Update pages/app_api_completion.py

pages/app_api_completion.py

@@ -1,128 +1,128 @@
-import requests
-import json
-import os
-import streamlit as st
-from clients import OllamaClient, NvidiaClient, GroqClient
-
-st.set_page_config(
-    page_title="QA Inference Streamlit App using Ollama, Nvidia and Groq APIs"
-)
-
-
-# Cache the header of the app to prevent re-rendering on each load
-@st.cache_resource
-def display_app_header():
-    """Display the header of the Streamlit app."""
-    st.title("QA Inference with Ollama & Nvidia & Groq as LLMs providers")
-    st.subheader("ChatBot based on provider's OpenAI-like APIs and clients")
-
-
-# Display the header of the app
-display_app_header()
-
-# UI sidebar ##########################################
-st.sidebar.subheader("Models")
-
-# LLM
-llm_providers = {
-    "Local Ollama": "ollama",
-    "Cloud Nvidia": "nvidia",
-    "Cloud Groq": "groq",
-}
-llm_provider = st.sidebar.radio(
-    "Choose your LLM Provider", llm_providers.keys(), key="llm_provider"
-)
-if llm_provider == "Local Ollama":
-    ollama_list_models = OllamaClient().list_models()
-    if ollama_list_models:
-        ollama_models = [x["name"] for x in ollama_list_models["models"]]
-        ollama_llm = st.sidebar.radio(
-        "Select your Ollama model", ollama_models, key="ollama_llm"
-        )  # retrive with st.session_state["ollama_llm"]
-    else:
-        st.sidebar.error('Ollama is not running')
-elif llm_provider == "Cloud Nvidia":
-    if nvidia_api_token := st.sidebar.text_input("Enter your Nvidia API Key"):
-        st.sidebar.info("Nvidia authentification ok")
-        nvidia_list_models = NvidiaClient().list_models() # api_key is not needed to list the available models
-        nvidia_models = [x["id"] for x in nvidia_list_models["data"]]
-        nvidia_llm = st.sidebar.radio(
-            "Select your Nvidia LLM", nvidia_models, key="nvidia_llm"
-        )
-    else:
-        st.sidebar.warning("You must enter your Nvidia API key")
-elif llm_provider == "Cloud Groq":
-    if groq_api_token := st.sidebar.text_input("Enter your Groq API Key"):
-        st.sidebar.info("Groq authentification ok")
-        groq_list_models = GroqClient(api_key=groq_api_token).list_models()
-        groq_models = [x["id"] for x in groq_list_models["data"]]
-        groq_llm = st.sidebar.radio("Choose your Groq LLM", groq_models, key="groq_llm")
-    else:
-        st.sidebar.warning("You must enter your Groq API key")
-
-# LLM parameters
-st.sidebar.subheader("Parameters")
-max_tokens = st.sidebar.number_input("Token numbers", value=1024, key="max_tokens")
-temperature = st.sidebar.slider(
-    "Temperature", min_value=0.0, max_value=1.0, value=0.5, step=0.1, key="temperature"
-)
-top_p = st.sidebar.slider(
-    "Top P", min_value=0.0, max_value=1.0, value=0.7, step=0.1, key="top_p"
-)
-
-
-# LLM response function ########################################
-def get_llm_response(provider, prompt):
-    options = dict(
-        max_tokens=st.session_state["max_tokens"],
-        top_p=st.session_state["top_p"],
-        temperature=st.session_state["temperature"],
-    )
-    if provider == "ollama":
-        return OllamaClient(
-            api_key="ollama",
-            model=st.session_state["ollama_llm"],
-        ).api_chat_completion(
-            prompt, **options
-        )  # or .client_chat_completion(prompt,**options)
-    elif provider == "nvidia":
-        return NvidiaClient(
-            api_key=nvidia_api_token,
-            model=st.session_state["nvidia_llm"],
-        ).api_chat_completion(
-            prompt, **options
-        )  # or .client_chat_completion(prompt,**options)
-    elif provider == "groq":
-        return GroqClient(
-            api_key=groq_api_token,
-            model=st.session_state["groq_llm"],
-        ).api_chat_completion(
-            prompt, **options
-        )  # or .client_chat_completion(prompt,**options)
-
-
-# UI main #####################################################
-# 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"])
-
-# React to 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})
-
-    response = f"Echo: {prompt}"
-    # Display assistant response in chat message container
-    with st.chat_message("assistant"):
-        response = get_llm_response(llm_providers[st.session_state["llm_provider"]], prompt)
-        st.markdown(response)
-    # Add assistant response to chat history
-    st.session_state.messages.append({"role": "assistant", "content": response})
+import requests
+import json
+import os
+import streamlit as st
+from clients import OllamaClient, NvidiaClient, GroqClient
+
+st.set_page_config(
+    page_title="QA Inference Streamlit App using Ollama, Nvidia and Groq APIs"
+)
+
+
+# Cache the header of the app to prevent re-rendering on each load
+@st.cache_resource
+def display_app_header():
+    """Display the header of the Streamlit app."""
