initial test commit

.gitignore

@@ -0,0 +1 @@
+.env

ai.py

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+import warnings
+import os # built in python library with operating system functions
+from dotenv import load_dotenv, find_dotenv
+import json
+
+import openai # Set of functions provided by openai to interact with their models
+from langchain.chat_models import ChatOpenAI
+from langchain.prompts.chat import (
+    ChatPromptTemplate,
+    SystemMessagePromptTemplate,
+    AIMessagePromptTemplate,
+    HumanMessagePromptTemplate,
+)
+from langchain.schema import AIMessage, HumanMessage, SystemMessage
+
+warnings.filterwarnings('ignore')
+_ = load_dotenv(find_dotenv())
+openai.api_key  = os.getenv('OPENAI_API_KEY')
+
+def get_completion(prompt, model="gpt-3.5-turbo"):
+    messages = [{"role": "user", "content": prompt}]
+    response = openai.ChatCompletion.create(
+        model=model,
+        messages=messages,
+        temperature=0, # this is the degree of randomness of the model's output
+    )
+    return response.choices[0].message["content"]
+
+def get_completion_from_messages(messages, model="gpt-3.5-turbo", temperature=0):
+    response = openai.ChatCompletion.create(
+        model=model,
+        messages=messages,
+        temperature=temperature, # this is the degree of randomness of the model's output
+    )
+#     print(str(response.choices[0].message))
+    return response.choices[0].message["content"]
+
+def collect_messages(_):
+    prompt = inp.value_input
+    inp.value = ''
+    context.append({'role':'user', 'content':f"{prompt}"})
+    response = get_completion_from_messages(context) 
+    context.append({'role':'assistant', 'content':f"{response}"})
+    panels.append(
+        pn.Row('User:', pn.pane.Markdown(prompt, width=600)))
+    panels.append(
+        pn.Row('Assistant:', pn.pane.Markdown(response, width=600, style={'background-color': '#F6F6F6'})))
+ 
+    return pn.Column(*panels)
+
+
+persona = "LLM_1"
+
+pn.extension('floatpanel')
+panels = [] # collect display 
+
+context = [ {'role':'system', 'content': sys_prompt.get_prompt(persona)} ]  # accumulate messages
+
+inp = pn.widgets.TextInput(value="Hi", placeholder='Enter text here…')
+button_conversation = pn.widgets.Button(name="Chat!")
+
+interactive_conversation = pn.bind(collect_messages, button_conversation)
+
+dashboard = pn.Column(
+    inp,
+    pn.Row(button_conversation),
+    pn.panel(interactive_conversation, loading_indicator=True, height=300),
+)
+
+dashboard

app.py

@@ -7,6 +7,8 @@ import panel as pn
 from PIL import Image
 from transformers import CLIPModel, CLIPProcessor
 
+import ai
+
 pn.extension(design="bootstrap", sizing_mode="stretch_width")
 
