Update pages/app_langchain_completion.py

pages/app_langchain_completion.py

@@ -1,135 +1,135 @@
-import requests
-import json
-import os
-import streamlit as st
-from langchain_community.llms import Ollama
-from langchain_nvidia_ai_endpoints import ChatNVIDIA
-from langchain_groq import ChatGroq
-from langchain.chains import ConversationChain
-from langchain.memory import ConversationBufferMemory
-from clients import OllamaClient, GroqClient
-
-st.set_page_config(
-    page_title="QA Inference Streamlit App using Ollama, Nvidia and Groq with Langchain framework"
-)
-
-
-# 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 Langchain framework")
-
-
-# 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",
-}
-# hard coded because models returned by NvidiaClient().list_models() are not well formed for Langchain ChatNVIDIA class
-llms_from_nvidia = [
-    "ai-llama3-70b",
-    "ai-mistral-large",
-    "ai-gemma-7b",
-    "ai-codellama-70b",
-]
-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()
-    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"]
-elif llm_provider == "Cloud Nvidia":
-    if nvidia_api_token := st.sidebar.text_input("Enter your Nvidia API Key"):
-        os.environ["NVIDIA_API_KEY"] = nvidia_api_token
-        st.sidebar.info("nvidia authentification ok")
-        # nvidia_models = [model.model_name for model in list_nvidia_models() if (model.model_type == "chat") & (model.model_name is not None)] # list is false
-        nvidia_models = llms_from_nvidia
-        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 client #########################################
-class LlmProvider:
-    def __init__(self, provider):
-        if provider == "ollama":
-            self.llm = Ollama(
-                model=st.session_state["ollama_llm"],
-                temperature=st.session_state["temperature"],
-                max_tokens=st.session_state["max_tokens"],
-                top_p=st.session_state["top_p"],
-            )
-        elif provider == "nvidia":
-            self.llm = ChatNVIDIA(
-                model=st.session_state["nvidia_llm"],
-                temperature=st.session_state["temperature"],
-                max_tokens=st.session_state["max_tokens"],
-                top_p=st.session_state["top_p"],
-            )
-        elif provider == "groq":
-            self.llm = ChatGroq(
-                groq_api_key = groq_api_token,
-                model_name=st.session_state["groq_llm"],
-                temperature=st.session_state["temperature"],
-                max_tokens=st.session_state["max_tokens"],
-                top_p=st.session_state["top_p"],
-            )
-
-
-# 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)
-    conversation = ConversationChain(
-        llm=LlmProvider(llm_providers[st.session_state["llm_provider"]]).llm,
-        memory=ConversationBufferMemory(),
-    )
-    response = f"Echo: {prompt}"
-    # Display assistant response in chat message container
-    with st.chat_message("assistant"):
-        # response = LlmProvider1(llm_providers[llm_provider], prompt=prompt).response
-        response = conversation.invoke(prompt)["response"]
-        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 langchain_community.llms import Ollama
+from langchain_nvidia_ai_endpoints import ChatNVIDIA
+from langchain_groq import ChatGroq
+from langchain.chains import ConversationChain
+from langchain.memory import ConversationBufferMemory
+from clients import OllamaClient, GroqClient
+
+st.set_page_config(
+    page_title="QA Inference Streamlit App using Ollama, Nvidia and Groq with Langchain framework"
+)
+
+
+# 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 Langchain framework")
+
+
+# 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",
+}
+# hard coded because models returned by NvidiaClient().list_models() are not well formed for Langchain ChatNVIDIA class
+llms_from_nvidia = [
+    "ai-llama3-70b",
+    "ai-mistral-large",
+    "ai-gemma-7b",
+    "ai-codellama-70b",
+]
+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()
+    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"]
+elif llm_provider == "Cloud Nvidia":
+    if nvidia_api_token := st.sidebar.text_input("Enter your Nvidia API Key", type="password"):
+        os.environ["NVIDIA_API_KEY"] = nvidia_api_token
+        st.sidebar.info("nvidia authentification ok")
+        # nvidia_models = [model.model_name for model in list_nvidia_models() if (model.model_type == "chat") & (model.model_name is not None)] # list is false
+        nvidia_models = llms_from_nvidia
+        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 client #########################################
+class LlmProvider:
+    def __init__(self, provider):
+        if provider == "ollama":
+            self.llm = Ollama(
+                model=st.session_state["ollama_llm"],
+                temperature=st.session_state["temperature"],
+                max_tokens=st.session_state["max_tokens"],
+                top_p=st.session_state["top_p"],
+            )
+        elif provider == "nvidia":
+            self.llm = ChatNVIDIA(
+                model=st.session_state["nvidia_llm"],
+                temperature=st.session_state["temperature"],
+                max_tokens=st.session_state["max_tokens"],
+                top_p=st.session_state["top_p"],
+            )
+        elif provider == "groq":
+            self.llm = ChatGroq(
+                groq_api_key = groq_api_token,
+                model_name=st.session_state["groq_llm"],
+                temperature=st.session_state["temperature"],
+                max_tokens=st.session_state["max_tokens"],
+                top_p=st.session_state["top_p"],
+            )
+
+
+# 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)
+    conversation = ConversationChain(
+        llm=LlmProvider(llm_providers[st.session_state["llm_provider"]]).llm,
+        memory=ConversationBufferMemory(),
+    )
+    response = f"Echo: {prompt}"
+    # Display assistant response in chat message container
+    with st.chat_message("assistant"):
+        # response = LlmProvider1(llm_providers[llm_provider], prompt=prompt).response
+        response = conversation.invoke(prompt)["response"]
+        st.markdown(response)
+    # Add assistant response to chat history
+    st.session_state.messages.append({"role": "assistant", "content": response})