Update app.py

app.py

@@ -1,136 +1,135 @@
-import gradio as gr
-import torch
-from transformers import (
-    Idefics2Processor, Idefics2ForConditionalGeneration,
-    Blip2Processor, Blip2ForConditionalGeneration
-)
-from PIL import Image
-import time
-import pandas as pd
-import nltk
-from nltk.translate.bleu_score import sentence_bleu
-
-# Descargar 'punkt' si no está disponible
-try:
-    nltk.data.find("tokenizers/punkt")
-except LookupError:
-    nltk.download("punkt")
-
-# Configuración del dispositivo
-device = "cuda" if torch.cuda.is_available() else "cpu"
-print(f"Usando dispositivo: {device}")
-
-# Definición de modelos
-models = {
-    "IDEFICS2": {
-        "model_id": "HuggingFaceM4/idefics2-8b",
-        "processor_class": Idefics2Processor,
-        "model_class": Idefics2ForConditionalGeneration,
-        "caption_prompt": "<image>Describe the image in detail"
-    },
-    "BLIP2": {
-        "model_id": "Salesforce/blip2-opt-2.7b",
-        "processor_class": Blip2Processor,
-        "model_class": Blip2ForConditionalGeneration,
-        "caption_prompt": ""  # Prompt vacío para BLIP2
-    }
-}
-
-# Cargar modelos (pre-cargados para evitar retrasos)
-model_instances = {}
-for model_name, config in models.items():
-    processor = config["processor_class"].from_pretrained(config["model_id"])
-    model = config["model_class"].from_pretrained(config["model_id"]).to(device)
-    model_instances[model_name] = (processor, model)
-
-# Preguntas VQA predefinidas
-vqa_questions = [
-    "Are there people in the image?",
-    "Which color predominates in the image?"
-]
-
-# Referencia genérica para BLEU (puedes ajustar según necesidades)
-reference_caption = ["An image with people and various objects"]
-
-def infer(image, model_name, task, question=None):
-    if image is None:
-        return "Por favor, sube una imagen.", None, None, None, None, None
-
-    # Abrir y preparar la imagen
-    image = Image.open(image).convert("RGB")
-    if "BLIP2" in model_name:
-        image = image.resize((224, 224))
-
-    processor, model = model_instances[model_name]
-    
-    start_time = time.time()
-    vram = torch.cuda.memory_allocated() / 1024**3 if torch.cuda.is_available() else 0
-
-    if task == "captioning":
-        caption_prompt = models[model_name]["caption_prompt"]
-        caption_text = "" if "BLIP2" in model_name else caption_prompt
-        inputs = processor(images=image, text=caption_text, return_tensors="pt").to(device)
-        output_ids = model.generate(
-            **inputs,
-            max_new_tokens=50,
-            num_beams=5 if "BLIP2" in model_name else 1,
-            no_repeat_ngram_size=2 if "BLIP2" in model_name else 0
-        )
-        caption = processor.decode(output_ids[0], skip_special_tokens=True)
-        inference_time = time.time() - start_time
-
-        # Calcular BLEU (simplificado, usando referencia genérica)
-        bleu_score = sentence_bleu([reference_caption[0].split()], caption.split()) if caption else 0.0
-
-        return (caption, inference_time, None, None, vram, bleu_score)
-
-    elif task == "vqa" and question:
-        vqa_text = question if "BLIP2" in model_name else f"<image>Q: {question}"
-        inputs = processor(images=image, text=vqa_text, return_tensors="pt").to(device)
-        output_ids = model.generate(
-            **inputs,
-            max_new_tokens=10,
-            num_beams=5 if "BLIP2" in model_name else 1,
-            no_repeat_ngram_size=2 if "BLIP2" in model_name else 0
-        )
-        vqa_answer = processor.decode(output_ids[0], skip_special_tokens=True)
-        inference_time = time.time() - start_time
-
-        return (None, None, vqa_answer, inference_time, vram, None)
-
-    return "Selecciona una tarea válida y, para VQA, una pregunta.", None, None, None, None, None
-
-# Interfaz Gradio
-with gr.Blocks(title="MLLM Benchmark Demo") as demo:
-    gr.Markdown("# Demostración de Benchmark para Modelos Multimodales (MLLMs)")
-    gr.Markdown("Sube una imagen, selecciona un modelo y una tarea, y obtén resultados de captioning o VQA.")
