push

app.py

@@ -29,6 +29,18 @@ huggingface_ef = embedding_functions.HuggingFaceEmbeddingFunction(
     api_key=hf_token,
     model_name="sentence-transformers/all-MiniLM-L6-v2"
 )
+
+#get their investor type
+investor_type_value = ""
+
+# Function to set the investor type
+def set_investor_type(investor_type):
+    global investor_type_value
+    investor_type_value = investor_type
+    return f"Investor type set to: {investor_type}"
+
+
+
 embedding_model = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2')
 # Define global variables
 BOT_AVATAR = 'https://automatedstockmining.org/wp-content/uploads/2024/08/south-west-value-mining-logo.webp'
@@ -188,7 +200,7 @@ def process_query(user_input, history):
     # Step 1: Generate a search term based on the user query
     stream_search = client.chat_completion(
         model="Qwen/Qwen2.5-72B-Instruct",
-        messages=[{"role": "user", "content": f"Based on this chat history {history} the user's request '{user_input}', and this vector database {search_texts}, suggest a Google search term in a single line without specific dates; use 'this year', 'this month', etc. INCLUDE NOTHING IN YOUR RESPONSE EXCEPT THE RELEVANT SEARCH RESULT. EXAMPLE: USER: WHAT IS THE CURRENT PRICE OF COCA COLA STOCK. YOUR RESPONSE: WHAT IS THE CURRENT PRICE OF COCA COLA STOCK"}],
+        messages=[{"role": "user", "content": f"Based on this chat history {history} the user's request '{user_input}', and this vector database {search_texts}, suggest a Google search term in a single line without specific dates; use 'this year', 'this month', etc. INCLUDE NOTHING IN YOUR RESPONSE EXCEPT THE RELEVANT SEARCH RESULT. EXAMPLE: USER: WHAT IS THE CURRENT PRICE OF COCA COLA STOCK. YOUR RESPONSE: WHAT IS THE CURRENT PRICE OF COCA COLA STOCK. here is their investor type, you can use it to specialise searches for their investor type{investor_type_value}"}],
         max_tokens=400,
         stream=True
     )
@@ -211,7 +223,7 @@ def process_query(user_input, history):
     # Step 3: Generate a response using the search results
     response = client.chat_completion(
         model="Qwen/Qwen2.5-72B-Instruct",
-        messages=[{"role": "user", "content": f"Using the search results: {search_results_str} and chat history {history}, this vector database on health checks {retrieved_texts} answer the user's query '{user_input}' in a concise, precise way,  using numerical data if available. ONLY GIVE ONE RESPONSE BACK, CONCISE OR DETAILED BASED ON THE USERS INPUT. "}],
+        messages=[{"role": "user", "content": f"Using the search results: {search_results_str} and chat history {history}, this vector database on health checks {retrieved_texts} answer the user's query '{user_input}' in a concise, precise way,  using numerical data if available. GIVE DETAILED RESPONSES LIKE A STOCK ANALYST. THEY HAVE TOLD US HOW THEY INVEST SO WHEN YOU ANALYSE THINGS AND EXPLAIN, TAILOR IT TO THEM, AND REFERENCE THEIR TYPE IN YOUR ANSWERS - {investor_type_value}"}],
         max_tokens=3000,
         stream=True
     )
@@ -226,8 +238,9 @@ def process_query(user_input, history):
         yield final_response
 
 theme = gr.themes.Citrus(
-    primary_hue="blue",
-    neutral_hue="slate",
+    primary_hue="indigo",
+    secondary_hue="indigo",
+    neutral_hue="zinc",
 )
 
 examples = [
@@ -258,17 +271,20 @@ examples = [
 ]
 
 chatbot = gr.Chatbot(
-    label="IM.S",
+    label="IM.analyst",
     avatar_images=[None, BOT_AVATAR],
     show_copy_button=True,
     layout="panel",
     height=700
 )
-theme = gr.themes.Ocean()
+
 with gr.Blocks(theme=theme) as demo:
     with gr.Column():
         gr.Markdown("## quantineuron.com: IM.analyst - Building the Future of Investing")
-    
+        investor_type_input = gr.Textbox(label="tell IM.analyst about how you invest", placeholder="Enter your investment style", interactive=True)
+        set_type_button = gr.Button("Set Investor Type")
+        set_type_button.click(set_investor_type, inputs=investor_type_input, outputs=None)
+          
     with gr.Column(scale=3, min_width=600):
         chat_interface = gr.ChatInterface(
             fn=process_query,