Update app.py

app.py

@@ -1,13 +1,11 @@
 import streamlit as st
 from transformers import pipeline
 
-# 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".
+# Choose a Flan-T5 model. "google/flan-t5-large" is a decent balance for CPU.
+# If it's slow, try "google/flan-t5-base". For higher quality, "google/flan-t5-xl" or "google/flan-ul2" (if resources allow).
 model_name = "google/flan-t5-large"
 
-# Create a text2text-generation pipeline with some sampling parameters.
+# Create a text2text-generation pipeline with sampling parameters for some creativity.
 pipe = pipeline(
     "text2text-generation",
     model=model_name,
@@ -16,53 +14,27 @@ 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.
+# A "system prompt" that instructs the model to provide structured, elaborate financial guidance.
+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.
-"""
+Ensure your tone is clear, professional, and detailed."""
 
-# Initialize conversation history in session_state
-if "history" not in st.session_state:
-    st.session_state.history = []
+# Streamlit UI
+st.title("Flan-T5 Financial Advisor")
+st.write("Ask a financial question and receive a single, detailed response.")
 
-st.title("Financial Guidance Chatbot (Flan-T5)")
-st.write("Ask your financial questions, and the assistant will respond with structured, elaborate answers.")
+user_input = st.text_area("Enter your financial question here:")
 
-# User input
-user_input = st.text_input("Type your question or message here:")
-
-if st.button("Send"):
+if st.button("Generate"):
     if user_input.strip():
-        # 1) Add user's message to the conversation history
-        st.session_state.history.append(("User", user_input))
-
-        # 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:"
-
-        # 3) Generate a reply
-        output = pipe(
-            conversation_text,
-            max_length=300  # Increase for more elaborate answers
-        )
-        # Flan-T5 returns a list of dicts with "generated_text"
-        assistant_reply = output[0]["generated_text"].strip()
-
-        # 4) Add assistant's reply to the history
-        st.session_state.history.append(("Assistant", assistant_reply))
-
-# Display the conversation
-st.write("### Conversation")
-for speaker, text in st.session_state.history:
-    st.write(f"**{speaker}:** {text}")
-
-# Clear Chat button
-if st.button("Clear Chat"):
-    st.session_state.history = []
+        # 1) Build a single-turn prompt with system instructions + user question
+        prompt = f"{system_prompt}\n\nUser: {user_input}\nAssistant:"
+        
+        # 2) Generate a reply
+        output = pipe(prompt, max_length=300)
+        answer = output[0]["generated_text"].strip()
+        
+        # 3) Display the model's answer
+        st.write("### Assistant's Response")
+        st.write(answer)