Initial setup

Initial file set up

app.py

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+import streamlit as st
+from openai import OpenAI
+import os
+import sys
+from langchain.callbacks import StreamlitCallbackHandler
+from dotenv import load_dotenv, dotenv_values
+load_dotenv()
+
+
+if 'key' not in st.session_state:
+    st.session_state['key'] = 'value'
+
+
+
+
+# initialize the client but point it to TGI
+client = OpenAI(
+  base_url="https://api-inference.huggingface.co/v1",
+  #api_key=os.environ.get('HUGGINGFACEHUB_API_TOKEN')#"hf_xxx" # Replace with your token
+) 
+
+
+
+
+#Create supported models
+model_links ={
+    "Mistral":"mistralai/Mistral-7B-Instruct-v0.2",
+    "Gemma":"google/gemma-7b-it"
+
+}
+
+
+
+# Define the available models
+# models = ["Mistral", "Gemma"]
+models =[key for key in model_links.keys()]
+
+# Create the sidebar with the dropdown for model selection
+selected_model = st.sidebar.selectbox("Select Model", models)
+
+
+
+#Pull in the model we want to use
+repo_id = model_links[selected_model]
+
+
+
+st.title(f'ChatBot Using {selected_model}')
+
+# Set a default model
+if selected_model not in st.session_state:
+    st.session_state[selected_model] = model_links[selected_model] #"google/gemma-7b-it"
+
+# Initialize chat history
+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"])
+
+
+
+# Accept user input
+if prompt := st.chat_input("What is up?"):
+
+    # Display user message in chat message container
+    with st.chat_message("user"):
+        st.markdown(prompt)
+    # Add user message to chat history
+    st.session_state.messages.append({"role": "user", "content": prompt})
+
+
+    # Display assistant response in chat message container
+    with st.chat_message("assistant"):
+        st_callback = StreamlitCallbackHandler(st.container())
+        # st_callback =stream_handler
+
+
+        # stream = client.completions.create(
+        #     model="google/gemma-7b-it",
+        #     prompt="You are a helpful agent in a question answer exhange. Give you best answer to the questions. {prompt}",
+        #     # messages=[
+        #     #     {"role": m["role"], "content": m["content"]}
+        #     #     for m in st.session_state.messages
+        #     # ],
+        #     temperature=0.5,
+        #     stream=True,
+        #     max_tokens=3000
+        # )
+
+
+        stream = client.chat.completions.create(
+            model=model_links[selected_model],#"google/gemma-7b-it",
+            messages=[
+                {"role": m["role"], "content": m["content"]}
+                for m in st.session_state.messages
+            ],
+            temperature=0.5,
+            stream=True,
+            max_tokens=3000,
+        )
+
+        response = st.write_stream(stream)
+    st.session_state.messages.append({"role": "assistant", "content": response})