init

README.md

@@ -1,13 +1,13 @@
----
-title: Groq Llama3 Developer
-emoji: 🚀
-colorFrom: gray
-colorTo: blue
-sdk: streamlit
-sdk_version: 1.33.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: Groq-LLaMA3.1
+emoji: 📚
+colorFrom: yellow
+colorTo: blue
+sdk: streamlit
+sdk_version: 1.33.0
+app_file: app.py
+pinned: false
+license: mit
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,128 @@
+import os
+from dotenv import find_dotenv, load_dotenv
+import streamlit as st
+from typing import Generator
+from groq import Groq
+
+_ = load_dotenv(find_dotenv())
+st.set_page_config(page_icon="📃", layout="wide", page_title="Groq & LLaMA3.1 Chat Bot...")
+
+
+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,
+    )
+
+
+# icon("⚡️")
+
+st.subheader("Groq Chat with LLaMA3.1 App", divider="rainbow", anchor=False)
+
+client = Groq(
+    api_key=os.environ['GROQ_API_KEY'],
+)
+
+# Initialize chat history and selected model
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+
+if "selected_model" not in st.session_state:
+    st.session_state.selected_model = None
+
+# Define model details
+models = {
+    "llama-3.1-70b-versatile": {"name": "LLaMA3.1-70b", "tokens": 4096, "developer": "Meta"},
+    "llama-3.1-8b-instant": {"name": "LLaMA3.1-8b", "tokens": 4096, "developer": "Meta"},
+    "llama3-70b-8192": {"name": "Meta Llama 3 70B", "tokens": 4096, "developer": "Meta"},
+    "llama3-8b-8192": {"name": "Meta Llama 3 8B", "tokens": 4096, "developer": "Meta"},
+    "llama3-groq-70b-8192-tool-use-preview": {"name": "Llama 3 Groq 70B Tool Use (Preview)", "tokens": 4096, "developer": "Groq"},
+    "gemma-7b-it": {"name": "Gemma-7b-it", "tokens": 4096, "developer": "Google"},
+    "mixtral-8x7b-32768": {
+        "name": "Mixtral-8x7b-Instruct-v0.1",
+        "tokens": 32768,
+        "developer": "Mistral",
+    },
+}
+
+# Layout for model selection and max_tokens slider
+col1, col2 = st.columns([1, 3])  # Adjust the ratio to make the first column smaller
+
+
+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
+    )
+    max_tokens_range = models[model_option]["tokens"]
+    max_tokens = st.slider(
+        "Max Tokens:",
+        min_value=512,
+        max_value=max_tokens_range,
+        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}",
+    )
+
+# 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
+
+# Add a "Clear Chat" button
+if st.button("Clear Chat"):
+    st.session_state.messages = []
+    
+# 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"])
+
+
+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..."):
+    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}
+        )

requirements.txt

@@ -0,0 +1,3 @@
+streamlit
+groq
+python-dotenv