Update app.py

app.py

@@ -7,14 +7,14 @@ import datetime
 import json
 
 _ = load_dotenv(find_dotenv())
-st.set_page_config(page_icon="💬", layout="wide", page_title="...")
+st.set_page_config(page_icon="", layout="wide", page_title="...")
 
 def icon(emoji: str):
-    """Shows an emoji as a Notion-style page icon."""
-    st.write(
-        f'<span style="font-size: 78px; line-height: 1">{emoji}</span>',
-        unsafe_allow_html=True,
-    )
+  """Shows an emoji as a Notion-style page icon."""
+  st.write(
+    f'<span style="font-size: 78px; line-height: 1">{emoji}</span>',
+    unsafe_allow_html=True,
+  )
 
 
 icon("⚡")
@@ -22,118 +22,117 @@ icon("⚡")
 st.subheader("Chatbot", divider="rainbow", anchor=False)
 
 client = Groq(
-    api_key=os.environ['GROQ_API_KEY'],
+  api_key=os.environ['GROQ_API_KEY'],
 )
 
 # Read saved prompts from file
 with open("saved_prompts.txt", "r") as f:
-    saved_prompts = f.read().split("<|>")
+  saved_prompts = f.read().split("<|>")
 
 prompt_names = [p.split(" ", 1)[0] for p in saved_prompts]
 prompt_map = {name: prompt for name, prompt in zip(prompt_names, saved_prompts)}
 
 # Initialize chat history and selected model
 if "messages" not in st.session_state:
-    st.session_state.messages = []
+  st.session_state.messages = []
 
 if "selected_model" not in st.session_state:
-    st.session_state.selected_model = None
+  st.session_state.selected_model = None
 
 # Define model details
 models = {
-    "mixtral-8x7b-32768": {
-        "name": "Mixtral-8x7b-Instruct-v0.1",
-        "tokens": 32768,
-        "developer": "Mistral",
-    },
-    "gemma-7b-it": {"name": "Gemma-7b-it", "tokens": 8192, "developer": "Google"},
-    "llama2-70b-4096": {"name": "LLaMA2-70b-chat", "tokens": 4096, "developer": "Meta"},
-    "llama3-70b-8192": {"name": "LLaMA3-70b-8192", "tokens": 8192, "developer": "Meta"},
-    "llama3-8b-8192": {"name": "LLaMA3-8b-8192", "tokens": 8192, "developer": "Meta"},
+  "mixtral-8x7b-32768": {
+    "name": "Mixtral-8x7b-Instruct-v0.1",
+    "tokens": 32768,
+    "developer": "Mistral",
+  },
+  "gemma-7b-it": {"name": "Gemma-7b-it", "tokens": 8192, "developer": "Google"},
+  "llama2-70b-4096": {"name": "LLaMA2-70b-chat", "tokens": 4096, "developer": "Meta"},
+  "llama3-70b-8192": {"name": "LLaMA3-70b-8192", "tokens": 8192, "developer": "Meta"},
+  "llama3-8b-8192": {"name": "LLaMA3-8b-8192", "tokens": 8192, "developer": "Meta"},
 }
 
 # Layout for model selection and max_tokens slider
 col1, col2 = st.columns(2)
 
 with col1:
-    model_option = st.selectbox(
-        "Choose a model:",
-        options=list(models.keys()),
-        format_func=lambda x: models[x]["name"],
-        index=0,  # Default to the first model in the list
-    )
-# Add prompt dropdown
-prompt_option = st.selectbox("Choose a prompt:", options=prompt_names)
-
-if not prompt_option:
-    prompt = ""
-else:
-    prompt = prompt_map[prompt_option]
+  def update_prompt(selected_prompt):  # Callback function for dropdown
+    global prompt
+    prompt = prompt_map[selected_prompt]
+
+  prompt_option = st.selectbox(
+    "Choose a prompt:",
+    options=list(models.keys()),
+    format_func=lambda x: models[x]["name"],
+    index=0,  # Default to the first model in the list
+    on_change=update_prompt,  # Call update_prompt on selection change
+  )
+
+# Chat input without value argument
+if prompt := st.chat_input("Enter your prompt here..."):
+  st.session_state.messages.append({"role": "user", "content": prompt})
+
+  with st.chat_message("user", avatar="❓"):
+    st.markdown(prompt)
 
