| # import gradio as gr | |
| # from langchain.llms import LlamaCpp | |
| # from langchain import PromptTemplate, LLMChain | |
| # from langchain.llms import GPT4All | |
| # from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler | |
| # # import requests | |
| # # url = "https://huggingface.co/TheBloke/Nous-Hermes-13B-GGML/resolve/main/nous-hermes-13b.ggmlv3.q4_0.bin" | |
| # # response = requests.get(url) | |
| # # with open("nous-hermes-13b.ggmlv3.q4_0.bin", "wb") as f: | |
| # # f.write(response.content) | |
| # print("DONE") | |
| # def func(user): | |
| # template = """ | |
| # Your name is John and not a assistant but more like a chatbot. Respond precise not more words and act like a human. for example: user: How are you? You: I'm doing good how about you?. user: hello You: Hello how you doing?. Don't say How can I assist you today?. | |
| # Question: {question} | |
| # Answer: """ | |
| # prompt = PromptTemplate(template=template, input_variables=["question"]) | |
| # local_path = ( | |
| # "./nous-hermes-13b.ggmlv3.q4_0.bin" | |
| # ) | |
| # # # Callbacks support token-wise streaming | |
| # # callbacks = [StreamingStdOutCallbackHandler()] | |
| # # Verbose is required to pass to the callback manager | |
| # llm = LlamaCpp(model_path="./nous-hermes-13b.ggmlv3.q4_0.bin", n_ctx=2048) | |
| # llm_chain = LLMChain(prompt=prompt, llm=llm) | |
| # question = user | |
| # llm_chain.run(question) | |
| # return llm_chain.run(question) | |
| # iface = gr.Interface(fn=func, inputs="text", outputs="text") | |
| # iface.launch() | |
| # import gradio as gr | |
| # from langchain.llms import LlamaCpp | |
| # from langchain import PromptTemplate, LLMChain | |
| # from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler | |
| # print("DONE") | |
| # def func(user): | |
| # template = """ | |
| # Your name is John and not a assistant but more like a chatbot. Respond precise not more words and act like a human. for example: user: How are you? You: I'm doing good how about you?. user: hello You: Hello how you doing?. Don't say How can I assist you today?. | |
| # Question: {question} | |
| # Answer: """ | |
| # prompt = PromptTemplate(template=template, input_variables=["question"]) | |
| # local_path = "./nous-hermes-13b.ggmlv3.q4_0.bin" | |
| # llm = LlamaCpp(model_path=local_path) | |
| # llm_chain = LLMChain(prompt=prompt, llm=llm, streaming=True) # Enable streaming mode | |
| # question = user | |
| # llm_chain.run(question) | |
| # return llm_chain.run(question) | |
| # iface = gr.Interface(fn=func, inputs="text", outputs="text") | |
| # iface.launch() | |
| import gradio as gr | |
| from gpt4allj import Model | |
| # Load the local model | |
| model = Model('./ggml-gpt4all-j.bin') | |
| # Define a function that generates the model's response given a prompt | |
| def generate_response(prompt): | |
| response = model.generate(prompt) | |
| return response | |
| # Create a Gradio interface with a text input and an output text box | |
| iface = gr.Interface( | |
| fn=generate_response, | |
| inputs="text", | |
| outputs="text", | |
| title="GPT-4 AllJ", | |
| description="Generate responses using GPT-4 AllJ model." | |
| ) | |
| # Run the Gradio interface | |
| iface.launch() | |