Versión 1.0
This commit is contained in:
lansan69
Binary file not shown.
Binary file not shown.
@@ -24,6 +24,12 @@ from app.core import config
|
||||
from app.utilities.image_utilities import json_to_rubric, encode_image_from_bytes
|
||||
from app.services.image.prompt_builder import build_image_evaluation_prompt
|
||||
from anthropic import Anthropic
|
||||
import re
|
||||
|
||||
# Importaciones de Clarifai
|
||||
from clarifai_grpc.channel.clarifai_channel import ClarifaiChannel
|
||||
from clarifai_grpc.grpc.api import resources_pb2, service_pb2, service_pb2_grpc
|
||||
from clarifai_grpc.grpc.api.status import status_code_pb2
|
||||
|
||||
# Función de adaptador principal que infiere el proveedor y llama al adaptador específico
|
||||
async def evaluate_image_with_provider(image_request: ImageRequestFile) -> StandardImageAnalysisResult:
|
||||
@@ -91,62 +97,77 @@ async def evaluate_with_openai(image_request: ImageRequestFile, prompt: str) ->
|
||||
detail=f"Error evaluando la imagen: {str(e)}"
|
||||
)
|
||||
|
||||
async def evaluate_with_clarifai(image_request: ImageRequestFile, rubric: ImageEvaluationRubric, prompt: str) -> StandardImageAnalysisResult:
|
||||
async def evaluate_with_clarifai(image_request: ImageRequestFile, prompt: str) -> StandardImageAnalysisResult:
|
||||
"""
|
||||
Función de adaptador para evaluar imágenes usando Clarifai con un modelo Multimodal.
|
||||
Función de adaptador para evaluar imágenes usando Clarifai.
|
||||
"""
|
||||
try:
|
||||
# 1. Obtener el token de configuración (PAT)
|
||||
pat = settings.CLARIFAI_API_KEY
|
||||
if not pat:
|
||||
raise ValueError("La clave CLARIFAI_API_KEY no está configurada en el entorno.")
|
||||
|
||||
# 2. Obtener la URL del modelo enviada en la petición
|
||||
model_url = image_request.model
|
||||
|
||||
CLARIFAI_API_KEY = settings.CLARIFAI_API_KEY
|
||||
if not CLARIFAI_API_KEY:
|
||||
raise HTTPException(
|
||||
status_code=500,
|
||||
detail="No se encontró CLARIFAI_API_KEY en las variables de entorno"
|
||||
)
|
||||
|
||||
# Inicializar el modelo de Clarifai
|
||||
model = Model(url=model_url, pat=pat)
|
||||
|
||||
# 3. Leer los bytes de la imagen subida
|
||||
USER_ID = "openai"
|
||||
APP_ID = "chat-completion"
|
||||
|
||||
try:
|
||||
image_bytes = await image_request.file.read()
|
||||
|
||||
if not image_bytes:
|
||||
raise ValueError("El archivo de imagen recibido está vacío.")
|
||||
# 2. Preparamos y ejecutamos la llamada a Clarifai (gRPC)
|
||||
channel = ClarifaiChannel.get_grpc_channel()
|
||||
stub = service_pb2_grpc.V2Stub(channel)
|
||||
metadata = (('authorization', 'Key ' + CLARIFAI_API_KEY),)
|
||||
userDataObject = resources_pb2.UserAppIDSet(user_id=USER_ID, app_id=APP_ID)
|
||||
|
||||
# 4. Preparar el input multimodal para Clarifai combinando la imagen y el prompt de evaluación
|
||||
multimodal_input = Inputs.get_multimodal_input(
|
||||
input_id="image_evaluation",
|
||||
image_bytes=image_bytes,
|
||||
raw_text=prompt
|
||||
request = service_pb2.PostModelOutputsRequest(
|
||||
user_app_id=userDataObject,
|
||||
model_id=image_request.model,
|
||||
inputs=[
|
||||
resources_pb2.Input(
|
||||
data=resources_pb2.Data(
|
||||
image=resources_pb2.Image(base64=image_bytes),
|
||||
text=resources_pb2.Text(raw=prompt)
|
||||
)
|
||||
)
|
||||
]
|
||||
)
|
||||
|
||||
# 5. Llamar a la API de Clarifai para evaluar la imagen
|
||||
predict_response = model.predict([multimodal_input])
|
||||
|
||||
# Extraer el texto crudo de la respuesta del modelo
|
||||
raw_output = predict_response.outputs[0].data.text.raw
|
||||
|
||||
# 6. Limpiar la respuesta y convertirla a JSON
|
||||
# Los LLMs suelen devolver el JSON envuelto en bloques de markdown (```json ... ```)
|
||||
clean_json = raw_output.replace("```json", "").replace("```", "").strip()
|
||||
response = stub.PostModelOutputs(request, metadata=metadata)
|
||||
|
||||
# Convertir el string limpio a un diccionario de Python
|
