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import streamlit as st
from huggingface_hub import InferenceClient
import os
import sys
st.title("CODEFUSSION ☄") # Changed from "strangerzone.world🗞️"
base_url = "https://api-inference.huggingface.co/models/"
API_KEY = os.environ.get('HUGGINGFACE_API_KEY')
# print(API_KEY)
# headers = {"Authorization":"Bearer "+API_KEY}
model_links = {
"LegacyLift🚀": base_url + "mistralai/Mistral-7B-Instruct-v0.2", # Changed from "Dorado🥤"
"ModernMigrate⭐": base_url + "mistralai/Mixtral-8x7B-Instruct-v0.1", # Changed from "Hercules⭐"
"RetroRecode🔄": base_url + "microsoft/Phi-3-mini-4k-instruct" # Changed from "Lepus🚀"
}
# Pull info about the model to display
model_info = {
"LegacyLift🚀": {
'description': """The LegacyLift model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n \
\nThis model is best for minimal problem-solving, content writing, and daily tips.\n""",
'logo': './dorado.png'
},
"ModernMigrate⭐": {
'description': """The ModernMigrate model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n \
\nThis model excels in coding, logical reasoning, and high-speed inference. \n""",
'logo': './hercules.png'
},
"RetroRecode🔄": {
'description': """The RetroRecode model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n \
\nThis model is best suited for critical development, practical knowledge, and serverless inference.\n""",
'logo': './lepus.png'
},
}
def format_promt(message, custom_instructions=None):
prompt = ""
if custom_instructions:
prompt += f"[INST] {custom_instructions} [/INST]"
prompt += f"[INST] {message} [/INST]"
return prompt
def reset_conversation():
'''
Resets Conversation
'''
st.session_state.conversation = []
st.session_state.messages = []
return None
models = [key for key in model_links.keys()]
selected_model = st.sidebar.selectbox("Select Model", models)
temp_values = st.sidebar.slider('Select a temperature value', 0.0, 1.0, (0.5))
st.sidebar.button('Reset Chat', on_click=reset_conversation) # Reset button
st.sidebar.write(f"You're now chatting with **{selected_model}**")
st.sidebar.markdown(model_info[selected_model]['description'])
st.sidebar.image(model_info[selected_model]['logo'])
st.sidebar.markdown("*Generated content may be inaccurate or false.*")
st.sidebar.markdown("\nYou can support me by sponsoring to buy me a coffee🥤.[here](https://buymeacoffee.com/prithivsakthi).")
if "prev_option" not in st.session_state:
st.session_state.prev_option = selected_model
if st.session_state.prev_option != selected_model:
st.session_state.messages = []
# st.write(f"Changed to {selected_model}")
st.session_state.prev_option = selected_model
reset_conversation()
repo_id = model_links[selected_model]
st.subheader(f'{selected_model}')
# st.title(f'ChatBot Using {selected_model}')
if "messages" not in st.session_state:
st.session_state.messages = []
for message in st.session_state.messages:
with st.chat_message(message["role"]):
st.markdown(message["content"])
if prompt := st.chat_input(f"Hi I'm {selected_model}🗞️, How can I help you today?"):
custom_instruction = "Act like a Human in conversation"
with st.chat_message("user"):
st.markdown(prompt)
st.session_state.messages.append({"role": "user", "content": prompt})
formated_text = format_promt(prompt, custom_instruction)
with st.chat_message("assistant"):
client = InferenceClient(
model=model_links[selected_model], )
output = client.text_generation(
formated_text,
temperature=temp_values, # 0.5
max_new_tokens=3000,
stream=True
)
response = st.write_stream(output)
st.session_state.messages.append({"role": "assistant", "content": response}) |