feat: examples added

app.py

@@ -8,19 +8,38 @@ from flamingo_mini_task import FlamingoModel, FlamingoProcessor
 from datasets import load_dataset,concatenate_datasets
 from PIL import Image
 
+EXAMPLES_DIR = 'examples'
+DEFAULT_PROMPT = "<image>"
+MINI_MODEL = "flamingo-mini-bilbaocaptions-scienceQA[QA]"
+TINY_MODEL = "flamingo-tiny-scienceQA[COT+QA]"
+MEGATINY_MODEL = "flamingo-megatiny-opt-scienceQA[QA]"
 
 flamingo_megatiny_captioning_models = {
-    'flamingo-tiny-scienceQA[COT+QA]': {
+    MINI_MODEL: {
         'model': FlamingoModel.from_pretrained('TheMrguiller/Flamingo-tiny_ScienceQA_COT-QA'),
     },
-    'flamingo-mini-bilbaocaptions-scienceQA[QA]': {
+    TINY_MODEL: {
         'model': FlamingoModel.from_pretrained('TheMrguiller/Flamingo-mini-Bilbao_Captions-task_BilbaoQA-ScienceQA'),
     },
-    'flamingo-megatiny-opt-scienceQA[QA]':{
+    MEGATINY_MODEL:{
         'model': FlamingoModel.from_pretrained('landersanmi/flamingo-megatiny-opt-QA')
     },
 }
 
+# setup some example images
+examples = []
+path = EXAMPLES_DIR + "/{}"
+cot = False
+
+examples.append([path.format("koala.png"), "What animal is this?", "Koala", "Elephant", "Cat", "Mouse", cot, MEGATINY_MODEL])
+examples.append([path.format("townhall.jpg"), "What building is this?", "Guggenheim museum", "San mames stadium", "Alhondiga", "Bilbao townhall", cot, TINY_MODEL])
+examples.append([path.format("muniain.jpeg"), "What team is IKer Muniain associated?", "Real Madrid", "Manchester United", "Athletic Bilbao", "Rayo Vallecano", cot, TINY_MODEL])
+examples.append([path.format("lasalve.jpeg"), "What is the name of this bridge?", "La Salve", "Zubizuri", "La Ribera", "San Anton", cot, TINY_MODEL])
+examples.append([path.format("athl.jpeg"), "Football fans hold flags with what team colors?", "Athletic", "Besiktas", "Udinese", "Real Madrid", cot, TINY_MODEL])
+
+#examples.append([path, cot, DEFAULT_PROMPT, DEFAULT_MODEL])
+#examples.append([path, cot, DEFAULT_PROMPT, DEFAULT_MODEL])
+
 
 def generate_text(image, question, option_a, option_b, option_c, option_d, cot_checkbox, model_name):
     model = flamingo_megatiny_captioning_models[model_name]['model']
@@ -44,17 +63,17 @@ def generate_text(image, question, option_a, option_b, option_c, option_d, cot_c
                                          prompt = prompt,
                                         )
 
-    return prediction[0]
+    return prediction[0].split('[ANSWER]')[1]
 
 
 
 
-image_input = gr.Image(value="giraffes.jpg")
-question_input = gr.inputs.Textbox(default="Which animal is this?")
+image_input = gr.Image(path.format("giraffe.jpeg"))
+question_input = gr.inputs.Textbox(default="What animal is this?")
 opt_a_input = gr.inputs.Textbox(default="Dog")
-opt_b_input = gr.inputs.Textbox(default="Cat")
-opt_c_input = gr.inputs.Textbox(default="Giraffe")
-opt_d_input = gr.inputs.Textbox(default="Horse")
+opt_b_input = gr.inputs.Textbox(default="Giraffe")
+opt_c_input = gr.inputs.Textbox(default="Elephant")
+opt_d_input = gr.inputs.Textbox(default="Cocodrile")
 cot_checkbox = gr.inputs.Checkbox(label="Generate COT")
 select_model = gr.inputs.Dropdown(choices=list(flamingo_megatiny_captioning_models.keys()))
 
@@ -72,6 +91,7 @@ gr.Interface(
             cot_checkbox,
             select_model
            ],
+    examples=examples,
     outputs=text_output,
     title='Generate answers from MCQ',
     description='Generate answers from Multiple Choice Questions or generate a Chain Of Though about the question and the options given',