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import os
import requests
import streamlit as st
# Get the Hugging Face API Token from environment variables
HF_API_TOKEN = os.getenv("HF_API_KEY")
if not HF_API_TOKEN:
raise ValueError("Hugging Face API Token is not set in the environment variables.")
# Hugging Face API URLs and headers for Gemma models
GEMMA_7B_API_URL = "https://api-inference.huggingface.co/models/google/gemma-1.1-7b-it"
GEMMA_27B_API_URL = "https://api-inference.huggingface.co/models/google/gemma-2-27b-it"
CODEGEMMA_7B_API_URL = "https://api-inference.huggingface.co/models/google/codegemma-7b"
HEADERS = {"Authorization": f"Bearer {HF_API_TOKEN}"}
def query_model(api_url, payload):
response = requests.post(api_url, headers=HEADERS, json=payload)
return response.json()
def add_message_to_conversation(user_message, bot_message, model_name):
st.session_state.conversation.append((user_message, bot_message, model_name))
# Streamlit app
st.set_page_config(page_title="Gemma Chatbot Interface", layout="wide")
st.title("Gemma Chatbot Interface")
st.write("Gemma Chatbot Interface")
# Initialize session state for conversation and model history
if "conversation" not in st.session_state:
st.session_state.conversation = []
if "model_history" not in st.session_state:
st.session_state.model_history = {model: [] for model in ["Gemma-1.1-7B", "Gemma-2-27B", "codegemma-7b"]}
# Dropdown for Gemma model selection
gemma_selection = st.selectbox("Select Gemma Model", ["Gemma-1.1-7B", "Gemma-2-27B", "codegemma-7b"])
# User input for question
question = st.text_input("Question", placeholder="Enter your question here...")
# Handle user input and Gemma model response
if st.button("Send") and question:
try:
with st.spinner("Waiting for the model to respond..."):
chat_history = " ".join(st.session_state.model_history[gemma_selection]) + f"User: {question}\n"
if gemma_selection == "Gemma-1.1-7B":
response = query_model(GEMMA_7B_API_URL, {"inputs": chat_history})
elif gemma_selection == "Gemma-2-27B":
response = query_model(GEMMA_27B_API_URL, {"inputs": chat_history})
elif gemma_selection == "codegemma-7b":
response = query_model(CODEGEMMA_7B_API_URL, {"inputs": chat_history})
answer = response.get("generated_text", "No response") if isinstance(response, dict) else response[0].get("generated_text", "No response") if isinstance(response, list) else "No response"
add_message_to_conversation(question, answer, gemma_selection)
st.session_state.model_history[gemma_selection].append(f"User: {question}\n{gemma_selection}: {answer}\n")
except ValueError as e:
st.error(str(e))
# Custom CSS for chat bubbles
st.markdown(
"""
<style>
.chat-bubble {
padding: 10px 14px;
border-radius: 14px;
margin-bottom: 10px;
display: inline-block;
max-width: 80%;
color: black;
}
.chat-bubble.user {
background-color: #dcf8c6;
align-self: flex-end;
}
.chat-bubble.bot {
background-color: #fff;
align-self: flex-start;
}
.chat-container {
display: flex;
flex-direction: column;
gap: 10px;
margin-top: 20px;
}
</style>
""",
unsafe_allow_html=True
)
# Display the conversation
st.write('<div class="chat-container">', unsafe_allow_html=True)
for user_message, bot_message, model_name in st.session_state.conversation:
st.write(f'<div class="chat-bubble user">You: {user_message}</div>', unsafe_allow_html=True)
st.write(f'<div class="chat-bubble bot">{model_name}: {bot_message}</div>', unsafe_allow_html=True)
st.write('</div>', unsafe_allow_html=True) |