Upload model_consumer.py

src/model_consumer.py

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+import streamlit as st
+from transformers import pipeline
+
+# 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 = load_model()
+
+# Streamlit UI
+st.title("Banking HelpDesk from Finetuned Phi1-5")
+
+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.")