Create app.py

app.py

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+import streamlit as st
+from transformers import pipeline
+
+# Set up the title and description of the app
+st.title("My LLM Model: Dementia Knowledge Assistant")
+st.markdown("""
+This app uses a fine-tuned **Large Language Model (LLM)** to answer questions about dementia. 
+Simply input your query, and the model will provide contextually relevant answers!
+""")
+
+# Load the model
+@st.cache_resource
+def load_qa_pipeline():
+    model_name = "rohitashva/dementia--chatbot-llm-model"  # Replace with your Hugging Face model repo name
+    qa_pipeline = pipeline("text-generation", model=model_name)
+    return qa_pipeline
+
+qa_pipeline = load_qa_pipeline()
+
+# Input field for user query
+st.header("Ask a Question")
+question = st.text_input("Enter your question about dementia (e.g., 'What are the symptoms of early-stage dementia?'):")
+
+# Context input for retrieval
+st.text_area(
+    "Provide additional context (optional):",
+    placeholder="Include any relevant context about dementia here to get better results.",
+    key="context"
+)
+
+if st.button("Get Answer"):
+    if not question:
+        st.error("Please enter a question!")
+    else:
+        # Call the QA pipeline
+        with st.spinner("Generating response..."):
+            result = qa_pipeline({
+                "question": question,
+                "context": st.session_state.context
+            })
+            answer = result.get("answer", "I don't know.")
+            confidence = result.get("score", 0.0)
+
+        # Display the result
+        st.subheader("Answer")
+        st.write(answer)
+
+        st.subheader("Confidence Score")
+        st.write(f"{confidence:.2f}")
+
+# Footer
+st.markdown("---")
+st.markdown("Deployed on **Hugging Face Spaces** using [Streamlit](https://streamlit.io/).")