Create app.py

app.py

@@ -0,0 +1,137 @@
+import streamlit as st
+import openai
+import requests
+
+st.set_page_config(page_title="CodeLlama Playground - via DeepInfra", page_icon='🦙')
+
+MODEL_IMAGES = {
+    "meta-llama/Meta-Llama-3-8B-Instruct": "https://em-content.zobj.net/source/twitter/376/llama_1f999.png",  # Add the emoji for the Meta-Llama model
+    "codellama/CodeLlama-34b-Instruct-hf": "https://em-content.zobj.net/source/twitter/376/llama_1f999.png",
+    "mistralai/Mistral-7B-Instruct-v0.1": "https://em-content.zobj.net/source/twitter/376/tornado_1f32a-fe0f.png",
+    "mistralai/Mixtral-8x7B-Instruct-v0.1": "https://em-content.zobj.net/source/twitter/376/tornado_1f32a-fe0f.png",
+}
+
+# Create a mapping from formatted model names to their original identifiers
+def format_model_name(model_key):
+    parts = model_key.split('/')
+    model_name = parts[-1]  # Get the last part after '/'
+    name_parts = model_name.split('-')
+
+    # Custom formatting for specific models
+    if "Meta-Llama-3-8B-Instruct" in model_key:
+        return "Llama 3 8B-Instruct"
+    else:
+        # General formatting for other models
+        formatted_name = ' '.join(name_parts[:-2]).title()  # Join them into a single string with title case
+        return formatted_name
+
+formatted_names_to_identifiers = {
+    format_model_name(key): key for key in MODEL_IMAGES.keys()
+}
+
+# Debug to ensure names are formatted correctly
+#st.write("Formatted Model Names to Identifiers:", formatted_names_to_identifiers)
+
+selected_formatted_name = st.sidebar.radio(
+    "Select LLM Model",
+    list(formatted_names_to_identifiers.keys())
+)
+
+selected_model = formatted_names_to_identifiers[selected_formatted_name]
+
+if MODEL_IMAGES[selected_model].startswith("http"):
+    st.image(MODEL_IMAGES[selected_model], width=90)
+else:
+    st.write(f"Model Icon: {MODEL_IMAGES[selected_model]}", unsafe_allow_html=True)
+
+# Display the selected model using the formatted name
+model_display_name = selected_formatted_name  # Already formatted
+# st.write(f"Model being used: `{model_display_name}`")
+
+st.sidebar.markdown('---')
+
+API_KEY = st.secrets["api_key"]
+
+openai.api_base = "https://api.deepinfra.com/v1/openai"
+MODEL_CODELLAMA = selected_model
+
+def get_response(api_key, model, user_input, max_tokens, top_p):
+    openai.api_key = api_key
+    try:
+        if "meta-llama/Meta-Llama-3-8B-Instruct" in model:
+            # Assume different API setup for Meta-Llama
+            chat_completion = requests.post(
+                "https://api.deepinfra.com/v1/openai/chat/completions",
+                headers={"Authorization": f"Bearer {api_key}"},
+                json={
+                    "model": model,
+                    "messages": [{"role": "user", "content": user_input}],
+                    "max_tokens": max_tokens,
+                    "top_p": top_p
+                }
+            ).json()
+            return chat_completion['choices'][0]['message']['content'], None
+        else:
+            # Existing setup for other models
+            chat_completion = openai.ChatCompletion.create(
+                model=model,
+                messages=[{"role": "user", "content": user_input}],
+                max_tokens=max_tokens,
+                top_p=top_p
+            )
+            return chat_completion.choices[0].message.content, None
+    except Exception as e:
+        return None, str(e)
+
+
+
+# Adjust the title based on the selected model
+st.header(f"`{model_display_name}` Model")
+
+with st.expander("About this app"):
+    st.write(f"""
+    This Chatbot app allows users to interact with various models including the new LLM models hosted on DeepInfra's OpenAI compatible API.
+    For more info, you can refer to [DeepInfra's documentation](https://deepinfra.com/docs/advanced/openai_api).
+
+    💡 For decent answers, you'd want to increase the `Max Tokens` value from `100` to `500`. 
+    """)
+
+if "api_key" not in st.session_state:
+    st.session_state.api_key = ""
+
+with st.sidebar:
+    max_tokens = st.slider('Max Tokens', 10, 500, 100)
+    top_p = st.slider('Top P', 0.0, 1.0, 0.5, 0.05)
+
+if max_tokens > 100:
+    user_provided_api_key = st.text_input("👇 Your DeepInfra API Key", value=st.session_state.api_key, type='password')
+    if user_provided_api_key:
+        st.session_state.api_key = user_provided_api_key
+    if not st.session_state.api_key:
+        st.warning("❄️ If you want to try this app with more than `100` tokens, you must provide your own DeepInfra API key. Get yours here → https://deepinfra.com/dash/api_keys")
+
+if max_tokens <= 100 or st.session_state.api_key:
+    if "messages" not in st.session_state:
+        st.session_state.messages = [{"role": "assistant", "content": "How may I assist you today?"}]
+        
+    for message in st.session_state.messages:
+        with st.chat_message(message["role"]):
+            st.write(message["content"])
+    
+    if prompt := st.chat_input():
+        st.session_state.messages.append({"role": "user", "content": prompt})
+        with st.chat_message("assistant"):
+            with st.spinner("Thinking..."):
+                response, error = get_response(st.session_state.api_key, MODEL_CODELLAMA, prompt, max_tokens, top_p)
+                if error:
+                    st.error(f"Error: {error}") 
+                else:
+                    placeholder = st.empty()
+                    placeholder.markdown(response)
+                    message = {"role": "assistant", "content": response}
+                    st.session_state.messages.append(message)
+
+# Clear chat history function and button
+def clear_chat_history():
+    st.session_state.messages = [{"role": "assistant", "content": "How may I assist you today?"}]
+st.sidebar.button('Clear Chat History', on_click=clear_chat_history)