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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", | |
| # "mistralai/Mistral-7B-v0.1", | |
| # "tiiuae/falcon-7b" | |
| # ] | |
| # 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 torch | |
| from langchain.chains import LLMChain | |
| from langchain.prompts import ChatPromptTemplate | |
| from langchain_huggingface import HuggingFaceEndpoint | |
| # Additional imports for AnimateDiff | |
| from diffusers import AnimateDiffPipeline, MotionAdapter, EulerDiscreteScheduler | |
| from diffusers.utils import export_to_gif | |
| from huggingface_hub import hf_hub_download | |
| from safetensors.torch import load_file | |
| 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) | |
| def main(): | |
| st.title("LLM Conversation Simulation + AnimateDiff Video") | |
| model_names = [ | |
| "meta-llama/Llama-3.3-70B-Instruct", | |
| "mistralai/Mistral-7B-v0.1", | |
| "tiiuae/falcon-7b" | |
| ] | |
| 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) | |
| # Extract the summary line from the summary text | |
| lines = summary.split("\n") | |
| summary_line = "" | |
| for line in lines: | |
| if line.strip().lower().startswith("summary:"): | |
| summary_line = line.split("Summary:", 1)[-1].strip() | |
| break | |
| if not summary_line: | |
| summary_line = "A friendly scene reflecting the conversation." | |
| # Now integrate AnimateDiff for text-to-video generation | |
| st.write("**Generating animation from summary using ByteDance/AnimateDiff-Lightning...**") | |
| print("Generating animation from summary using ByteDance/AnimateDiff-Lightning...") | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| dtype = torch.float16 if torch.cuda.is_available() else torch.float32 | |
| step = 4 # Adjust if needed | |
| repo = "ByteDance/AnimateDiff-Lightning" | |
| ckpt = f"animatediff_lightning_{step}step_diffusers.safetensors" | |
| base = "emilianJR/epiCRealism" # Check if this model exists or choose a known base model | |
| # Load and configure AnimateDiff pipeline | |
| adapter = MotionAdapter().to(device, dtype) | |
| adapter.load_state_dict(load_file(hf_hub_download(repo ,ckpt), device=device)) | |
| pipe = AnimateDiffPipeline.from_pretrained( | |
| base, | |
| motion_adapter=adapter, | |
| torch_dtype=dtype | |
| ).to(device) | |
| pipe.scheduler = EulerDiscreteScheduler.from_config( | |
| pipe.scheduler.config, | |
| timestep_spacing="trailing", | |
| beta_schedule="linear" | |
| ) | |
| # Generate the animation | |
| output = pipe(prompt=summary_line, guidance_scale=1.0, num_inference_steps=step) | |
| # Save as GIF | |
| # output.frames is a list of frames (PIL images) | |
| st.write("**Exporting animation to GIF...**") | |
| print("Exporting animation to GIF...") | |
| export_to_gif(output.frames, "animation.gif") | |
| st.subheader("Generated Animation:") | |
| st.image("animation.gif", caption="Generated by AnimateDiff using summary prompt") | |
| except Exception as e: | |
| st.error(f"Error generating conversation or summary: {e}") | |
| print(f"Error generating conversation or summary: {e}") | |
| if __name__ == "__main__": | |
| main() | |