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| # import os | |
| # import streamlit as st | |
| # import torch | |
| # from langchain.chains import LLMChain | |
| # from langchain.prompts import ChatPromptTemplate | |
| # from langchain_huggingface import HuggingFaceEndpoint | |
| # def create_conversation_prompt(name1: str, name2: str, persona_style: str): | |
| # """ | |
| # Create a prompt that instructs the model to produce exactly 15 messages | |
| # of conversation, alternating between name1 and name2, starting with name1. | |
| # We will be very explicit and not allow any formatting except the required lines. | |
| # """ | |
| # prompt_template_str = f""" | |
| # You are simulating a conversation of exactly 15 messages between two people: {name1} and {name2}. | |
| # {name1} speaks first (message 1), then {name2} (message 2), then {name1} (message 3), and so forth, | |
| # alternating until all 15 messages are complete. The 15th message is by {name1}. | |
| # Requirements: | |
| # - Output exactly 15 lines, no more, no less. | |
| # - Each line must be a single message in the format: | |
| # {name1}: <message> or {name2}: <message> | |
| # - Do not add any headings, numbers, sample outputs, or explanations. | |
| # - Do not mention code, programming, or instructions. | |
| # - Each message should be 1-2 short sentences, friendly, natural, reflecting the style: {persona_style}. | |
| # - Use everyday language, can ask questions, show opinions. | |
| # - Use emojis sparingly if it fits the style (no more than 1-2 total). | |
| # - No repeated lines, each message should logically follow from the previous one. | |
| # - Do not produce anything after the 15th message. No extra lines or text. | |
| # Produce all 15 messages now: | |
| # """ | |
| # return ChatPromptTemplate.from_template(prompt_template_str) | |
| # def create_summary_prompt(name1: str, name2: str, conversation: str): | |
| # """Prompt for generating a title and summary.""" | |
| # summary_prompt_str = f""" | |
| # Below is a completed 15-message conversation between {name1} and {name2}: | |
| # {conversation} | |
| # Please provide: | |
| # Title: <A short descriptive title of the conversation> | |
| # Summary: <A few short sentences highlighting the main points, tone, and conclusion> | |
| # Do not continue the conversation, do not repeat it, and do not add extra formatting beyond the two lines: | |
| # - One line starting with "Title:" | |
| # - One line starting with "Summary:" | |
| # """ | |
| # return ChatPromptTemplate.from_template(summary_prompt_str) | |
| # def main(): | |
| # st.title("LLM Conversation Simulation") | |
| # model_names = [ | |
| # "meta-llama/Llama-3.3-70B-Instruct", | |
| # "meta-llama/Llama-3.1-405B-Instruct", | |
| # "Qwen/Qwen2.5-72B-Instruct", | |
| # "deepseek-ai/DeepSeek-V3", | |
| # "deepseek-ai/DeepSeek-V2.5" | |
| # ] | |
| # selected_model = st.selectbox("Select a model:", model_names) | |
| # name1 = st.text_input("Enter the first user's name:", value="Alice") | |
| # name2 = st.text_input("Enter the second user's name:", value="Bob") | |
| # persona_style = st.text_area("Enter the persona style characteristics:", | |
| # value="friendly, curious, and a bit sarcastic") | |
| # if st.button("Start Conversation Simulation"): | |
| # st.write("**Loading model...**") | |
| # print("Loading model...") | |
| # with st.spinner("Starting simulation..."): | |
| # endpoint_url = f"https://api-inference.huggingface.co/models/{selected_model}" | |
| # try: | |
| # llm = HuggingFaceEndpoint( | |
| # endpoint_url=endpoint_url, | |
| # huggingfacehub_api_token=os.environ.get("HUGGINGFACEHUB_API_TOKEN"), | |
| # task="text-generation", | |
| # temperature=0.7, | |
| # max_new_tokens=512 | |
| # ) | |
| # st.write("**Model loaded successfully!**") | |
| # print("Model loaded successfully!") | |
| # except Exception as e: | |
| # st.error(f"Error initializing HuggingFaceEndpoint: {e}") | |
| # print(f"Error initializing HuggingFaceEndpoint: {e}") | |
| # return | |
| # conversation_prompt = create_conversation_prompt(name1, name2, persona_style) | |
| # conversation_chain = LLMChain(llm=llm, prompt=conversation_prompt) | |
