longer description

pages/1_⭐️_Info.py

@@ -7,9 +7,43 @@ st.markdown("""
 # Better Image Retrieval With Retrieval-Augmented CLIP 🧠
 
 
-CLIP is a neural network trained on image-text pairs that can predict how semantically close images are with some text
-But, although CLIP understands what it sees, it doesn't know its properties. While other models can understand text that contains such information, like Wikipedia.
+CLIP is a neural network that can predict how semantically close images and text pairs are. 
+In simpler terms, it can tell that the string "Cat" is closer to images of cats rather than images of dogs.
 
-In this demo application, we see if we can 'help' CLIP with another model.""")
+What makes CLIP so powerful is that is a zero-shot model: that means that it can generalize concepts,
+understand text and images it has never seen before. For example, it can tell that the string "an animal with yellow eyes"
+is closer to images of cats rather than dogs, even though such pair was not in its training data.
 
-st.image("diagram.png")
+Why does this matter? Because zero shot capabilities allow models to understand descriptions. And in fact
+CLIP understands that "an animal with pink feathers" matches a flamingo better than a pig.
+
+However, these descriptions need to be related to what the image shows. CLIP knows nothing about the animal features, 
+history and cultural references: It doesn't know which animals live longer than others, that jaguars were often depicted 
+in Aztec wall paintings, or that wolves and bears are typical animals that show up in European fairy tales. It doesn't even 
+know that cheetas are fast, because it cannot tell it from the image.
+
+However, Wikipedia contains all this information, and more. Can we make CLIP "look up" the answer to
+our questions on Wikipedia before looking for matches?
+
+In this demo application, we see how can we combine traditional Extractive QA on Wikipedia and CLIP with Haystack.""")
+
+st.image("diagram.png")
+
+st.markdown("""
+In the image above you can see how the process looks like.
+
+First, we download a slice of Wikipedia with information about all the animals in the Lisbon zoo and preprocess,
+index, embed and store them.
+
+At this point they are ready to be queried by the text Retriever, which compares the user's question ("The fastest animal")
+to all the documents indexed earlier and returns the documents which are more likely to contain an answer to the question.
+In this case, it will probably return snippets from the Cheetah Wikipedia entry.
+
+Once the documents are found, they are handed over to the Reader, a model that is able to locate precisely the answer to a
+question into a document. These answers are strings that should be now very easy for CLIP to understand, such as the name of an animal.
+In this case, the Reader will return answers such as "Cheetah", "the cheetah", etc.
+
+These strings are then ranked and the most likely one is sent over to CLIP, which will use its own document store of images
+to find all the pictures that match the string. Cheetah are present in the Lisbon zoo, so it will find pictures of them and
+return them.
+""")