Update src/streamlit_app.py

src/streamlit_app.py

@@ -43,7 +43,7 @@ embeddings_model = OpenAIEmbeddings(openai_api_key=API_KEY)
 
 # --- Streamlit Page Setup ---
 st.set_page_config(page_title="RAG File Chat", layout="centered")
-st.title("\ud83e\udee0 Chat with Your Uploaded File")
+st.title("🧠 Chat with Your Uploaded File")
 
 # --- Session State ---
 if "uploaded_file" not in st.session_state:
@@ -72,50 +72,50 @@ def create_agent_and_index(file_content, file_type):
         df = pd.read_csv(io.StringIO(file_content.decode("utf-8")))
         llm = OpenAI(openai_api_key=API_KEY)
         st.session_state.agent = create_pandas_dataframe_agent(llm, df, verbose=False)
-        st.success("\ud83e\udd16 Agent created for CSV.")
+        st.success("🤖 Agent created for CSV.")
     elif file_type == "xlsx":
         df = pd.read_excel(file_content)
         llm = OpenAI(openai_api_key=API_KEY)
         st.session_state.agent = create_pandas_dataframe_agent(llm, df, verbose=False)
-        st.success("\ud83e\udd16 Agent created for Excel.")
+        st.success("🤖 Agent created for Excel.")
     elif file_type == "json":
         df = pd.DataFrame(json.loads(file_content.decode("utf-8")))
         llm = OpenAI(openai_api_key=API_KEY)
         st.session_state.agent = create_pandas_dataframe_agent(llm, df, verbose=False)
-        st.success("\ud83e\udd16 Agent created for JSON.")
+        st.success("🤖 Agent created for JSON.")
     elif file_type in ["pdf", "docx"]:
         text = extract_text_from_file(file_content, file_type)
         chunks = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0).split_text(text)
         st.session_state.vectorstore = FAISS.from_texts(chunks, embeddings_model)
-        st.success("\ud83d\udcca Text embedded into FAISS vectorstore.")
+        st.success("📊 Text embedded into FAISS vectorstore.")
     else:
-        st.error("\u274c Unsupported file type.")
+        st.error("❌ Unsupported file type.")
         return
     st.session_state.file_uploaded = True
     st.session_state.file_type = file_type
 
 # --- File Upload UI ---
-uploaded = st.file_uploader("\ud83d\udcc1 Browse and select a file", type=["csv", "xlsx", "json", "pdf", "docx"])
+uploaded = st.file_uploader("📁 Browse and select a file", type=["csv", "xlsx", "json", "pdf", "docx"])
 if uploaded:
     st.session_state.uploaded_file = uploaded
-    st.info(f"\u2705 File selected: `{uploaded.name}` ({uploaded.size / 1024:.1f} KB)")
+    st.info(f"✅ File selected: `{uploaded.name}` ({uploaded.size / 1024:.1f} KB)")
 
-if st.session_state.uploaded_file and st.button("\ud83d\udce4 Upload File"):
+if st.session_state.uploaded_file and st.button("📤 Upload File"):
     content = st.session_state.uploaded_file.read()
     ftype = st.session_state.uploaded_file.name.split(".")[-1].lower()
-    with st.spinner("\ud83d\udd04 Processing file..."):
+    with st.spinner("🔄 Processing file..."):
         create_agent_and_index(content, ftype)
 
 # --- Query UI ---
 if st.session_state.file_uploaded:
-    output_format = st.selectbox("\ud83d\udcca Select Output Format", ["Plain Text", "Markdown", "Tabular View"])
-    query = st.text_area("\ud83d\udd0d Ask a question about your uploaded file")
+    output_format = st.selectbox("📋 Select Output Format", ["Plain Text", "Markdown", "Tabular View"])
+    query = st.text_area("🔍 Ask a question about your uploaded file")
 
     if st.button("Submit Query"):
         if not query.strip():
-            st.warning("\u26a0\ufe0f Please enter a valid question.")
+            st.warning("⚠️ Please enter a valid question.")
         else:
-            with st.spinner("\ud83d\udca1 Thinking..."):
+            with st.spinner("💡 Thinking..."):
                 if st.session_state.file_type in ["pdf", "docx"]:
                     qa_chain = RetrievalQA.from_chain_type(
                         llm=OpenAI(openai_api_key=API_KEY),
@@ -127,7 +127,7 @@ if st.session_state.file_uploaded:
                 else:
                     response = st.session_state.agent.run(query)
 
-            st.subheader("\ud83d\udccc Answer")
+            st.subheader("📌 Answer")
             if output_format == "Plain Text":
                 st.text(response)
             elif output_format == "Markdown":
@@ -140,5 +140,5 @@ if st.session_state.file_uploaded:
                     df = pd.DataFrame(rows[1:], columns=rows[0])
                     st.dataframe(df)
                 except Exception:
-                    st.warning("\u26a0\ufe0f Could not render table. Showing raw text.")
-                    st.text(response)
+                    st.warning("⚠️ Could not render table. Showing raw text.")
+                    st.text(response))