-
-    with gr.Row():
-        with gr.Column():
-            image_input = gr.Image(type="filepath", label="Subir Imagen")
-            model_dropdown = gr.Dropdown(choices=["IDEFICS2", "BLIP2"], label="Seleccionar Modelo", value="IDEFICS2")
-            task_dropdown = gr.Dropdown(choices=["captioning", "vqa"], label="Seleccionar Tarea", value="captioning")
-            question_input = gr.Textbox(label="Pregunta VQA (opcional, solo para VQA)", placeholder="Ej: Are there people in the image?")
-            submit_btn = gr.Button("Generar")
-
-        with gr.Column():
-            caption_output = gr.Textbox(label="Subtítulo Generado")
-            vqa_output = gr.Textbox(label="Respuesta VQA")
-            metrics_output = gr.Textbox(label="Métricas (Tiempo, VRAM, BLEU)")
-
-    submit_btn.click(
-        fn=infer,
-        inputs=[image_input, model_dropdown, task_dropdown, question_input],
-        outputs=[caption_output, gr.Number(label="Tiempo Captioning (s)"), vqa_output, gr.Number(label="Tiempo VQA (s)"), gr.Number(label="VRAM (GB)"), gr.Number(label="BLEU Score")]
-    )
-
-    gr.Markdown("### Notas")
-    gr.Markdown("""
-    - Este demo usa un subconjunto de imágenes de COCO 2017. Descarga las imágenes desde [cocodataset.org](http://cocodataset.org/#download) si deseas usar tus propias imágenes.
-    - Requiere GPU para mejores resultados, pero funciona en CPU con tiempos más largos.
-    - La métrica BLEU usa una referencia genérica y puede no reflejar la calidad real.
-    - Para más detalles, consulta el [repositorio del paper](https://huggingface.co/datasets/pdro-ruiz/multimodal-llms-paper-2025).
-    """)
-
-if __name__ == "__main__":
+import gradio as gr
+import torch
+from transformers import (
+    Idefics2Processor, Idefics2ForConditionalGeneration,
+    Blip2Processor, Blip2ForConditionalGeneration
+)
+from PIL import Image
+import time
+import pandas as pd
+import nltk
+from nltk.translate.bleu_score import sentence_bleu
+
+# Descargar 'punkt' si no está disponible
+try:
+    nltk.data.find("tokenizers/punkt")
+except LookupError:
+    nltk.download("punkt")
+
+# Configuración del dispositivo
+device = "cuda" if torch.cuda.is_available() else "cpu"
+print(f"Usando dispositivo: {device}")
+
+# Definición de modelos
+models = {
+    "IDEFICS2": {
+        "model_id": "HuggingFaceM4/idefics2-8b",
+        "processor_class": Idefics2Processor,
+        "model_class": Idefics2ForConditionalGeneration,
+        "caption_prompt": "<image>Describe the image in detail"
+    },
+    "BLIP2": {
+        "model_id": "Salesforce/blip2-opt-2.7b",
+        "processor_class": Blip2Processor,
+        "model_class": Blip2ForConditionalGeneration,
+        "caption_prompt": ""  # Prompt vacío para BLIP2
+    }
+}
+
+# Cargar modelos (pre-cargados para evitar retrasos)
+model_instances = {}
+for model_name, config in models.items():
+    processor = config["processor_class"].from_pretrained(config["model_id"])
+    model = config["model_class"].from_pretrained(config["model_id"]).to(device)
+    model_instances[model_name] = (processor, model)
+
+# Preguntas VQA predefinidas
+vqa_questions = [
+    "Are there people in the image?",
+    "Which color predominates in the image?"
