Merge pull request #385 from BePeace/main

Day 2 work using py for ollama

week1/community-contributions/ag-w1d2-ollama-site-summary

@@ -0,0 +1,55 @@
+import ollama
+import requests
+from bs4 import BeautifulSoup
+from IPython.display import Markdown, display
+
+MODEL = "llama3.2"
+
+#headers and class for website to summarize
+headers = {
+ "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36"
+}
+class Website:
+    def __init__(self, url):
+        self.url = url
+        response = requests.get(url, headers=headers)
+        soup = BeautifulSoup(response.content, 'html.parser')
+        self.title = soup.title.string if soup.title else "No title found"
+        for irrelevant in soup.body(["script", "style", "img", "input"]):
+            irrelevant.decompose()
+        self.text = soup.body.get_text(separator="\n", strip=True)
+        
+#define prompts
+system_prompt = "You are an assistant that analyzes the contents of a website \
+and provides a short summary, ignoring text that might be navigation related. \
+Respond in markdown."
+
+def user_prompt_for(website):
+    user_prompt = f"You are looking at a website titled {website.title}"
+    user_prompt += "\nThe content of this website is as follows; \
+please provide a short summary of this website in markdown. \
+If it includes news or announcements, then summarize these too.\n\n"
+    user_prompt += website.text
+    return user_prompt
+
+#prepare message for use in OpenAI call
+def messages_for(website):
+    return [
+        {"role": "system", "content": system_prompt},
+        {"role": "user", "content": user_prompt_for(website)}
+    ]
+
+#define function to summarize a given website
+def summarize(url):
+    website = Website(url)
+    response = ollama.chat(model=MODEL, messages=messages_for(website))
+    return response['message']['content']
+
+#function to display summary in markdown format
+def display_summary(url):
+    summary = summarize(url)
+    display(Markdown(summary))
+    print(summary)
+
+url = "https://edwarddonner.com"
+display_summary(url)

week1/community-contributions/ag-w1d2-use-local-ollama-library.py

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+import ollama
+from IPython.display import Markdown, display
+
+MODEL = "llama3.2"
+
+# Create a messages list (Note that "system" role is not required)
+messages = [
+    { "role": "user", "content": "Describe some of the business applications of Generative AI"}
+]
+
+""" 
+#under the covers calls this API with specified payload
+
+OLLAMA_API = "http://local_host:11434/api/chat" 
+payload = {
+    "model": MODEL,
+    "messages": messages,
+    "stream": False
+}
+response = requests.post(OLLAMA_API, json=payload, headers=HEADERS)
+
+"""
+response = ollama.chat(model=MODEL, messages=messages)
+#print(response['message']['content'])
+answer = response['message']['content']
+
+#Note that markdown will not display in VSCode but only in Jupyter
+#to view in markdown in VSCode, save output to .md file and then oipen in VSCode
+display(Markdown(answer))
+print(answer)
+
+

week1/community-contributions/ag-w1d2-use-local-ollama-url-py

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+import ollama
+import requests
+from IPython.display import Markdown, display
+
+OLLAMA_API = "http://localhost:11434/api/chat"
+HEADERS = {"Content-Type": "application/json"}
+MODEL = "llama3.2"
+
+# Create a messages list (Note that "system" role is not required)
+messages = [
+    { "role": "user", "content": "Describe some of the business applications of Generative AI"}
+]
+
+payload = {
+    "model": MODEL,
+    "messages": messages,
+    "stream": False
+}
+
+response = requests.post(OLLAMA_API, json=payload, headers=HEADERS)
+print(response.json()['message']['content'])
+

week1/community-contributions/ag-w1d2-use-local-ollama-with-openai.py

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+from openai import OpenAI
+
+MODEL = "llama3.2"
+
+messages = [
+    { "role": "user", "content": "Describe some of the business applications of Generative AI"}
+]
+
+# The python class OpenAI is simply code written by OpenAI engineers that
+# makes calls over the internet to an endpoint.
+ollama_via_openai = OpenAI(base_url='http://localhost:11434/v1', api_key='ollama')
+
+# When we call openai.chat.completions.create(), this python code just makes 
+# a web request to: "https://api.openai.com/v1/chat/completions"
+# Code like this is known as a "client library" - it's just wrapper code that 
+# runs on your machine to make web requests. The actual power of GPT is running 
+# on OpenAI's cloud behind this API, not on your computer
+response = ollama_via_openai.chat.completions.create(
+    model=MODEL,
+    messages=messages
+)
+
+print(response.choices[0].message.content)