 ICON_URLS = {

logging.py

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+#@title JSON log system prompt management
+
+class LLMPrompts:
+    def __init__(self, log_file):
+        self.prompts = []
+        self.log_file = log_file
+        self.load_prompts_from_log()
+
+    def add_prompt(self, identity, prompt, temperature):
+        self.prompts.append({'identity': identity, 'prompt': prompt, 'temperature': temperature})
+        self.save_prompts_to_log()
+
+    def remove_prompt(self, identity):
+        self.prompts = [p for p in self.prompts if p['identity'] != identity]
+        self.save_prompts_to_log()
+
+    def get_prompt(self, identity):
+        for prompt in self.prompts:
+            if prompt['identity'] == identity:
+                return prompt['prompt']
+        return None
+
+    def get_temperature(self, identity):
+        for prompt in self.prompts:
+            if prompt['identity'] == identity:
+                return prompt['temperature']
+        return None
+
+    def load_prompts_from_log(self):
+        try:
+            with open(self.log_file, 'r') as file:
+                self.prompts = json.load(file)
+        except FileNotFoundError:
+            print("No file found. Starting from scratch.")
+            pass  # Ignore if the log file is not found or cannot be read
+
+    def save_prompts_to_log(self):
+        with open(self.log_file, 'w') as file:
+            json.dump(self.prompts, file)
+            
+# Accessing prompts and temperatures
+# print(llm_prompts.get_prompt("LLM_2"))  # Output: Write a short story about a detective solving a mysterious murder case.
+# print(llm_prompts.get_temperature("LLM_2"))  # Output: 0.7
+
+
+# Adding a new prompt with temperature
+# llm_prompts.add_prompt("LLM_5", "Explain the concept of artificial intelligence.", 0.5)
+
+# Set the filepath for the log
+log_file_path = '/drive/My Drive/Colab Notebooks/llm_prompts_log.json'
+sys_prompt = LLMPrompts(log_file_path)
+
+# Print all prompts and temperatures
+for prompt in sys_prompt.prompts:
+   print(prompt['identity'], prompt['prompt'], prompt['temperature'])
+
+# Want to add personas? Do that here!
+
+# sys_prompt.add_prompt('LLM_2', "")
+
+
+
+
+# Log the last conversation
+convo_log_file_path = '/drive/My Drive/Colab Notebooks/last_conversation.json'
+
+# Convert list to JSON object
+json_object = json.dumps(context)
+
+# Save JSON object to a file
+with open('output.json', 'w') as file:
+    file.write(json_object)
+
+# Save list items as separate lines in a JSON
+# with open(convo_log_file_path, 'w') as file:
+#     for item in context:
+#         file.write(json.dumps(item) + '\n')

test-repo.py

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+import os
+import csv
+import panel as pn
+from datetime import datetime
+import pandas as pd
+# import huggingface_hub
+from huggingface_hub import Repository
+
+DATASET_REPO_URL = "https://huggingface.co/datasets/julien-c/persistent-space-dataset"
+DATA_FILENAME = "data.csv"
+DATA_FILE = os.path.join("data", DATA_FILENAME)
+
+repo_dir = "data"
+if not os.path.exists(repo_dir):
+    os.makedirs(repo_dir)
+
+repo = pn.widgets.TextInput(name="Repository URL", value=DATASET_REPO_URL)
+name_input = pn.widgets.TextInput(name="Your name")
+message_input = pn.widgets.TextAreaInput(name="Your message")
+
+def generate_html() -> str:
+    if not os.path.exists(DATA_FILE):
+        return "<div class='chatbot'>no messages yet</div>"
+    else:
+        df = pd.read_csv(DATA_FILE)
+        df = df.iloc[::-1]  # Reverse the order of rows
+        html = "<div class='chatbot'>"
+        for _, row in df.iterrows():
+            html += "<div>"
+            html += f"<span>{row['name']}</span>"
+            html += f"<span class='message'>{row['message']}</span>"
+            html += "</div>"
+        html += "</div>"
+        return html
+
+def store_message(event):
+    name = name_input.value
+    message = message_input.value
+    if name and message:
+        with open(DATA_FILE, "a", newline="") as csvfile:
+            writer = csv.writer(csvfile)
+            writer.writerow([name, message, str(datetime.now())])
+        pn.state.session_context.request_relayout(repo)
+        pn.state.session_context.request_relayout(messages)
+
+repo.on_change(store_message, "value")
+name_input.on_change(store_message, "value")
+message_input.on_change(store_message, "value")
+
+repo_text = pn.pane.Markdown(f"The dataset repo is [{DATASET_REPO_URL}]({DATASET_REPO_URL}) (open in new tab)", sizing_mode="stretch_width")
+messages = pn.pane.HTML(sizing_mode="stretch_width")
+messages.object = generate_html()
+
+template = pn.template.FastListTemplate(
+    site="Persistent Space Dataset",
+    title="Reading/Writing to a HuggingFace Dataset Repo from Spaces",
+    main=[
+        pn.Column(repo_text, name_input, message_input, messages),
+    ],
+    header_background="#333",
+    header_color="white",
+    main_max_width="800px",
+    main_padding=20,
+    main_min_height="600px"
+)
+template.servable()