+]
+
+# Referencia genérica para BLEU (puedes ajustar según necesidades)
+reference_caption = ["An image with people and various objects"]
+
+def infer(image, model_name, task, question=None):
+    if image is None:
+        return "Por favor, sube una imagen.", None, None, None, None, None
+
+    # Abrir y preparar la imagen
+    image = Image.open(image).convert("RGB")
+    if "BLIP2" in model_name:
+        image = image.resize((224, 224))
+
+    processor, model = model_instances[model_name]
+    
+    start_time = time.time()
+    vram = torch.cuda.memory_allocated() / 1024**3 if torch.cuda.is_available() else 0
+
+    if task == "captioning":
+        caption_prompt = models[model_name]["caption_prompt"]
+        caption_text = "" if "BLIP2" in model_name else caption_prompt
+        inputs = processor(images=image, text=caption_text, return_tensors="pt").to(device)
+        output_ids = model.generate(
+            **inputs,
+            max_new_tokens=50,
+            num_beams=5 if "BLIP2" in model_name else 1,
+            no_repeat_ngram_size=2 if "BLIP2" in model_name else 0
+        )
+        caption = processor.decode(output_ids[0], skip_special_tokens=True)
+        inference_time = time.time() - start_time
+
+        # Calcular BLEU (simplificado, usando referencia genérica)
+        bleu_score = sentence_bleu([reference_caption[0].split()], caption.split()) if caption else 0.0
+
+        return (caption, inference_time, None, None, vram, bleu_score)
+
+    elif task == "vqa" and question:
+        vqa_text = question if "BLIP2" in model_name else f"<image>Q: {question}"
+        inputs = processor(images=image, text=vqa_text, return_tensors="pt").to(device)
+        output_ids = model.generate(
+            **inputs,
+            max_new_tokens=10,
+            num_beams=5 if "BLIP2" in model_name else 1,
+            no_repeat_ngram_size=2 if "BLIP2" in model_name else 0
+        )
+        vqa_answer = processor.decode(output_ids[0], skip_special_tokens=True)
+        inference_time = time.time() - start_time
+
+        return (None, None, vqa_answer, inference_time, vram, None)
+
+    return "Selecciona una tarea válida y, para VQA, una pregunta.", None, None, None, None, None
+
+# Interfaz Gradio
+with gr.Blocks(title="MLLM Benchmark Demo") as demo:
+    gr.Markdown("# Benchmark para Modelos Multimodales (MLLMs)")
+    gr.Markdown("Sube una imagen, selecciona un modelo y una tarea, y obtén resultados de captioning o VQA.")
+
+    with gr.Row():
+        with gr.Column():
+            image_input = gr.Image(type="filepath", label="Subir Imagen")
+            model_dropdown = gr.Dropdown(choices=["IDEFICS2", "BLIP2"], label="Seleccionar Modelo", value="IDEFICS2")
+            task_dropdown = gr.Dropdown(choices=["captioning", "vqa"], label="Seleccionar Tarea", value="captioning")
+            question_input = gr.Textbox(label="Pregunta VQA (opcional, solo para VQA)", placeholder="Ej: Are there people in the image?")
+            submit_btn = gr.Button("Generar")
+
+        with gr.Column():
+            caption_output = gr.Textbox(label="Subtítulo Generado")
+            vqa_output = gr.Textbox(label="Respuesta VQA")
+            metrics_output = gr.Textbox(label="Métricas (Tiempo, VRAM, BLEU)")
+
+    submit_btn.click(
+        fn=infer,
+        inputs=[image_input, model_dropdown, task_dropdown, question_input],
+        outputs=[caption_output, gr.Number(label="Tiempo Captioning (s)"), vqa_output, gr.Number(label="Tiempo VQA (s)"), gr.Number(label="VRAM (GB)"), gr.Number(label="BLEU Score")]
+    )
+
+    gr.Markdown("### Notas")
+    gr.Markdown("""
+    - para mejroar la velocidad de inferencia, descarga en local y usar GPU avanzada.
+    - La métrica BLEU usa una referencia genérica y puede no reflejar la calidad real.
+    - Para más detalles, consulta el [repositorio del paper](https://huggingface.co/spaces/Pdro-ruiz/MLLM_Estado_del_Arte_Feb25/tree/main).
+    """)
+
+if __name__ == "__main__":
     demo.launch()