Implemented follow

tootapalooza/cli.py

@@ -182,6 +182,21 @@ class Tooter(Mastodon):
     def follow_random_local(self) -> int:
         if( args.debug ):
             print(f"{self.name} follow_random_local")
+
+        acctdict = self.me()
+        followlist = self.account_following(id=acctdict.id)
+        followed_people = {account.acct for account in followlist}
+
+        # This does outersection on sets. It's the set of all users we know about
+        # (from the users.toml file) minus ourselves and anyone we already follow
+        potentials = set(self.credentials) ^ {self.name} ^ followed_people
+        follow_target = random.choice(list(potentials))
+        target_dict = self.account_lookup(follow_target)
+        if( args.dry_run ):
+            print(f"{self.name} will follow {follow_target} (id: {target_dict.id})")
+        else:
+            self.account_follow(target_dict.id)
+
         return( 0 )
     
     def unfollow_random(self) -> int:
@@ -217,19 +232,19 @@ class Tooter(Mastodon):
     def random_interaction(self):
         """Choose one possible interaction according to the weights, and do it."""
         interactions = {
-            self.reply_random_local:     4,
+            self.reply_random_local:     0,
             self.reply_random_home:      0,
-            self.reply_random_public: 0,
+            self.reply_random_public:    0,
             self.follow_random_local:    1,
-            self.unfollow_random:        1,
-            self.toot_plain_public:      1,
-            self.toot_tagged_public:     1,
-            self.toot_plain_unlisted:    1,
-            self.boost_random_local:     1,
-            self.favourite_random_local: 1,
-            self.favourite:              1,
-            self.federated_favourite:    1,
-            self.report_random_local:    1
+            self.unfollow_random:        0,
+            self.toot_plain_public:      0,
+            self.toot_tagged_public:     0,
+            self.toot_plain_unlisted:    0,
+            self.boost_random_local:     0,
+            self.favourite_random_local: 0,
+            self.favourite:              0,
+            self.federated_favourite:    0,
+            self.report_random_local:    0
         }
         chosen = random.choices(population=list(interactions.keys()), 
             weights=list(interactions.values()))[0]