+    st.title("QA Inference with Ollama & Nvidia & Groq as LLMs providers")
+    st.subheader("ChatBot based on provider's OpenAI-like APIs and clients")
+
+
+# Display the header of the app
+display_app_header()
+
+# UI sidebar ##########################################
+st.sidebar.subheader("Models")
+
+# LLM
+llm_providers = {
+    "Local Ollama": "ollama",
+    "Cloud Nvidia": "nvidia",
+    "Cloud Groq": "groq",
+}
+llm_provider = st.sidebar.radio(
+    "Choose your LLM Provider", llm_providers.keys(), key="llm_provider"
+)
+if llm_provider == "Local Ollama":
+    ollama_list_models = OllamaClient().list_models()
+    if ollama_list_models:
+        ollama_models = [x["name"] for x in ollama_list_models["models"]]
+        ollama_llm = st.sidebar.radio(
+        "Select your Ollama model", ollama_models, key="ollama_llm"
+        )  # retrive with st.session_state["ollama_llm"]
+    else:
+        st.sidebar.error('Ollama is not running')
+elif llm_provider == "Cloud Nvidia":
+    if nvidia_api_token := st.sidebar.text_input("Enter your Nvidia API Key", type="password"):
+        st.sidebar.info("Nvidia authentification ok")
+        nvidia_list_models = NvidiaClient().list_models() # api_key is not needed to list the available models
+        nvidia_models = [x["id"] for x in nvidia_list_models["data"]]
+        nvidia_llm = st.sidebar.radio(
+            "Select your Nvidia LLM", nvidia_models, key="nvidia_llm"
+        )
+    else:
+        st.sidebar.warning("You must enter your Nvidia API key")
+elif llm_provider == "Cloud Groq":
+    if groq_api_token := st.sidebar.text_input("Enter your Groq API Key", type="password"):
+        st.sidebar.info("Groq authentification ok")
+        groq_list_models = GroqClient(api_key=groq_api_token).list_models()
+        groq_models = [x["id"] for x in groq_list_models["data"]]
+        groq_llm = st.sidebar.radio("Choose your Groq LLM", groq_models, key="groq_llm")
+    else:
+        st.sidebar.warning("You must enter your Groq API key")
+
+# LLM parameters
+st.sidebar.subheader("Parameters")
+max_tokens = st.sidebar.number_input("Token numbers", value=1024, key="max_tokens")
+temperature = st.sidebar.slider(
+    "Temperature", min_value=0.0, max_value=1.0, value=0.5, step=0.1, key="temperature"
+)
+top_p = st.sidebar.slider(
+    "Top P", min_value=0.0, max_value=1.0, value=0.7, step=0.1, key="top_p"
+)
+
+
+# LLM response function ########################################
+def get_llm_response(provider, prompt):
+    options = dict(
+        max_tokens=st.session_state["max_tokens"],
+        top_p=st.session_state["top_p"],
+        temperature=st.session_state["temperature"],
+    )
+    if provider == "ollama":
+        return OllamaClient(
+            api_key="ollama",
+            model=st.session_state["ollama_llm"],
+        ).api_chat_completion(
+            prompt, **options
+        )  # or .client_chat_completion(prompt,**options)
+    elif provider == "nvidia":
+        return NvidiaClient(
+            api_key=nvidia_api_token,
+            model=st.session_state["nvidia_llm"],
+        ).api_chat_completion(
+            prompt, **options
+        )  # or .client_chat_completion(prompt,**options)
+    elif provider == "groq":
+        return GroqClient(
+            api_key=groq_api_token,
+            model=st.session_state["groq_llm"],
+        ).api_chat_completion(
+            prompt, **options
+        )  # or .client_chat_completion(prompt,**options)
+
+
+# UI main #####################################################
+# 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"])
+
+# React to 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})
+
+    response = f"Echo: {prompt}"
+    # Display assistant response in chat message container
+    with st.chat_message("assistant"):
+        response = get_llm_response(llm_providers[st.session_state["llm_provider"]], prompt)
+        st.markdown(response)
+    # Add assistant response to chat history
+    st.session_state.messages.append({"role": "assistant", "content": response})