OpenAI key on sidebar

.gitignore

@@ -1 +1 @@
-app_v2.py
+app_backup.py

app.py

@@ -1,75 +1,76 @@
-# ©2024 Intel Corporation
-# Permission is granted for recipient to internally use and modify this software for purposes of benchmarking and testing on Intel architectures. 
-# This software is provided "AS IS" possibly with faults, bugs or errors; it is not intended for production use, and recipient uses this design at their own risk with no liability to Intel.
-# Intel disclaims all warranties, express or implied, including warranties of merchantability, fitness for a particular purpose, and non-infringement. 
-# Recipient agrees that any feedback it provides to Intel about this software is licensed to Intel for any purpose worldwide. No permission is granted to use Intel’s trademarks.
-# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the code.
-
-# Import necessary libraries
-
-import streamlit as st
-import os
-from openai import OpenAI
-import json
-
-
-
-working_dir = os.path.dirname(os.path.abspath(__file__))
-endpoint_data = json.load(open(f"{working_dir}/model_info.json"))
-
-def clear_chat():
-    st.session_state.messages = []
-
-st.title("Intel® AI for Enterprise Inference - Chatbot")
-
-# Extract the keys (model names) from the JSON data
-model_names = list(endpoint_data.keys())
-
-with st.sidebar:
-    modelname = st.selectbox("Select a LLM model (Hosted by DENVR DATAWORKS) ", model_names)
-    st.write(f"You selected: {modelname}")
-    st.button("Start New Chat", on_click=clear_chat)
-
-endpoint = endpoint_data[modelname]
-    
-# api_key=os.environ.get('API_KEY')
-# api_key = st.secrets["openai_apikey"]
-api_key = None
-
-if not api_key:
-    st.info("Please add your OpenAI API key to continue.")
-    st.stop()
-base_url = endpoint
-client = OpenAI(api_key=api_key, base_url=base_url)
-
-# Extract the model name
-models = client.models.list()
-modelname = models.data[0].id
-
-
-if "messages" not in st.session_state:
-    st.session_state.messages = []
-
-for message in st.session_state.messages:
-    with st.chat_message(message["role"]):
-        st.markdown(message["content"])
-
-if prompt := st.chat_input("What is up?"):
-    st.session_state.messages.append({"role": "user", "content": prompt})
-    with st.chat_message("user"):
-        st.markdown(prompt)
-
-    with st.chat_message("assistant"):
-        stream = client.chat.completions.create(
-            model=modelname,
-            messages=[
-                {"role": m["role"], "content": m["content"]}
-                for m in st.session_state.messages
-            ],
-            max_tokens=5000,
-            stream=True,
-        )
-        response = st.write_stream(stream)
-    st.session_state.messages.append({"role": "assistant", "content": response})
-
-
+# Import necessary libraries
+
+import streamlit as st
+import os
+from openai import OpenAI
+import json
+
+working_dir = os.path.dirname(os.path.abspath(__file__))
+endpoint_data = json.load(open(f"{working_dir}/model_info.json"))
+
+def clear_chat():
+    st.session_state.messages = []
+
+st.title("Intel® AI for Enterprise Inference \n Chatbot")
+
+# Extract the keys (model names) from the JSON data
+model_names = list(endpoint_data.keys())
+
+with st.sidebar:
+    modelname = st.selectbox("Select a LLM model (Running on Intel® Gaudi®) ", model_names)
+    st.write(f"You selected: {modelname}")
+    st.button("Start New Chat", on_click=clear_chat)
+
+    # Add a text input for the API key
+    api_key = st.text_input("Enter your API Key", type="password")
+    if api_key:
+        st.session_state.api_key = api_key    
+
+# Check if the API key is provided
+if "api_key" not in st.session_state or not st.session_state.api_key:
+    st.error("Please enter your API Key in the sidebar.")
+else:
+    try:
+        endpoint = endpoint_data[modelname]
+        
+        api_key = st.session_state.api_key
+        base_url = endpoint
+        client = OpenAI(api_key=api_key, base_url=base_url)
+
+        # Extract the model name
+        models = client.models.list()
+        modelname = models.data[0].id
+
+        if "messages" not in st.session_state:
+            st.session_state.messages = []
+
+        for message in st.session_state.messages:
+            with st.chat_message(message["role"]):
+                st.markdown(message["content"])
+
+        if prompt := st.chat_input("What is up?"):
+            st.session_state.messages.append({"role": "user", "content": prompt})
+            with st.chat_message("user"):
+                st.markdown(prompt)
+
+            with st.chat_message("assistant"):
+                try:
+                    stream = client.chat.completions.create(
+                        model=modelname,
+                        messages=[
+                            {"role": m["role"], "content": m["content"]}
+                            for m in st.session_state.messages
+                        ],
+                        max_tokens=1024,
+                        stream=True,
+                    )
+                    response = st.write_stream(stream)
+                except Exception as e:
+                    st.error(f"An error occurred while generating the response: {e}")
+                    response = "An error occurred while generating the response."
+
+            st.session_state.messages.append({"role": "assistant", "content": response})
+    except KeyError as e:
+        st.error(f"Key error: {e}")
+    except Exception as e:
+        st.error(f"An unexpected error occurred: {e}")