Add api key in env for user, change model to small

app.py

@@ -7,45 +7,49 @@ from llama_index.embeddings import MistralAIEmbedding
 from llama_index import ServiceContext
 from llama_index.query_engine import RetrieverQueryEngine
 
-
-description = "Example of an assistant with Gradio and Mistral AI via its API"
-placeholder = "Ask a question"
+title = "Gaia Mistral Chat RAG URL Demo"
+description = "Example of an assistant with Gradio, RAG from url and Mistral AI via its API"
+placeholder = "Posez moi une question sur l'agriculture"
 placeholder_url = "Extract text from this url"
-placeholder_api_key = "API key"
+llm_model = 'mistral-small'
+# choose api_key from .env or from input field
+# placeholder_api_key = "API key"
+env_api_key = os.environ.get("MISTRAL_API_KEY")
 
 query_engine = None
 
 with gr.Blocks() as demo:
 
     gr.Markdown(""" ### Welcome to Gaia Level 2 Demo 
-    Add an URL and your API key at the bottom of the interface before interacting with the Chat. This demo allows you to interact with a webpage and then ask questions to Mistral APIs. Mistral will answer with the context extracted from the webpage.
+    Add an URL at the bottom of the interface before interacting with the Chat.
+    This demo allows you to interact with a webpage and then ask questions to Mistral APIs.
+    Mistral will answer with the context extracted from the webpage.
     """)
     chatbot = gr.Chatbot()
     msg = gr.Textbox()
     clear = gr.ClearButton([msg, chatbot])
 
-    with gr.Row():
-        api_key_text_box = gr.Textbox(placeholder=placeholder_api_key, container=False, scale=7)
+    # with gr.Row():
+        # api_key_text_box = gr.Textbox(placeholder=placeholder_api_key, container=False, scale=7)
 
-    def setup_with_url(url, api_key):
+    def setup_with_url(url):
         global query_engine
+
         # Set-up clients
-        llm = MistralAI(api_key=api_key,model="mistral-medium")
-        embed_model = MistralAIEmbedding(model_name='mistral-embed', api_key=api_key)
+        llm = MistralAI(api_key=env_api_key,model=llm_model)
+        embed_model = MistralAIEmbedding(model_name='mistral-embed', api_key=env_api_key)
         service_context = ServiceContext.from_defaults(chunk_size=1024, llm=llm, embed_model=embed_model)
 
         # Set-up db
         documents = SimpleWebPageReader(html_to_text=True).load_data([url])
         index = VectorStoreIndex.from_documents(documents, service_context=service_context)
         query_engine = index.as_query_engine(similarity_top_k=15)
-        return "I'm ready, please add a question here."
+        return placeholder
 
     with gr.Row():
         url_msg = gr.Textbox(placeholder=placeholder_url, container=False, scale=7)
         url_btn = gr.Button(value="Set-up API and process url ✅", interactive=True)
-        url_btn.click(setup_with_url, [url_msg, api_key_text_box], msg, show_progress= "full")
-
-    
+        url_btn.click(setup_with_url, [url_msg], msg, show_progress= "full")
 
     def respond(message, chat_history):
         response = query_engine.query(message)
@@ -54,4 +58,6 @@ with gr.Blocks() as demo:
 
     msg.submit(respond, [msg, chatbot], [chatbot])
 
+demo.title = title
+
 demo.launch()