Initial commit

requirements.txt

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+streamlit
+huggingface_hub

streamlit_s2.py

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+import streamlit as st
+import requests
+from huggingface_hub import InferenceClient
+
+st.title("Streamlit with Hugging Face API")
+
+def get_models():
+     return ["meta-llama/Meta-Llama-3-8B-Instruct","meta-llama/Llama-3.1-8B-Instruct"]
+
+models = get_models()
+model_names = get_models()
+placeholder_option = "Select a model"
+model_names_with_placeholder = [placeholder_option] + model_names
+selected_model = st.selectbox("Select the model:", model_names_with_placeholder)
+prompt1 = st.chat_input("Message")
+
+if selected_model != placeholder_option:
+    st.write(f"You selected: {selected_model}")
+    client = InferenceClient(model=selected_model)
+
+    # Initialize chat history in session state if it doesn't exist
+    if "messages" not in st.session_state:
+        st.session_state.messages = []
+
+    # Display chat messages from history on app rerun
+    for message in st.session_state.messages:
+        with st.chat_message(message["role"]):
+            st.markdown(message["content"])
+
+    if prompt1:  
+        with st.chat_message("user"):
+            st.markdown(prompt1)
+        st.session_state.messages.append({"role": "user", "content": prompt1})
+        try:
+            response = client.text_generation(prompt1)
+        except Exception as e:
+            response = f"An error occurred: {e}"
+        with st.chat_message("assistant"):
+            st.markdown(response)
+    # Add assistant response to chat history
+        st.session_state.messages.append({"role": "assistant", "content": response})
+
+else:
+    st.write("Please select a model.")