examples: dataflow: chatbot: Update docs to use dataflow run single

Author: aghinsa

docs/tutorials/dataflows/chatbot.rst

@@ -22,36 +22,56 @@ messages which are directed to our bot.
     `examples/dataflow/chatbot <https://github.com/intel/dffml/blob/master/examples/dataflow/chatbot>`_
     directory of the DFFML source code.
 
-We'll write the operations for this dataflow in operations.py
+You'll need to install `aiohttp <https://github.com/aio-libs/aiohttp>`_
+and ``dffml-model-scikit`` (The model used for prediction).
+
+.. code-block:: console
+
+    $ pip install aiohttp dffml-model-scikit
+
+We'll write the operations for this dataflow in **operations.py**
+
+Adding necessary imports and defining ``Definitions`` for operation
+inputs.
+
+.. literalinclude:: /../examples/dataflow/chatbot/operations.py
+    :lines: 1-16
+
+Defining config for our operations
 
 .. literalinclude:: /../examples/dataflow/chatbot/operations.py
-    :lines: 24-51
+    :lines: 19-21
 
-All requests to Gitter's API requires the room id for our room.
+All requests to Gitter's API requires the room id of our room.
 ``get_room_id`` gets the ``room id`` from room name (The input to
 our dataflow).
 
 .. literalinclude:: /../examples/dataflow/chatbot/operations.py
-    :lines: 52-87
+    :lines: 24-49
 
 We listen to new messages directed to our bot.
 
 .. literalinclude:: /../examples/dataflow/chatbot/operations.py
-    :lines: 90-122
+    :lines: 52-86
 
 We'll use this op to send replies back to the chatroom
 
 .. literalinclude:: /../examples/dataflow/chatbot/operations.py
-    :lines: 125-220
+    :lines: 89-121
 
 This is the operation where all the logic for interpreting the messages
 go. If you have a Natural Language Understanding module It'd go here, so
 that you can parse unstructered data.
 
+.. literalinclude:: /../examples/dataflow/chatbot/operations.py
+    :lines: 124-248
+
 Our operations are ``get_room_id, stream_chat, send_message and interpret_message``.
 All of them use at least one config. The common config being INISecretConfig which
 loads secret token and bot name from the ini config file.
 
+**configs.ini**
+
 .. literalinclude:: /../examples/dataflow/chatbot/configs.ini
 
 Detour: What are imp_enter and ctx_enter?
@@ -90,19 +110,21 @@ we can do
 Running the dataflow
 --------------------
 
+**run.py**
+
 .. literalinclude:: /../examples/dataflow/chatbot/run.py
 
 set the room name, config file name and run the dataflow
 
 .. code-block:: console
 
-    python run.py
+    $ python run.py
 
 Or using the command line to, create the dataflow
 
 .. code-block:: console
 
-    dffml dataflow create \
+    $ dffml dataflow create \
         operations:get_room_id \
         operations:stream_chat \
         operations:send_message \
@@ -116,14 +138,12 @@ Or using the command line to, create the dataflow
             configs.ini=operations:send_message.secret.config.filename \
             ini=operations:interpret_message.secret.plugin \
             configs.ini=operations:interpret_message.secret.config.filename \
-    > chatbot_df.json
+        > chatbot_df.json
 
 And run it by providing the ``room_name`` as the input
 
 .. code-block:: console
 
-    dffml dataflow run records all \
+    $ dffml dataflow run single \
         -dataflow ./chatbot_df.json \
-        -inputs test_community1/community=room_name \
-        -sources m=memory \
-        -source-records temp
+        -inputs test_community1/community=room_name

docs/usage/webhook/deploy.rst

@@ -13,11 +13,11 @@ we'll setup another HTTP service which waits on GitHub webhooks to rebuilt and d
     `examples/ffmpeg <https://github.com/intel/dffml/blob/master/examples/ffmpeg/>`_
     directory of the DFFML source code.
 
-We'll be using additional plugins from dffml, ``dffml-yaml-config`` and ``dffml-http-service``.
+We'll be using additional plugins from dffml, ``dffml-config-yaml`` and ``dffml-http-service``.
 