| # st.write("**Generating the full 15-message conversation...**") | |
| # print("Generating the full 15-message conversation...") | |
| # try: | |
| # # Generate all 15 messages in one go | |
| # conversation = conversation_chain.run(chat_history="", input="").strip() | |
| # st.subheader("Final Conversation:") | |
| # st.text(conversation) | |
| # print("Conversation Generation Complete.\n") | |
| # print("Full Conversation:\n", conversation) | |
| # # Summarize the conversation | |
| # summary_prompt = create_summary_prompt(name1, name2, conversation) | |
| # summary_chain = LLMChain(llm=llm, prompt=summary_prompt) | |
| # st.subheader("Summary and Title:") | |
| # st.write("**Summarizing the conversation...**") | |
| # print("Summarizing the conversation...") | |
| # summary = summary_chain.run(chat_history="", input="") | |
| # st.write(summary) | |
| # print("Summary:\n", summary) | |
| # except Exception as e: | |
| # st.error(f"Error generating conversation: {e}") | |
| # print(f"Error generating conversation: {e}") | |
| # if __name__ == "__main__": | |
| # main() | |
| import os | |
| import streamlit as st | |
| import google.cloud.aiplatform as aiplatform | |
| from langchain.chains import LLMChain | |
| from langchain.prompts import ChatPromptTemplate | |
| from langchain.llms.base import LLM | |
| from pydantic import BaseModel | |
| from typing import Optional, List, Mapping, Any | |
| ############################################################################### | |
| # 1. Create a Custom LLM class for LangChain to call your Vertex AI endpoint. | |
| ############################################################################### | |
| class VertexAICustomModel(LLM, BaseModel): | |
| project_id: str | |
| location: str | |
| endpoint_id: str | |
| temperature: float = 0.7 | |
| max_new_tokens: int = 512 | |
| def _llm_type(self) -> str: | |
| return "vertex_ai_custom" | |
| def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str: | |
| # Initialize Vertex AI with your project/region | |
| aiplatform.init(project=self.project_id, location=self.location) | |
| endpoint = aiplatform.Endpoint( | |
| endpoint_name=f"projects/{self.project_id}/locations/{self.location}/endpoints/{self.endpoint_id}" | |
| ) | |
| # Construct the instance for prediction. | |
| # NOTE: Adjust 'prompt', 'temperature', etc. if your model expects different parameters. | |
| instance = { | |
| "prompt": prompt, | |
| "temperature": self.temperature, | |
| "max_new_tokens": self.max_new_tokens | |
| } | |
| # Call the endpoint | |
| response = endpoint.predict(instances=[instance]) | |
| # Extract the text from the response. | |
| # This will vary depending on how your model returns predictions. | |
| # A common approach is response.predictions[0]["generated_text"], | |
| # but confirm your model's actual JSON structure. | |
| predictions = response.predictions | |
| if not predictions or "generated_text" not in predictions[0]: | |
| raise ValueError( | |
| f"Unexpected response structure from Vertex AI endpoint: {response}" | |
| ) | |
| text = predictions[0]["generated_text"] | |
| # Optionally apply 'stop' tokens | |
| if stop: | |
| for s in stop: | |
| if s in text: | |
| text = text.split(s)[0] | |
| return text | |
| def _identifying_params(self) -> Mapping[str, Any]: | |
| """Return any identifying parameters of this LLM.""" | |
| return { | |
| "endpoint_id": self.endpoint_id, | |
| "project_id": self.project_id, | |
| "location": self.location, | |
| "temperature": self.temperature, | |
| "max_new_tokens": self.max_new_tokens, | |
| } | |
| ############################################################################### | |
| # 2. Create your conversation and summary prompt templates (unchanged). | |
| ############################################################################### | |
| def create_conversation_prompt(name1: str, name2: str, persona_style: str): | |
| """ | |
| Create a prompt that instructs the model to produce exactly 15 messages | |
| of conversation, alternating between name1 and name2, starting with name1. | |
| """ | |
| prompt_template_str = f""" | |
