Add application file

app.py

@@ -1,8 +1,61 @@
 import gradio as gr
 
-def greet(name):
-    return "Hello " + name + "!!"
+# Use a pipeline as a high-level helper
+from transformers import pipeline
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+detector = pipeline("object-detection", model="hustvl/yolos-tiny")
 
+# Load model directly
+from transformers import AutoImageProcessor, AutoModelForImageClassification
+
+processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50")
+model = AutoModelForImageClassification.from_pretrained("microsoft/resnet-50")
+def food_classifier(image):
+  inputs = processor(image, return_tensors="pt")
+  logits = model(**inputs).logits
+  predicted_label = logits.argmax(-1).item()
+  label = model.config.id2label[predicted_label]
+  return [{'label': label, 'score': 0.0}]
+
+
+# Use a pipeline as a high-level helper
+from transformers import pipeline
+
+food_classifier = pipeline("image-classification", model="facebook/deit-base-distilled-patch16-384")
+
+def get_ingridients_list(image, score_threshold=.85):
+  objects = detector(image)
+  ingridients = []
+  for obj in objects:
+    cropped_image = image.crop((obj['box']['xmin'], obj['box']['ymin'], obj['box']['xmax'], obj['box']['ymax']))
+    classes = food_classifier(cropped_image)
+    best_match = max(classes, key=lambda x: x['score'])
+    if best_match['score'] > score_threshold:
+      ingridients.append(best_match['label'])
+  return list(set(ingridients))
+
+def get_ingridients(image):
+  ingridients = get_ingridients_list(image)
+  return ', '.join(ingridients)
+
+
+#text_to_text = pipeline("text-generation", model="ai-forever/mGPT")
+
+def get_reciepe(ingridients):
+    return 'dish of ' + ingridients
+
+def get_answer(image):
+  ingridients = get_ingridients(image)
+  return get_reciepe(ingridients)
+
+# Create a Gradio interface
+iface = gr.Interface(
+    fn=get_answer,  # Function to call
+    inputs=gr.Image(label="Upload an image", type="pil"),  # Input type: Image
+    outputs=gr.Markdown(label="Classification Result"),  # Output type: Markdown
+    title="Food Ingredient Classifier",
+    description="Upload an image of a food ingredient to classify it."
+)
+
+# Launch the Gradio app
+iface.launch()