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| import streamlit as st | |
| from transformers import AutoTokenizer, AutoModelForCausalLM, AutoModelForSeq2SeqLM | |
| def load_model_tokenizer(model_name, hf_api_key): | |
| if model_name == "Mistral-7B": | |
| #model_name="mistralai/Mistral-7B-Instruct-v0.2" | |
| #model = AutoModelForCausalLM.from_pretrained(model_name, token=hf_api_key) | |
| #tokenizer = AutoTokenizer.from_pretrained(model_name, tokenizer=hf_api_key) | |
| pass | |
| elif model_name == "blenderbot-400M-distill": | |
| model_name = "facebook/blenderbot-400M-distill" | |
| model = AutoModelForCausalLM.from_pretrained(model_name) | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| return (model,tokenizer) | |
| def generate_response(prompt_input, model, tokenizer): | |
| inputs = tokenizer.encode_plus(prompt_input, return_tensors="pt") | |
| # Generate the response from the model with additional parameters | |
| outputs = model.generate(**inputs, max_length=max_length, do_sample=True ,temperature=temperature) | |
| response = tokenizer.decode(outputs[0], skip_special_tokens=True).strip() | |
| return response | |
| st.set_page_config(page_title="Learn Geoscience") | |
| with st.sidebar: | |
| st.title('Learn Geoscience Chat') | |
| if 'hf_key' in st.secrets: | |
| st.success('Huggingface API key provided', icon='✅') | |
| hf_api_key = st.secrets['hf_key'] | |
| else: | |
| hf_api_key = st.text_input('Enter Huggingface API Key:', type='password') | |
| if not hf_api_key: | |
| st.warning('Please enter Huggingface API key!', icon='⚠️') | |
| else: | |
| st.success('Proceed to entering your prompt message!', icon='👉') | |
| max_length = st.slider("Max Length", 10, 100, 50) | |
| temperature = st.slider("Temperature", 0.0, 1.0, 0.7) | |
| if "messages" not in st.session_state.keys(): | |
| st.session_state.messages = [{"role": "assistant", "content": "How may I help you?"}] | |
| model_name = st.radio("Select model to chat", options=["Mistral-7B", "LLaMa-2B", "blenderbot-400M-distill"], horizontal=True, key='model_selection') | |
| model, tokenizer = load_model_tokenizer(model_name, hf_api_key) | |
| for message in st.session_state.messages: | |
| with st.chat_message(message["role"]): | |
| st.write(message["content"]) | |
| if prompt := st.chat_input(disabled = not hf_api_key): | |
| st.session_state.messages.append({"role": "user", "content": prompt}) | |
| with st.chat_message("user"): | |
| st.write(prompt) | |
| if st.session_state.messages[-1]["role"] != "assistant": | |
| with st.chat_message("assistant"): | |
| with st.spinner("Thinking..."): | |
| response = generate_response(prompt, model, tokenizer) | |
| st.write(response) | |
| message = {"role": "assistant", "content": response} | |
| st.session_state.messages.append(message) | |