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@@ -33,3 +33,7 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+163.png filter=lfs diff=lfs merge=lfs -text
+6.png filter=lfs diff=lfs merge=lfs -text
+img_71_2.png filter=lfs diff=lfs merge=lfs -text
+img_98.png filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,13 +0,0 @@
----
-title: BacanoResponder
-emoji: 👁
-colorFrom: gray
-colorTo: green
-sdk: gradio
-sdk_version: 5.32.1
-app_file: app.py
-pinned: false
-short_description: BacanoResponder - VQA en Imágenes
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,173 @@
+import gradio as gr
+from PIL import Image
+import numpy as np
+import torch
+from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
+from qwen_vl_utils import process_vision_info
+from peft import PeftModel
+from pathlib import Path
+
+system_prompt = (
+    "A conversation between User and Assistant. The user asks a question, and the Assistant solves it. "
+    "The assistant es un experto sobre Colombia. Primero razona en mente y luego da la respuesta. "
+    "El razonamiento y la respuesta van en <think></think> y <answer></answer>."
+)
+
+MODEL_ID = 'Qwen/Qwen2.5-VL-3B-Instruct'
+ADAPTER_ID = 'Factral/qwen2.5vl-3b-colombia-finetuned'
+
+processor = AutoProcessor.from_pretrained(MODEL_ID)
+
+# Carga del modelo base
+has_gpu = torch.cuda.is_available()
+attn_impl = "flash_attention_2" if has_gpu else "eager"
+model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+    MODEL_ID,
+    torch_dtype=torch.bfloat16,
+    attn_implementation=attn_impl,
+    device_map="auto"
+)
+
+# Carga y fusión del adaptador PEFT
+model = PeftModel.from_pretrained(model, ADAPTER_ID)
+model = model.merge_and_unload()
+model.eval()
+device = torch.device("cuda" if has_gpu else "cpu")
+model.to(device)
+
+example_imgs = [
+    ("6.png", "Shakira"),
+    ("163.png", "Tienda esquinera"),
+    ("img_71_2.png", "Comida colombiana"),
+    ("img_98.png", "Oso de anteojos"),
+]
+
+def cargar_imagen(imagen_path: str) -> Image.Image:
+    return Image.open(imagen_path)
+
+with gr.Blocks(theme='lone17/kotaemon') as demo:
+    demo.css = """
+        #galeria-scroll {
+            max-height: 320px;
+            overflow-y: auto;
+            border: 1px solid #ccc;
+            padding: 8px;
+            border-radius: 8px;
+        }
+    """
+
+    gr.Markdown(
+        """
+        # 🇨🇴 Bacano Responder
+        Sube o elige una imagen, haz una pregunta y obtén una respuesta con contexto local.
+        """
+    )
+
+    with gr.Row(equal_height=True):
+        # Columna izquierda
+        with gr.Column(scale=1):
+            pregunta = gr.Textbox(
+                label="❓ Pregunta sobre tu imagen",
+                placeholder="¿Qué muestra esta imagen?",
+                lines=2
+            )
+
+            with gr.Box(elem_id="galeria-scroll"):
+                galeria = gr.Gallery(
+                    label="📁 Elige una imagen de ejemplo",
+                    value=[img for img, _ in example_imgs],
+                    columns=2,
+                    height=None,
+                    allow_preview=True,
+                    show_label=True,
+                )
+
+        # Columna derecha
+        with gr.Column(scale=1):
+            imagen_mostrada = gr.Image(
+                label="🖼 Imagen seleccionada o subida",
+                type="numpy",  # Puede ser numpy o PIL; convertiremos luego si es numpy
+                height=256
+            )
+
+            respuesta = gr.Textbox(
+                label="🧠 Respuesta",
+                interactive=False,
+                lines=4
+            )
+
+            btn_procesar = gr.Button("🔍 Procesar")
+
+    def seleccionar_imagen(evt: gr.SelectData):
+        idx = evt.index
+        img_path = example_imgs[idx][0]
+        pil = cargar_imagen(img_path)
+        return np.array(pil)
+
+    galeria.select(fn=seleccionar_imagen, inputs=None, outputs=imagen_mostrada)
+
+    def responder(img, pregunta_text):
+        if img is None or pregunta_text.strip() == "":
+            return "Por favor sube una imagen y escribe una pregunta."
+
+        # Convertir array numpy a PIL si es necesario
+        if isinstance(img, np.ndarray):
+            img_pil = Image.fromarray(img.astype('uint8'))
+        else:
+            img_pil = img  # ya es PIL
+
+        messages = [
+            {
+                "role": "system",
+                "content": [{"type": "text", "text": system_prompt}],
+            },
+            {
+                "role": "user",
+                "content": [
+                    {"type": "text", "text": pregunta_text},
+                    {"type": "image", "image": img_pil},
+                ],
+            }
+        ]
+
+        text = processor.apply_chat_template(
+            messages,
+            tokenize=False,
+            add_generation_prompt=True
+        )
+        image_inputs, video_inputs = process_vision_info(messages)
+
+        inputs = processor(
+            text=[text],
+            images=image_inputs,
+            videos=video_inputs,
+            padding=True,
+            return_tensors="pt",
+        )
+        inputs = inputs.to(device)
+
+        with torch.no_grad():
+            generated_ids = model.generate(
+                **inputs,
+                max_new_tokens=512,
+                top_p=1.0,
+                do_sample=True,
+                temperature=0.9
+            )
+
+        trimmed = [
+            out_ids[len(in_ids):]
+            for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
+        ]
+        respuesta_text = processor.batch_decode(
+            trimmed,
+            skip_special_tokens=True,
+            clean_up_tokenization_spaces=False
+        )
+
+        return respuesta_text[0]
+
+    btn_procesar.click(fn=responder, inputs=[imagen_mostrada, pregunta], outputs=respuesta)
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

@@ -0,0 +1,7 @@
+gradio>=4.0.0
+torchvision
+torch 
+pillow 
+transformers 
+peft
+qwen_vl_utils