 .. code-block:: console
 
-    $ pip install dffml-yaml-config dffml-http-service
+    $ pip install dffml-config-yaml dffml-http-service
 
 Writing the function
 --------------------

examples/dataflow/chatbot/operations.py

@@ -9,7 +9,7 @@
 from dffml.cli.cli import CLI
 from dffml import op, config, Definition, BaseSecret
 
-ACCESSTOKEN = Definition(name="access_tok3n", primitive="str")
+ACCESSTOKEN = Definition(name="access_token", primitive="str")
 ROOMNAME = Definition(name="room_name", primitive="str")
 ROOMID = Definition(name="room_id", primitive="str")
 MESSAGE = Definition(name="message", primitive="str")
@@ -78,7 +78,6 @@ async def stream_chat(self, room_id):
             # Gitter sends " \n" at some intervals
             if data == " \n".encode():
                 continue
-            print(f"\n\n Got data {data} \n\n")
             data = json.loads(data.strip())
             message = data["text"]
             # Only listen to messages directed to bot
@@ -136,9 +135,45 @@ async def interpret_message(self, message):
             return {"message": "Hey Hooman ฅ^•ﻌ•^ฅ"}
 
     def extract_data(raw_data):
-        raw_data = raw_data.split("data:")
+        """
+        Parses data from text
+
+        eg
+            >>> raw_data = "
+                            details:
+                            features: Years:int:1 Expertise:int:1 Trust:float:1
+                            predict: Salary:float:1
+                            data:
+                            Years,Expertise,Trust,Salary
+                            0,1,0.1,10
+                            1,3,0.2,20
+                            2,5,0.3,30
+                            3,7,0.4,40
+                            "
+
+            >>> extract_data(raw_data)
+                {
+                    model-data:
+                        "
+                            Years,Expertise,Trust,Salary
+                            0,1,0.1,10
+                            1,3,0.2,20
+                            2,5,0.3,30
+                            3,7,0.4,40
+                        "
+                    ,
+                    features:
+                        Years:int:1 Expertise:int:1 Trust:float:1
+                    ,
+                    predict: Salary:float:1
+                }
+        """
+        raw_data = raw_data.split("data:")  # (Feature details, training data)
         data = {"model-data": raw_data[1]}
-        raw_data = raw_data[0].split("\n")
+        raw_data = raw_data[0].split(
+            "\n"
+        )  # splits feautre details to seprate lines
+        # Iterate and add to to dictionary `data`
         for x in raw_data:
             k, *v = x.split(":")
             if isinstance(v, list):  # for features
@@ -157,7 +192,7 @@ def extract_data(raw_data):
     message = re.sub(r"(@[^\s]+)(.*)", r"\2", message).strip()
 
     if message.lower().startswith("train model"):
-        return {"message": "Gimme more details!!"}
+        return {"message": "Gimme more info!!"}
 
     elif message.lower().startswith("predict:"):
         # Only replace first occurence of predict
@@ -172,15 +207,9 @@ def extract_data(raw_data):
     else:
         return {"message": " Oops ,I didnt get that ᕙ(⇀‸↼‶)ᕗ "}
 
-    # If predict or train, extract data
+    # We'll use scikit logistic regression
     data = extract_data(raw_data)
-    if "model-type" in data:
-        model_type = data["model-type"]
-    if "model-name" in data:
-        model_name = data["model-name"]
-    else:
-        model_name = "mymodel"
-
+    model_type = "scikitlr"
     features = data["features"].split(" ")
     predict = data["predict"]
     model_data = data["model-data"]
@@ -196,7 +225,7 @@ def extract_data(raw_data):
                 "-model",
                 model_type,
                 "-model-directory",
-                model_name,
+                "tempModel",
                 "-model-features",
                 *features,
                 "-model-predict",
@@ -206,7 +235,6 @@ def extract_data(raw_data):
                 "-source-filename",
                 fileobj.name,
             )
-            sys.stdout.flush()
 
     if "train" in cmds:
         return {"message": "Done!!"}