 # Detect model change and clear chat history if model has changed
 if st.session_state.selected_model != model_option:
-    st.session_state.messages = []
-    st.session_state.selected_model = model_option
+  st.session_state.messages = []
+  st.session_state.selected_model = model_option
 
 max_tokens_range = models[model_option]["tokens"]
 
 with col2:
-    # Adjust max_tokens slider dynamically based on the selected model
-    max_tokens = st.slider(
-        "Max Tokens:",
-        min_value=512,  # Minimum value to allow some flexibility
-        max_value=max_tokens_range,
-        # Default value or max allowed if less
-        value=min(32768, max_tokens_range),
-        step=512,
-        help=f"Adjust the maximum number of tokens (words) for the model's response. Max for selected model: {max_tokens_range}",
-    )
+  # Adjust max_tokens slider dynamically based on the selected model
+  max_tokens = st.slider(
+    "Max Tokens:",
+    min_value=512,  # Minimum value to allow some flexibility
+    max_value=max_tokens_range,
+    # Default value or max allowed if less
+    value=min(32768, max_tokens_range),
+    step=512,
+    help=f"Adjust the maximum number of tokens (words) for the model's response. Max for selected model: {max_tokens_range}",
+  )
 
 # Display chat messages from history on app rerun
 for message in st.session_state.messages:
-    avatar = "🧠" if message["role"] == "assistant" else "❓"
-    with st.chat_message(message["role"], avatar=avatar):
-        st.markdown(message["content"])
+  avatar = "" if message["role"] == "assistant" else "❓"
+  with st.chat_message(message["role"], avatar=avatar):
+    st.markdown(message["content"])
 
 def generate_chat_responses(chat_completion) -> Generator[str, None, None]:
-    """Yield chat response content from the Groq API response."""
-    for chunk in chat_completion:
-        if chunk.choices[0].delta.content:
-            yield chunk.choices[0].delta.content
-
-if prompt := st.chat_input("Enter your prompt here...", value=prompt):
-    st.session_state.messages.append({"role": "user", "content": prompt})
-
-    with st.chat_message("user", avatar="❓"):  
-        st.markdown(prompt)
-
-    # Fetch response from Groq API
-    try:
-        chat_completion = client.chat.completions.create(
-            model=model_option,
-            messages=[
-                {"role": m["role"], "content": m["content"]}
-                for m in st.session_state.messages
-            ],
-            max_tokens=max_tokens,
-            stream=True,
-        )
-
-        # Use the generator function with st.write_stream
-        with st.chat_message("assistant", avatar="🧠"):
-            chat_responses_generator = generate_chat_responses(chat_completion)
-            full_response = st.write_stream(chat_responses_generator)
-    except Exception as e:
-        st.error(e, icon="🚨")
-
-    # Append the full response to session_state.messages
-    if isinstance(full_response, str):
-        st.session_state.messages.append(
-            {"role": "assistant", "content": full_response}
-        )
-    else:
-        # Handle the case where full_response is not a string
-        combined_response = "\n".join(str(item) for item in full_response)
-        st.session_state.messages.append(
-            {"role": "assistant", "content": combined_response}
-        )
+  """Yield chat response content from the Groq API response."""
+  for chunk in chat_completion:
+    if chunk.choices[0].delta.content:
+      yield chunk.choices[0].delta.content
+
+# Fetch response from Groq API
+try:
+  chat_completion = client.chat.completions.create(
+    model=model_option,
+    messages=[
+      {"role": m["role"], "content": m["content"]}
+      for m in st.session_state.messages
+    ],
+    max_tokens=max_tokens,
+    stream=True,
+  )
+
+  # Use the generator function with st.write_stream
+  with st.chat_message("assistant", avatar=""):
+    chat_responses_generator = generate_chat_responses(chat_completion)
+    full_response = st.write_stream(chat_responses_generator)
+except Exception as e:
+  st.error(e, icon="")
+
+# Append the full response to session_state.messages
+if isinstance(full_response, str):
+  st.session_state.messages.append(
+    {"role": "assistant", "content": full_response}
+  )
+else:
+  # Handle the case where full_response is not a string
+  combined_response = "\n".join(str(item) for item in full_response)
+  st.session_state.messages.append(
+    {"role": "assistant", "content": combined_response}
+  )