Update app.py

app.py

@@ -1,13 +1,13 @@
 import streamlit as st
 from transformers import pipeline
 
-# Choose a Flan-T5 model (publicly available)
-# "google/flan-t5-base" (250M params) is faster on CPU
-# "google/flan-t5-large" (770M params) gives better replies but is slower
+# Choose a Flan-T5 model (public and instruction-tuned).
+# "google/flan-t5-large" is a good balance of performance and quality on CPU.
+# If it's slow, try "google/flan-t5-base". If you want better responses (and can handle bigger CPU load),
+# try "google/flan-t5-xl" or "google/flan-ul2".
 model_name = "google/flan-t5-large"
 
-# Create a text2text-generation pipeline with some sampling parameters for variety
-# If you prefer more deterministic outputs, remove or lower do_sample/temperature
+# Create a text2text-generation pipeline with some sampling parameters.
 pipe = pipeline(
     "text2text-generation",
     model=model_name,
@@ -16,37 +16,46 @@ pipe = pipeline(
     temperature=0.7
 )
 
+# A "system prompt" or "prebuilt prompt" that sets the context for financial guidance
+# and encourages structured, elaborate answers.
+system_prompt = """
+You are a helpful AI assistant specialized in finance. 
+You provide thorough, step-by-step, structured guidance, using bullet points or headings if relevant.
+Offer disclaimers that this is not official financial advice, but well-researched educational content.
+Whenever you respond, ensure the tone is clear, professional, and detailed.
+"""
+
 # Initialize conversation history in session_state
 if "history" not in st.session_state:
     st.session_state.history = []
 
-st.title("Flan-T5 Chatbot")
-st.write("A chat-like interface using an instruction-tuned model (Flan-T5).")
+st.title("Financial Guidance Chatbot (Flan-T5)")
+st.write("Ask your financial questions, and the assistant will respond with structured, elaborate answers.")
 
-# Collect user input
-user_input = st.text_input("Type your message here:")
+# User input
+user_input = st.text_input("Type your question or message here:")
 
-# Send button
 if st.button("Send"):
     if user_input.strip():
-        # Add user message to the history
+        # 1) Add user's message to the conversation history
         st.session_state.history.append(("User", user_input))
 
-        # Build a prompt that includes the entire conversation
-        conversation_text = ""
+        # 2) Build the full prompt (system instructions + entire conversation)
+        conversation_text = system_prompt.strip() + "\n\n"
         for speaker, text in st.session_state.history:
             conversation_text += f"{speaker}: {text}\n"
+        # The final line signals the assistant to respond
         conversation_text += "Assistant:"
 
-        # Generate a reply
+        # 3) Generate a reply
         output = pipe(
             conversation_text,
-            max_length=100  # You can adjust for longer or shorter replies
+            max_length=300  # Increase for more elaborate answers
         )
-        # Flan-T5 pipeline returns a list of dict with "generated_text"
+        # Flan-T5 returns a list of dicts with "generated_text"
         assistant_reply = output[0]["generated_text"].strip()
 
-        # Add the assistant's reply to history
+        # 4) Add assistant's reply to the history
         st.session_state.history.append(("Assistant", assistant_reply))
 
 # Display the conversation