Update model_consumer.py

model_consumer.py

@@ -1,14 +1,33 @@
+import streamlit as st
 from transformers import pipeline
 
-# Replace with your actual model repo ID
-#model_id = "prd101-wd/phi1_5-bankingqa-merged"
+# model repo ID
+model_id = "prd101-wd/phi1_5-bankingqa-merged"
 
+# Load model only once
+@st.cache_resource
+def load_model():
+    return pipeline("question-answering", model=model_id)
+                    
 # Create a text generation pipeline
-pipe = pipeline("question-answering", model="prd101-wd/phi1_5-bankingqa-merged")
+pipe = load_model()
 
-# Test the model with a prompt
-prompt = "I want to open an account with you. What do I need to do?"
-result = pipe(prompt, max_new_tokens=20)
+# Streamlit UI
+st.title("Banking HelpDesk from Finetuned Phi1-5")
 
-# Print the generated text
-print(result[0]["generated_text"])
+st.markdown("Ask a question and the fine-tuned Phi-1.5 model will answer.")
+
+user_input = st.text_area("Your question:", height=100)
+
+if st.button("Ask"):
+    if user_input.strip():
+        # Format the prompt like Alpaca-style
+        prompt = f"### Instruction:\n{user_input}\n\n### Response:\n"
+        output = pipe(prompt, max_new_tokens=200, do_sample=True)[0]["generated_text"]
+
+        # Extract only the model's response (remove prompt part if included in output)
+        answer = output.split("### Response:")[-1].strip()
+        st.markdown("### HelpdeskBot Answer:")
+        st.success(answer)
+    else:
+        st.warning("Please enter a question.")