First demo with origami tutorial.

docs/README.md

@@ -1,3 +1,4 @@
 # Orimami
 
 * [Getting Started](/docs/Getting-Started.md)
+* [First Demo](/docs/Your-First-Demo.md)

docs/Your-First-Demo.md

@@ -0,0 +1,89 @@
+# First Demo
+
+Since now you know what origami is and how it works its time to convert an actual machine learning model to origami wrapped
+model demo. For the sake of this document we will focus on Visual Dialog demo. https://github.com/batra-mlp-lab/visdial-rl
+
+* You can find the actual source of origami wrapped visual dialog demo [here](https://github.com/fristonio/visdial-rl/tree/origami-wrapper).
+* The actual demo code for the visual dialog demo is located [here](https://github.com/fristonio/visdial-rl/blob/origami-wrapper/temp_main.py)
+
+The main part of the demo in which we are interested is when demos takes in the user input. The demo we have is a command line interface demo
+so the input to the demo comes from standard input.
+
+```python
+def main():
+    DIALOG = []
+    visdial_model = VisualDialog()
+
+    while (True):
+        CAPTION = "a cat sitting on top of a refrigerator"
+        QUESTION = input("Enter the question: ")
+        IMG_PATH = "demo/img.jpg"
+        if QUESTION == "EXIT":
+            break
+        dialog = visdial_model.predict(QUESTION, IMG_PATH, CAPTION, DIALOG)
+        DIALOG = dialog
+        print(dialog)
+
+```
+
+This method first initializes the Visual Dialog demo using `VisualDialog` class, then it infinitely waits for user input from the command line.
+For the sake of this demo we have hardcoded the image and the caption, so we only require question from the user. Then corresponding to each question the
+code predicts the answer using the model we initialized earlier. The response from the prediction is then printed to stdout.
+
+Let's go ahead and wrap this with origami-lib step by step.
+
+* First we need to import the lib and initialize the application.
+
+```python
+from origami_lib.origami import Origami
+
+app = Origami("VisualDialogue", cache_path="cache_visdial")
+visdial_model = VisualDialog()
+```
+
+* Implement a method with `@app.listen` decorator which will be called each time a request is made to access the demo.
+
+```python
+@app.listen()
+def visdial():
+    # caption = app.get_text_array()[0]
+    # image = app.get_image_array()[0]
+
+    CAPTION = "a cat sitting on top of a refrigerator"
+    IMG_PATH = "demo/img.jpg"
+	dialog = []
+```
+
+Again for the sake of this example we have hardcoded the image and caption. We can have used input from the user the uncommenting the commented lines.
+
+* Now for each connection we get on our demo we need to get answer from the same combination of caption and image. So we register a function to be called
+each time when a question is asked for the image. This way we can write some preprocessing code for the image in `visdial` function without having to perform
+the preprocessing for every question.
+
+```python
+def predict(image_path, caption, dialog, message=""):
+    new_dial = visdial_model.predict(message, image_path, caption, dialog)
+    dialog.append(new_dial[-1])
+    print("Current dialog object is : \n", dialog)
+    return dialog[-1]["answer"]
+
+
+@app.listen()
+def visdial():
+    # caption = app.get_text_array()[0]
+    # image = app.get_image_array()[0]
+
+    CAPTION = "a cat sitting on top of a refrigerator"
+    IMG_PATH = "demo/img.jpg"
+    dialog = []
+
+    # Register predict function
+    app.register_persistent_connection(predict, [IMG_PATH, CAPTION, dialog])
+```
+
+In the above code predict function is called each time a question is asked, where the question corresponds to message argument.
+The variable dialog contains the entire history of the conversation. So we can also return that to user, but for this example we are only
+returning the current question prediction.
+
+After defining all the functions and classes we run the app by simply calling `run` on app. And thats it, our demo is ready to be used with
+Origami frontend. So with a minimal set of changes to the actual demo code we have our demo ready.