Corregir API de gráficos vectoriales - usar requests en lugar de InferenceClient

app.py

@@ -761,10 +761,12 @@ def load_vector_model(model_name):
                 
                 # Para SVGDreamer, no necesitamos cargar el modelo localmente
                 # Solo verificamos que podemos acceder a la API
-                from huggingface_hub import InferenceClient
+                import requests
                 
                 try:
-                    client = InferenceClient("jree423/svgdreamer")
+                    API_URL = "https://api-inference.huggingface.co/models/jree423/svgdreamer"
+                    headers = {"Authorization": f"Bearer {HF_TOKEN}"} if HF_TOKEN else {}
+                    
                     # Hacer una prueba simple para verificar acceso
                     test_payload = {
                         "inputs": "test",
@@ -784,7 +786,7 @@ def load_vector_model(model_name):
                 
                 model_cache[model_name] = {
                     "type": "svgdreamer",
-                    "client": "huggingface_hub.InferenceClient"
+                    "client": "requests"
                 }
                 
             else:
@@ -793,10 +795,11 @@ def load_vector_model(model_name):
                 print("✅ Modelo de vector configurado (usa API de Hugging Face)")
                 
                 # Verificar acceso al modelo
-                from huggingface_hub import InferenceClient
+                import requests
                 
                 try:
-                    client = InferenceClient(model_name)
+                    API_URL = f"https://api-inference.huggingface.co/models/{model_name}"
+                    headers = {"Authorization": f"Bearer {HF_TOKEN}"} if HF_TOKEN else {}
                     print(f"✅ Acceso a {model_name} verificado")
                 except Exception as e:
                     print(f"⚠️ Advertencia: No se pudo verificar acceso a {model_name}: {e}")
@@ -804,7 +807,7 @@ def load_vector_model(model_name):
                 
                 model_cache[model_name] = {
                     "type": "vector_generic",
-                    "client": "huggingface_hub.InferenceClient"
+                    "client": "requests"
                 }
             
             load_time = time.time() - start_time
@@ -819,7 +822,7 @@ def load_vector_model(model_name):
             print("🔄 Fallback a SVGDreamer...")
             model_cache[model_name] = {
                 "type": "svgdreamer",
-                "client": "huggingface_hub.InferenceClient"
+                "client": "requests"
             }
             print("✅ Fallback exitoso con SVGDreamer")
     else:
@@ -1159,20 +1162,45 @@ def generate_vector(prompt, model_name, style="iconography", n_particle=4, num_i
             print(f"🚀 Enviando request a SVGDreamer...")
             print(f"📦 Payload: {payload}")
             
-            # Realizar request
-            result = client.post(json=payload)
+            # Realizar request usando el método correcto
+            try:
+                # Intentar con el método text_generation para SVGDreamer
+                result = client.text_generation(
+                    prompt,
+                    model="jree423/svgdreamer",
+                    parameters={
+                        "n_particle": n_particle,
+                        "num_iter": num_iter,
+                        "guidance_scale": guidance_scale,
+                        "style": style,
+                        "width": width,
+                        "height": height,
+                        "seed": seed
+                    }
+                )
+            except Exception as e:
+                print(f"⚠️ Error con text_generation: {e}")
+                # Fallback: usar requests directamente
+                import requests
+                
+                API_URL = "https://api-inference.huggingface.co/models/jree423/svgdreamer"
+                headers = {"Authorization": f"Bearer {HF_TOKEN}"} if HF_TOKEN else {}
+                
+                response = requests.post(API_URL, headers=headers, json=payload)
+                if response.status_code == 200:
+                    result = response.json()
+                else:
+                    raise Exception(f"Error en API request: {response.status_code} - {response.text}")
             
             print(f"✅ Respuesta recibida de SVGDreamer")
             print(f"📊 Tipo de respuesta: {type(result)}")
             
             # Procesar respuesta
-            if hasattr(result, 'content'):
-                # Si es una respuesta HTTP
-                svg_content = result.content
-                if isinstance(svg_content, bytes):
-                    svg_content = svg_content.decode('utf-8')
+            if isinstance(result, dict) and 'generated_text' in result:
+                svg_content = result['generated_text']
+            elif isinstance(result, list):
+                svg_content = result
             else:
-                # Si es una respuesta directa
                 svg_content = result
             
             # Si la respuesta es una lista de partículas
@@ -1229,10 +1257,11 @@ def generate_vector(prompt, model_name, style="iconography", n_particle=4, num_i
             # Para otros modelos de vector (usar API genérica)
             print(f"🎨 Usando modelo genérico para vector: {model_name}")
             
-            # Usar la API de Hugging Face genérica
-            from huggingface_hub import InferenceClient
+            # Usar requests directamente para modelos genéricos
+            import requests
             
-            client = InferenceClient(model_name)
+            API_URL = f"https://api-inference.huggingface.co/models/{model_name}"
+            headers = {"Authorization": f"Bearer {HF_TOKEN}"} if HF_TOKEN else {}
             
             # Preparar payload genérico
             payload = {
@@ -1250,15 +1279,19 @@ def generate_vector(prompt, model_name, style="iconography", n_particle=4, num_i
             print(f"🚀 Enviando request a {model_name}...")
             
