fix dockerifle

Dockerfile

@@ -7,7 +7,7 @@ ENV PYTHONUNBUFFERED 1
 
 ADD src/ /blog_creator
 
-RUN apt-get update && apt-get install -y rustc cargo python-is-python3 pip python3-venv libmagic-dev 
+RUN apt-get update && apt-get install -y rustc cargo python-is-python3 pip python3-venv libmagic-dev git 
 
 RUN python -m venv /opt/venv
 ENV PATH="/opt/venv/bin:$PATH"

generated_files/when_to_use_ai.md

@@ -1,53 +0,0 @@
-# When Should You Use AI?
-
-Right off the bat? Well, let’s talk about when *not* using an LLM is actually pretty much like trying to build that perfect pavlova with a robot: Sure, they might have all these instructions and ingredients laid out for them (or so it seems), but can you really trust this machine to understand those subtle nuances of temperature or timing? No. And let’s be real here – if we’re talking about tasks requiring precise logic like financial calculations or scientific modeling - well, that sounds more suited to the human brain.
-
-But where does AI actually shine bright and come in handy?
-
-* **Pattern Recognition:** Spotting trends within data is one of those areas LLMs are pretty darn good at. Whether it’s identifying patterns across a dataset for insights (or even generating creative ideas based on existing information), they can do that with speed, efficiency - not to mention accuracy.
-
-**And when shouldn’t you use AI?**
-
-* **Tasks Requiring Precise Logic:** If your job is something needing absolute precision – like crunching numbers or modeling scientific data where a miscalculation could mean millions in losses for the company. Well… maybe hold off on letting an LLM take over.
-* **Situations Demanding Critical Thinking**: Let’s be honest, if you need to make judgment calls based upon complex factors that even humans can struggle with – then it might not just do a good job; but rather fall short.
-
-LMLs are great at mimicking intelligence. But they don’t actually understand things the way we human beings (or I should say: non-humans) comprehend them.
-* **Processes Where Errors Have Serious Consequences:** If your work involves tasks where errors can have serious consequences, then you probably want to keep it in human hands.
-
-**The Bottom Line**
-
-AI is a powerful tool. But like any good chef knows – even the best kitchen appliances can't replace their own skills and experience when making that perfect pavlova (or for us humans: delivering results). It’s about finding balance between leveraging AI capabilities, while also relying on our critical thinking - and human intuition.
-
-Don’t get me wrong here; I’m not anti-AI. But let’s be sensible – use it where it's truly helpful but don't forget to keep those tasks in the hands of your fellow humans (or at least their non-humans).
-
----
-
-**Note for Editors:** This draft is designed with ease-of-editing and clarity as a priority, so feel free to adjust any sections that might need further refinement or expansion. I aimed this piece towards an audience who appreciates both humor-infused insights into the world of AI – while also acknowledging its limitations in certain scenarios.
-
-```markdown
-# When Should You Use AI?
-
-Right off the bat? Well, let’s talk about when *not* using LLM is actually pretty much like trying to build that perfect pavlova with a robot: Sure, they might have all these instructions and ingredients laid out for them (or so it seems), but can you really trust this machine to understand those subtle nuances of temperature or timing? No. And let’s be real here – if we’re talking about tasks requiring precise logic like financial calculations or scientific modeling - well, that sounds more suited to the human brain.
-
-But where does AI actually shine bright and come in handy?
-
-* **Pattern Recognition:** Spotting trends within data is one of those areas LLMs are pretty darn good at. Whether it’s identifying patterns across a dataset for insights (or even generating creative ideas based on existing information), they can do that with speed, efficiency - not to mention accuracy.
-
-**And when shouldn’t you use AI?**
-
-* **Tasks Requiring Precise Logic:** If your job is something needing absolute precision – like crunching numbers or modeling scientific data where a miscalculation could mean millions in losses for the company. Well… maybe hold off on letting an LLM take over.
-* **Situations Demanding Critical Thinking**: Let’s be honest, if you need to make judgment calls based upon complex factors that even humans can struggle with – then it might not just do a good job; but rather fall short.
-
-LMLs are great at mimicking intelligence. But they don’t actually understand things the way we human beings (or I should say: non-humans) comprehend them.
-* **Processes Where Errors Have Serious Consequences:** If your work involves tasks where errors can have serious consequences, then you probably want to keep it in human hands.
-
-**The Bottom Line**
-
-AI is a powerful tool. But like any good chef knows – even the best kitchen appliances can't replace their own skills and experience when making that perfect pavlova (or for us humans: delivering results). It’s about finding balance between leveraging AI capabilities, while also relying on our critical thinking - and human intuition.
-
-Don’t get me wrong here; I’m not anti-AI. But let’s be sensible – use it where it's truly helpful but don't forget to keep those tasks in the hands of your fellow humans (or at least their non-humans).
-
----
-
-**Note for Editors:** This draft is designed with ease-of-editing and clarity as a priority, so feel free to adjust any sections that might need further refinement or expansion. I aimed this piece towards an audience who appreciates both humor-infused insights into the world of AI – while also acknowledging its limitations in certain scenarios.
-```

