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app.py

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+import streamlit as st
+
+# Streamlit App Title
+st.title("Query Processing App")
+
+# Query Input Section
+st.subheader("Enter your Query")
+query = st.text_area("Query:", height=100, key="query_input")
+
+# Buttons in a row
+col1, col2 = st.columns([1, 1])
+
+with col1:
+    if st.button("Submit"):
+        st.session_state.response = "Sample Response: Processed Query"
+        st.session_state.retrieved_docs = "Sample Retrieved Documents"
+        st.session_state.metrics = "Sample Metrics: Accuracy 95%"
+
+with col2:
+    if st.button("Clear"):
+        st.session_state.query_input = ""
+        st.session_state.response = ""
+        st.session_state.retrieved_docs = ""
+        st.session_state.metrics = ""
+
+# Response Text Box
+st.subheader("Response")
+st.text_area("Response:", value=st.session_state.get("response", ""), height=100, key="response_box", disabled=True)
+
+# Retrieved Documents Section
+if st.button("Show Retrieved Documents"):
+    st.session_state.retrieved_docs = "Sample Retrieved Documents"
+
+st.subheader("Retrieved Documents")
+st.text_area("Retrieved Documents:", value=st.session_state.get("retrieved_docs", ""), height=100, key="docs_box", disabled=True)
+
+# Metrics Calculation Section
+if st.button("Calculate Metrics"):
+    st.session_state.metrics = "Sample Metrics: Accuracy 95%"
+
+st.subheader("Metrics")
+st.text_area("Metrics:", value=st.session_state.get("metrics", ""), height=100, key="metrics_box", disabled=True)

requirements.txt

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+fastapi
+uvicorn
+streamlit
+faiss-cpu
+torch
+transformers
+sentence-transformers
+langchain
+openai
+chromadb
+datasets
+langchain-community
+rank_bm25
+nltk
+requests
+rouge-score
+numpy