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Browse files- .streamlit/config.toml +6 -0
- .streamlit/secrets.toml +0 -0
- app.py +9 -0
- pages/1_Simple_Chat_UI.py +60 -0
- requirements.txt +1 -1
.streamlit/config.toml
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[theme]
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primaryColor="#F63366"
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backgroundColor="#FFFFFF"
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secondaryBackgroundColor="#F0F2F6"
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textColor="#262730"
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font="sans serif"
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.streamlit/secrets.toml
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app.py
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import streamlit as st
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readme = """
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This is a Simple chat application using streamlit and huggingface api
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watch the below video to get started
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"""
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st.write(readme)
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pages/1_Simple_Chat_UI.py
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import streamlit as st
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from transformers import AutoTokenizer, AutoModelForCausalLM, AutoModelForSeq2SeqLM
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def load_model_tokenizer(model_name, hf_api_key):
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if model_name == "Mistral-7B":
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#model_name="mistralai/Mistral-7B-Instruct-v0.2"
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#model = AutoModelForCausalLM.from_pretrained(model_name, token=hf_api_key)
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#tokenizer = AutoTokenizer.from_pretrained(model_name, tokenizer=hf_api_key)
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pass
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elif model_name == "blenderbot-400M-distill":
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model_name = "facebook/blenderbot-400M-distill"
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model = AutoModelForCausalLM.from_pretrained(model_name)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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return (model,tokenizer)
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def generate_response(prompt_input, model, tokenizer):
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inputs = tokenizer.encode_plus(prompt_input, return_tensors="pt")
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# Generate the response from the model with additional parameters
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outputs = model.generate(**inputs, max_length=max_length, do_sample=True ,temperature=temperature)
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response = tokenizer.decode(outputs[0], skip_special_tokens=True).strip()
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return response
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st.set_page_config(page_title="Learn Geoscience")
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with st.sidebar:
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st.title('Learn Geoscience Chat')
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if 'hf_key' in st.secrets:
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st.success('Huggingface API key provided', icon='β
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hf_api_key = st.secrets['hf_key']
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else:
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hf_api_key = st.text_input('Enter Huggingface API Key:', type='password')
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if not hf_api_key:
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st.warning('Please enter Huggingface API key!', icon='β οΈ')
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else:
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st.success('Proceed to entering your prompt message!', icon='π')
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max_length = st.slider("Max Length", 10, 100, 50)
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temperature = st.slider("Temperature", 0.0, 1.0, 0.7)
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if "messages" not in st.session_state.keys():
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st.session_state.messages = [{"role": "assistant", "content": "How may I help you?"}]
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model_name = st.radio("Select model to chat", options=["Mistral-7B", "LLaMa-2B", "blenderbot-400M-distill"], horizontal=True, key='model_selection')
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model, tokenizer = load_model_tokenizer(model_name, hf_api_key)
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for message in st.session_state.messages:
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with st.chat_message(message["role"]):
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st.write(message["content"])
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if prompt := st.chat_input(disabled = not hf_api_key):
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st.session_state.messages.append({"role": "user", "content": prompt})
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with st.chat_message("user"):
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st.write(prompt)
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if st.session_state.messages[-1]["role"] != "assistant":
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with st.chat_message("assistant"):
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with st.spinner("Thinking..."):
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response = generate_response(prompt, model, tokenizer)
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st.write(response)
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message = {"role": "assistant", "content": response}
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st.session_state.messages.append(message)
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requirements.txt
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transformers
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torch
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transformers
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torch
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