             # Realizar request
-            result = client.post(json=payload)
+            response = requests.post(API_URL, headers=headers, json=payload)
+            
+            if response.status_code != 200:
+                raise Exception(f"Error en API request: {response.status_code} - {response.text}")
             
+            result = response.json()
             print(f"✅ Respuesta recibida de {model_name}")
             
             # Procesar respuesta genérica
-            if hasattr(result, 'content'):
-                svg_content = result.content
-                if isinstance(svg_content, bytes):
-                    svg_content = svg_content.decode('utf-8')
+            if isinstance(result, dict) and 'generated_text' in result:
+                svg_content = result['generated_text']
+            elif isinstance(result, list):
+                svg_content = result[0] if len(result) > 0 else str(result)
             else:
                 svg_content = result
             

test_vector_graphics.py

@@ -8,15 +8,17 @@ import os
 import sys
 import time
 import tempfile
-from huggingface_hub import InferenceClient
+import requests
 
 def test_svgdreamer_basic():
     """Prueba básica de SVGDreamer"""
     print("🎨 Probando SVGDreamer - Generación básica...")
     
     try:
-        # Configurar cliente
-        client = InferenceClient("jree423/svgdreamer")
+        # Configurar API
+        API_URL = "https://api-inference.huggingface.co/models/jree423/svgdreamer"
+        hf_token = os.getenv("HF_TOKEN") or os.getenv("HUGGING_FACE_HUB_TOKEN")
+        headers = {"Authorization": f"Bearer {hf_token}"} if hf_token else {}
         
         # Payload básico
         payload = {
@@ -36,17 +38,21 @@ def test_svgdreamer_basic():
         
         # Realizar request
         start_time = time.time()
-        result = client.post(json=payload)
+        response = requests.post(API_URL, headers=headers, json=payload)
         generation_time = time.time() - start_time
         
+        if response.status_code != 200:
+            raise Exception(f"Error en API: {response.status_code} - {response.text}")
+        
+        result = response.json()
         print(f"✅ Respuesta recibida en {generation_time:.2f}s")
         print(f"📊 Tipo de respuesta: {type(result)}")
         
         # Procesar respuesta
-        if hasattr(result, 'content'):
-            svg_content = result.content
-            if isinstance(svg_content, bytes):
-                svg_content = svg_content.decode('utf-8')
+        if isinstance(result, dict) and 'generated_text' in result:
+            svg_content = result['generated_text']
+        elif isinstance(result, list):
+            svg_content = result
         else:
             svg_content = result
         
@@ -84,7 +90,9 @@ def test_svgdreamer_multiple_styles():
     prompt = "a friendly robot character"
     
     try:
-        client = InferenceClient("jree423/svgdreamer")
+        API_URL = "https://api-inference.huggingface.co/models/jree423/svgdreamer"
+        hf_token = os.getenv("HF_TOKEN") or os.getenv("HUGGING_FACE_HUB_TOKEN")
+        headers = {"Authorization": f"Bearer {hf_token}"} if hf_token else {}
         
         for style in styles:
             print(f"🎯 Probando estilo: {style}")
@@ -103,10 +111,13 @@ def test_svgdreamer_multiple_styles():
             }
             
             start_time = time.time()
-            result = client.post(json=payload)
+            response = requests.post(API_URL, headers=headers, json=payload)
             generation_time = time.time() - start_time
             
-            print(f"✅ Estilo {style} completado en {generation_time:.2f}s")
+            if response.status_code == 200:
+                print(f"✅ Estilo {style} completado en {generation_time:.2f}s")
+            else:
+                print(f"❌ Error con estilo {style}: {response.status_code}")
             
             # Pausa entre requests para no sobrecargar
             time.sleep(1)
@@ -123,7 +134,9 @@ def test_svgdreamer_multiple_particles():
     print("\n🎨 Probando SVGDreamer - Múltiples partículas...")
     
     try:
-        client = InferenceClient("jree423/svgdreamer")
+        API_URL = "https://api-inference.huggingface.co/models/jree423/svgdreamer"
+        hf_token = os.getenv("HF_TOKEN") or os.getenv("HUGGING_FACE_HUB_TOKEN")
+        headers = {"Authorization": f"Bearer {hf_token}"} if hf_token else {}
         
         payload = {
             "inputs": "geometric patterns in bright colors",
@@ -141,16 +154,20 @@ def test_svgdreamer_multiple_particles():
         print(f"📦 Enviando payload con 3 partículas...")
         
         start_time = time.time()
-        result = client.post(json=payload)
+        response = requests.post(API_URL, headers=headers, json=payload)
         generation_time = time.time() - start_time
         
+        if response.status_code != 200:
+            raise Exception(f"Error en API: {response.status_code} - {response.text}")
+        
+        result = response.json()
         print(f"✅ Respuesta recibida en {generation_time:.2f}s")
         
         # Procesar respuesta
-        if hasattr(result, 'content'):
-            svg_content = result.content
-            if isinstance(svg_content, bytes):
-                svg_content = svg_content.decode('utf-8')
+        if isinstance(result, dict) and 'generated_text' in result:
+            svg_content = result['generated_text']
+        elif isinstance(result, list):
+            svg_content = result
         else:
             svg_content = result
         
@@ -176,7 +193,9 @@ def test_error_handling():
     print("\n🎨 Probando manejo de errores...")
     
     try:
-        client = InferenceClient("jree423/svgdreamer")
+        API_URL = "https://api-inference.huggingface.co/models/jree423/svgdreamer"
+        hf_token = os.getenv("HF_TOKEN") or os.getenv("HUGGING_FACE_HUB_TOKEN")
+        headers = {"Authorization": f"Bearer {hf_token}"} if hf_token else {}
         
         # Payload con parámetros inválidos
         payload = {
@@ -195,8 +214,11 @@ def test_error_handling():
         print("🧪 Probando parámetros extremos...")
         
         try:
-            result = client.post(json=payload)
-            print("⚠️ Request completado (esperaba error)")
+            response = requests.post(API_URL, headers=headers, json=payload)
+            if response.status_code == 200:
+                print("⚠️ Request completado (esperaba error)")
+            else:
+                print(f"✅ Error capturado correctamente: {response.status_code}")
         except Exception as e:
             print(f"✅ Error capturado correctamente: {type(e).__name__}")