wip reddit agent

rnd/examples/reddit-marketing/poetry.lock

@@ -495,6 +495,20 @@ tomli = {version = ">=1", markers = "python_version < \"3.11\""}
 [package.extras]
 dev = ["argcomplete", "attrs (>=19.2)", "hypothesis (>=3.56)", "mock", "pygments (>=2.7.2)", "requests", "setuptools", "xmlschema"]
 
+[[package]]
+name = "python-dotenv"
+version = "1.0.1"
+description = "Read key-value pairs from a .env file and set them as environment variables"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "python-dotenv-1.0.1.tar.gz", hash = "sha256:e324ee90a023d808f1959c46bcbc04446a10ced277783dc6ee09987c37ec10ca"},
+    {file = "python_dotenv-1.0.1-py3-none-any.whl", hash = "sha256:f7b63ef50f1b690dddf550d03497b66d609393b40b564ed0d674909a68ebf16a"},
+]
+
+[package.extras]
+cli = ["click (>=5.0)"]
+
 [[package]]
 name = "requests"
 version = "2.32.3"
@@ -624,4 +638,4 @@ test = ["websockets"]
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.10"
-content-hash = "ffe49151cde677818965792e005fb64f97a64ec1fc8d5c00289a1491abde338b"
+content-hash = "b74b2eaeb00ad5ae8c005e7137db3cbca43d8f08ba8d8d5d918ad55b6e28058c"

rnd/examples/reddit-marketing/pyproject.toml

@@ -12,6 +12,7 @@ openai = "^1.34.0"
 pydantic = "^2.7.4"
 praw = "^7.7.1"
 click = "^8.1.7"
+python-dotenv = "^1.0.1"
 
 [tool.poetry.group.dev.dependencies]
 pytest = "^8.2.2"

rnd/examples/reddit-marketing/redagent/__main__.py

@@ -1,7 +1,21 @@
+import os
 import click
+from httpx import post
+import pydantic
 from openai import OpenAI
 import praw
 import datetime
+from dotenv import load_dotenv
+
+load_dotenv()
+
+
+class EvalPost(pydantic.BaseModel):
+    is_of_value: bool
+
+
+class Comment(pydantic.BaseModel):
+    message: str
 
 
 @click.group()
@@ -9,42 +23,57 @@ def main():
     """RedAgent- A reddit marketing agent"""
 
 
+@main.command()
+def test():
+    reddit = praw.Reddit(
+        client_id=os.environ.get("CLIENT_ID"),
+        client_secret=os.environ.get("CLIENT_SECRET"),
+        user_agent="AutoGPT Test Script",
+        username=os.environ.get("USERNAME"),
+        password=os.environ.get("PASSWORD"),
+    )
+    print(reddit.user.me())
+    print(reddit.subreddit("AutoGPT").title)
+
+
 @main.command()
 def start():
     client = OpenAI()
 
     reddit = praw.Reddit(
-        client_id="",
-        client_secret="",
-        user_agent="",
+        client_id=os.environ.get("CLIENT_ID"),
+        client_secret=os.environ.get("CLIENT_SECRET"),
+        user_agent="AutoGPT Test Script",
+        username=os.environ.get("USERNAME"),
+        password=os.environ.get("PASSWORD"),
     )
-    product = "Something cool"
-    subreddits = ["a", "b"]
+    product = "AutoGPT Agent Server to run your own private agent locally. It can do thigns like personal writing editing"
+    subreddits = ["LocalLlama"]
     for sub in subreddits:
         posts = get_recent_posts(sub, reddit)
         for post in posts:
             if is_relevant(post, product, client):
+                print(f"[Y] {post.title}")
                 post_marketing_message(post, product, client)
+            else:
+                print(f"[N] {post.title}")
 
 
 def get_recent_posts(subreddit, reddit, time_period=3):
     # Calculate the time three hours ago
-    three_hours_ago = datetime.datetime.now(datetime.UTC) - datetime.timedelta(
-        hours=time_period
-    )
+    three_hours_ago = datetime.datetime.utcnow() - datetime.timedelta(hours=time_period)
 
     sub = reddit.subreddit(subreddit)
     posts = []
     # Get submissions from the subreddit
     for submission in sub.new(limit=None):
-        post_time = datetime.datetime.fromtimestamp(
-            submission.created_utc, datetime.UTC
-        )
+        post_time = datetime.datetime.utcfromtimestamp(submission.created_utc)
         if post_time > three_hours_ago:
             posts.append(submission)
             print(f"Title: {submission.title}, Time: {post_time}")
         else:
             break  # Stop checking if the post is older than 3 hours
+    print(f"Found {len(posts)} posts matching your criteria")
     return posts
 
 
@@ -60,27 +89,31 @@ def is_relevant(post, product, client: OpenAI) -> bool:
     {json_format}
     """
     task_message = f"""Based on the following post, would posting a reply about your product add value to the discussion? 
-    {post}
+    Title: {post.title}
+    Content: {post.selftext}
     """
 
     msgs = [
         {"role": "system", "content": system_mesage},
         {"role": "user", "content": task_message},
     ]
-    ans = client.chat.completions.create(
-        model="gpt-4-turbo", messages=msgs, response_format="json"
+    response = client.chat.completions.create(
+        model="gpt-4-turbo", messages=msgs, response_format={"type": "json_object"}
+    )
+    ans = EvalPost.model_validate_json(
+        response.choices[0].message.content, strict=False
     )
 
-    if ans["is_of_value"]:
+    if ans.is_of_value:
         return True
     else:
         return False
 
 
 def post_marketing_message(post, product, client):
-    json_format = """{"is_of_value": bool}"""
+    json_format = """{"message": str}"""
     system_mesage = f"""
-    You are an expert at gurellia marketing and have been tasked with picking reddit posts that are relevant to your product.
+    You are an expert at gurellia marketing and have been tasked with writing a reddit comment subtelly promoting your product by telling how you had a simliar problem and used the product your promoting to help solver the problem. Make sure to explain how the product helped. 
 
     The product you are marketing is:
     {product}
@@ -88,19 +121,22 @@ def post_marketing_message(post, product, client):
     Reply in json format like so 
     {json_format}
     """
-    task_message = f"""Based on the following post, would posting a reply about your product add value to the discussion? 
-    {post}
+    task_message = f"""Based on the following post, write your gurilla marketing comment
+    Title: {post.title}
+    Content: {post.selftext}
     """
 
     msgs = [
         {"role": "system", "content": system_mesage},
         {"role": "user", "content": task_message},
     ]
-    ans = client.chat.completions.create(
-        model="gpt-4-turbo", messages=msgs, response_format="json"
-    )
 
-    reply = post.repy(ans["message"])
+    response = client.chat.completions.create(
+        model="gpt-4-turbo", messages=msgs, response_format={"type": "json_object"}
+    )
+    reply = Comment.model_validate_json(
+        response.choices[0].message.content, strict=False
+    )
 
     print(f"Replied to post: {reply}")