||||
parsed_data = json.loads(clean_json)
|
||||
if response.status.code != status_code_pb2.SUCCESS:
|
||||
raise Exception(f"Clarifai Error: {response.status.description}")
|
||||
|
||||
# 3. Extraemos la respuesta cruda
|
||||
raw_output = response.outputs[0].data.text.raw
|
||||
|
||||
# 7. Retornar el resultado validado contra tu esquema estándar de Pydantic
|
||||
return StandardImageAnalysisResult(**parsed_data)
|
||||
|
||||
except json.JSONDecodeError as e:
|
||||
# Error específico si el modelo alucinó texto extra y no devolvió un JSON válido
|
||||
raise HTTPException(
|
||||
status_code=500,
|
||||
detail=f"El modelo de Clarifai no devolvió un JSON válido. Error: {str(e)} | Respuesta cruda: {raw_output if 'raw_output' in locals() else 'N/A'}"
|
||||
|
||||
|
||||
# 4. Limpiamos y parseamos el JSON devuelto
|
||||
json_match = re.search(r'\{.*\}', raw_output, re.DOTALL)
|
||||
|
||||
if json_match:
|
||||
clean_json = json_match.group(0)
|
||||
parsed_data = json.loads(clean_json)
|
||||
else:
|
||||
# Respaldo en caso de que el modelo devuelva texto plano en lugar de JSON
|
||||
parsed_data = {"raw_response": raw_output}
|
||||
|
||||
# 5. Retornamos el modelo estandarizado
|
||||
return StandardImageAnalysisResult(
|
||||
status="success",
|
||||
original_filename=image_request.file.filename,
|
||||
provider_used="Clarifai",
|
||||
model_used=image_request.model,
|
||||
**parsed_data
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
# Captura cualquier otro error (problemas de red, token inválido, URL incorrecta, etc.)
|
||||
raise HTTPException(
|
||||
status_code=500,
|
||||
detail=f"Error interno al evaluar con Clarifai: {str(e)}"
|
||||
status_code=500,
|
||||
detail=f"Error evaluando la imagen con Clarifai: {str(e)}"
|
||||
)
|
||||
|
||||
async def evaluate_with_claude(image_request: ImageRequestFile, prompt: str) -> StandardImageAnalysisResult:
|
||||
|
||||
@@ -44,9 +44,10 @@ Specialization Path: {specialization_path}
|
||||
# EVALUATION RULES
|
||||
1. **Domain Calibration (CRITICAL):** Calibrate your expectations, strictness, and feedback entirely according to the **{specialization_path}** context. Do not evaluate using generalized standards; apply the specific standards expected at this exact level and category.
|
||||
2. **Strict Adherence:** Evaluate ONLY the criteria and subcriteria listed in the rubric. Do not invent new metrics.
|
||||
3. **Scoring:** Assign a numeric `score` to each criterion and subcriterion. The score must reflect how well the input meets the description of that specific metric.
|
||||
3. **Scoring:** Assign a numeric `score` to each criterion and subcriterion. The score must reflect how well the input meets the description of that specific metric.
|
||||
4. **Objective Feedback:** Generate constructive, evidence-grounded `feedback` for the overall evaluation. Mention specific elements or patterns observed in the input that justify the scores within the context of the specialization.
|
||||
5. **Subcriteria Handling:** If a criterion has `subcriteria`, evaluate each subcriterion individually. The parent criterion's score should be a logical aggregate (e.g., average) of its subcriteria scores.
|
||||
5. **Subcriteria Handling:** If a criterion has `subcriteria`, evaluate and score each subcriterion individually. The parent criterion's `score` MUST be the exact mathematical **sum** of its subcriteria scores.
|
||||
6. **Final Score Calculation:** The overall final `score` of the evaluation MUST be the exact mathematical **sum** of all the main criteria scores.
|
||||
|
||||
# OUTPUT FORMAT
|
||||
You MUST return your response EXCLUSIVELY as a raw, valid JSON object that strictly adheres to the following JSON Schema definition. Do NOT include markdown blocks (```json), explanations, or any text outside the JSON object.
|
||||
|
||||
Reference in New Issue
Block a user