| You are simulating a conversation of exactly 15 messages between two people: {name1} and {name2}. | |
| {name1} speaks first (message 1), then {name2} (message 2), then {name1} (message 3), and so forth, | |
| alternating until all 15 messages are complete. The 15th message is by {name1}. | |
| Requirements: | |
| - Output exactly 15 lines, no more, no less. | |
| - Each line must be a single message in the format: | |
| {name1}: <message> or {name2}: <message> | |
| - Do not add any headings, numbers, sample outputs, or explanations. | |
| - Do not mention code, programming, or instructions. | |
| - Each message should be 1-2 short sentences, friendly, natural, reflecting the style: {persona_style}. | |
| - Use everyday language, can ask questions, show opinions. | |
| - Use emojis sparingly if it fits the style (no more than 1-2 total). | |
| - No repeated lines, each message should logically follow from the previous one. | |
| - Do not produce anything after the 15th message. No extra lines or text. | |
| Produce all 15 messages now: | |
| """ | |
| return ChatPromptTemplate.from_template(prompt_template_str) | |
| def create_summary_prompt(name1: str, name2: str, conversation: str): | |
| """Prompt for generating a title and summary.""" | |
| summary_prompt_str = f""" | |
| Below is a completed 15-message conversation between {name1} and {name2}: | |
| {conversation} | |
| Please provide: | |
| Title: <A short descriptive title of the conversation> | |
| Summary: <A few short sentences highlighting the main points, tone, and conclusion> | |
| Do not continue the conversation, do not repeat it, and do not add extra formatting beyond the two lines: | |
| - One line starting with "Title:" | |
| - One line starting with "Summary:" | |
| """ | |
| return ChatPromptTemplate.from_template(summary_prompt_str) | |
| ############################################################################### | |
| # 3. Main Streamlit app with Vertex AI usage. | |
| ############################################################################### | |
| def main(): | |
| st.title("LLM Conversation Simulation (GCP Vertex AI)") | |
| # We can remove model selection if we are always using your deployed model: | |
| # st.selectbox(... ) # => Removed | |
| # Hardcode or load your Vertex AI endpoint details here | |
| project_id = "282802344966" | |
| location = "us-west1" | |
| endpoint_id = "1106913540054188032" | |
| # Input fields for conversation | |
| name1 = st.text_input("Enter the first user's name:", value="Alice") | |
| name2 = st.text_input("Enter the second user's name:", value="Bob") | |
| persona_style = st.text_area("Enter the persona style characteristics:", | |
| value="friendly, curious, and a bit sarcastic") | |
| if st.button("Start Conversation Simulation"): | |
| st.write("**Initializing Vertex AI endpoint...**") | |
| st.spinner("Starting simulation...") | |
| # Create your custom LLM that calls Vertex AI | |
| llm = VertexAICustomModel( | |
| project_id=project_id, | |
| location=location, | |
| endpoint_id=endpoint_id, | |
| temperature=0.7, | |
| max_new_tokens=512 | |
| ) | |
| st.write("**Vertex AI endpoint loaded successfully!**") | |
| # Build the conversation chain | |
| conversation_prompt = create_conversation_prompt(name1, name2, persona_style) | |
| conversation_chain = LLMChain(llm=llm, prompt=conversation_prompt) | |
| st.write("**Generating the full 15-message conversation...**") | |
| try: | |
| # Generate all 15 messages in one go | |
| conversation = conversation_chain.run(chat_history="", input="").strip() | |
| st.subheader("Final Conversation:") | |
| st.text(conversation) | |
| # Summarize the conversation | |
| summary_prompt = create_summary_prompt(name1, name2, conversation) | |
| summary_chain = LLMChain(llm=llm, prompt=summary_prompt) | |
| st.subheader("Summary and Title:") | |
| st.write("**Summarizing the conversation...**") | |
| summary = summary_chain.run(chat_history="", input="") | |
| st.write(summary) | |
| except Exception as e: | |
| st.error(f"Error generating conversation: {e}") | |
| print(f"Error generating conversation: {e}") | |
| if __name__ == "__main__": | |
| main() | |