Question: {req.message}"
inputs = [final_text_prompt]
if top_image_data: inputs.append(top_image_data)
response = model_llm.generate_content(inputs)
return {"response": response.text, "status": "success"}
except Exception as e:
print(f"❌ Error Chat: {e}")
return {"response": "I encountered an error processing your request.", "status": "error"}
@app.post("/rate_match")
async def rate_match(req: RateRequest):
if not gemini_available: return {"score": 0, "reason": "AI Service Unavailable"}
try:
image = fetch_image_from_stream(req.image_index)
model_vision = genai.GenerativeModel('gemini-2.5-flash')
prompt = f"""You are a strict medical auditor.\nQuery: "{req.query}"\nRetrieved Image Caption: "{req.caption}"\nTask: Rate the relevance of this retrieval to the query from 0 to 100.\nOutput format JSON: {{ "score": int, "reason": "brief explanation" }}"""
inputs = [prompt]
if image: inputs.append(image)
response = model_vision.generate_content(inputs)
text = response.text.strip().replace("```json", "").replace("```", "")
try: return json.loads(text)
except: return {"score": 50, "reason": response.text[:100]}
except Exception as e:
print(f"Rating Error: {e}")
return {"score": 0, "reason": "Error processing rating"}
@app.get("/game/quiz")
def get_game_quiz(background_tasks: BackgroundTasks):
global GAME_CACHE
if dataset_stream is None: raise HTTPException(status_code=503, detail="Dataset not loaded")
try:
if not GAME_CACHE: populate_game_cache(20)
if GAME_CACHE:
question = GAME_CACHE.pop(0)
if len(GAME_CACHE) < 5: background_tasks.add_task(populate_game_cache, 15)
return question
else:
populate_game_cache(1)
return GAME_CACHE.pop(0)
except Exception as e:
print(f"Game Error: {e}")
raise HTTPException(status_code=500, detail="Game generation failed")
# --- ENDPOINTS SIN ASYNC (CRÍTICO PARA EVITAR BLOQUEO DE MAIN THREAD) ---
@app.post("/generate_comparison")
def generate_comparison(req: GenerationRequest):
# NOTA: Al quitar 'async', FastAPI ejecuta esto en un threadpool,
# permitiendo que el Event Loop principal siga sirviendo imágenes (/image/{id})
if not model: raise HTTPException(status_code=503, detail="Loading...")
try:
final_query = req.original_text
final_add = req.add_concept
final_sub = req.sub_concept
if req.language and req.language.lower() not in ['en', 'english']:
final_query = translate_to_english(req.original_text, req.language)
if req.add_concept: final_add = translate_to_english(req.add_concept, req.language)
if req.sub_concept: final_sub = translate_to_english(req.sub_concept, req.language)
print(f"⚡ Procesando: '{final_query}' (Lang: {req.language}, Alpha: {req.alpha})")
response_data = {
"original_text": final_query,
"modified_text": final_query,
"original": {},
"modified": None,
"input_lang_detected": req.language
}
# 2. PROCESAR ORIGINAL
# Se pasa alpha desde el request
vec_orig = calculate_vector(final_query)
match_orig, caps_orig = get_retrieval_and_context(vec_orig, req.top_k, alpha=req.alpha if req.alpha is not None else 1.0)
prompt_orig = ""
if req.gen_text:
prompt_orig = generate_llm_prompt(caps_orig, final_query)
else:
prompt_orig = "LLM generation skipped."
img_orig_b64 = ""
if req.gen_image:
p_to_use = prompt_orig if req.gen_text else final_query
img_orig_b64 = generate_synthetic_image(p_to_use, steps=req.num_inference_steps, guidance=req.guidance_scale)
response_data["original"] = {
"real_match": match_orig,
"synthetic": {
"image_base64": img_orig_b64,
"generated_prompt": prompt_orig
}
}
# 3. PROCESAR MODIFICADO (Dual Search)
has_dual = (final_add and final_add.strip()) and (final_sub and final_sub.strip())
if has_dual:
vec_mod = calculate_vector(final_query, final_add, final_sub)
match_mod, caps_mod = get_retrieval_and_context(vec_mod, req.top_k, alpha=req.alpha if req.alpha is not None else 1.0)
prompt_mod = ""
if req.gen_text:
prompt_mod = generate_llm_prompt(caps_mod, f"{final_query} plus {final_add} minus {final_sub}")
else:
prompt_mod = "LLM generation skipped."
img_mod_b64 = ""
if req.gen_image:
p_to_use_mod = prompt_mod if req.gen_text else f"{final_query} {final_add}"
img_mod_b64 = generate_synthetic_image(p_to_use_mod, steps=req.num_inference_steps, guidance=req.guidance_scale)
response_data["modified"] = {
"real_match": match_mod,
"synthetic": {
"image_base64": img_mod_b64,
"generated_prompt": prompt_mod
}
}
response_data["modified_text"] = f"{final_query} + {final_add} - {final_sub}"
return response_data
except Exception as e:
print(f"🔥 Error: {e}")
raise HTTPException(status_code=500, detail=str(e))
@app.post("/search")
def search(req: GenerationRequest):
return generate_comparison(req) |