src/ai_generators/ollama_md_generator.py

@@ -10,7 +10,7 @@ class OllamaGenerator:
         self.inner_title = inner_title
         self.content = content
         self.response = None
-        self.chroma = chromadb.HttpClient(host="172.18.0.2", port=8000)
+        self.chroma = chromadb.HttpClient(host="172.19.0.2", port=8000)
         ollama_url = f"{os.environ["OLLAMA_PROTOCOL"]}://{os.environ["OLLAMA_HOST"]}:{os.environ["OLLAMA_PORT"]}"
         self.ollama_client = Client(host=ollama_url)
         self.ollama_model = os.environ["EDITOR_MODEL"]
@@ -150,3 +150,10 @@ class OllamaGenerator:
     def save_to_file(self, filename: str) -> None:
         with open(filename, "w") as f:
             f.write(self.generate_markdown())
+
+    def generate_commit_message(self):
+        prompt_system = "You are a blog creator commiting a piece of content to a central git repo"
+        prompt_human = f"Generate a 10 word git commit message describing {self.response}"
+        messages = [("system", prompt_system), ("human", prompt_human),]
+        commit_message = self.llm.invoke(messages).text()
+        return commit_message

src/main.py

@@ -1,6 +1,7 @@
 import ai_generators.ollama_md_generator as omg
 import trilium.notes as tn
-import string
+import repo_management.repo_manager as git_repo
+import string,os
 
 tril = tn.TrilumNotes()
 
@@ -23,4 +24,11 @@ for note in tril_notes:
     ai_gen = omg.OllamaGenerator(os_friendly_title,
                                  tril_notes[note]['content'],
                                  tril_notes[note]['title'])
-    ai_gen.save_to_file(f"/blog_creator/generated_files/{os_friendly_title}.md")
+    blog_path = f"/blog_creator/generated_files/{os_friendly_title}.md"
+    ai_gen.save_to_file(blog_path)
+    # Generate commit messages and push to repo
+    commit_message = ai_gen.generate_commit_message()
+    git_user = os.environp["GIT_USER"]
+    git_pass = os.environ["GIT_PASS"]
+    repo_manager = git_repo("blog/", git_user, git_pass)
+    repo_manager.create_copy_commit_push(blog_path, os_friendly_title, commit_message)

src/repo_management/repo_manager.py

@@ -14,7 +14,7 @@ class GitRepository:
 
         if os.path.exists(repo_path):
             shutil.rmtree(repo_path)
-
+        self.repo_path = repo_path
         Repo.clone_from(remote, repo_path)
         self.repo = Repo(repo_path)
         self.username = username
@@ -50,3 +50,42 @@ class GitRepository:
     def get_branches(self):
         """List all branches in the repository"""
         return [branch.name for branch in self.repo.branches]
+    
+    
+    def create_branch(self, branch_name, remote_name='origin', ref_name='main'):
+        """Create a new branch in the repository with authentication."""
+        try:
+            # Use the same remote and ref as before
+            self.repo.git.branch(branch_name, commit=True)
+            return True
+        except GitCommandError as e:
+            print(f"Failed to create branch: {e}")
+            return False
+        
+    def add_and_commit(self, message=None):
+        """Add and commit changes to the repository."""
+        try:
+            # Add all changes
+            self.repo.git.add(all=True)
+            # Commit with the provided message or a default
+            if message is None:
+                commit_message = "Added and committed new content"
+            else:
+                commit_message = message
+            self.repo.git.commit(commit_message=commit_message)
+            return True
+        except GitCommandError as e:
+            print(f"Commit failed: {e}")
+            return False
+        
+    def create_copy_commit_push(self, file_path, title, commit_messge):
+        self.create_branch(title)
+
+        shutil.copy(f"{file_path}", f"{self.repo_path}src/content/")
+
+        self.add_and_commit(commit_messge)
+
+        self.repo.git.push(remote_name='origin', ref_name=title, force=True)
+
+    def remove_repo(self):
+        shutil.rmtree(self.repo_path)