Update app.py

app.py

@@ -126,8 +126,6 @@ if "documents" in st.session_state:
                 chain = create_stuff_documents_chain(llm, summary_prompt)
                 summary = run_chain(chain, {"context": docs})
                 st.session_state["last_agent_output"] = summary
-                st.markdown("### 📝 Summary")
-                st.write(summary)
 
             # Gap analysis
             elif task == "Identify research gaps":
@@ -136,8 +134,6 @@ if "documents" in st.session_state:
                 chain2 = LLMChain(llm=llm, prompt=gap_prompt)
                 gaps = run_chain(chain2, {"summary": summary})
                 st.session_state["last_agent_output"] = gaps
-                st.markdown("### 🔍 Identified Gaps")
-                st.write(gaps)
 
             # Idea generation
             elif task == "Suggest research ideas":
@@ -148,8 +144,6 @@ if "documents" in st.session_state:
                 chain3 = LLMChain(llm=llm, prompt=idea_prompt)
                 ideas = run_chain(chain3, {"gaps": gaps})
                 st.session_state["last_agent_output"] = ideas
-                st.markdown("### 💡 Research Ideas")
-                st.write(ideas)
 
             # Debate agent
             elif task == "Simulate a debate":
@@ -158,34 +152,39 @@ if "documents" in st.session_state:
                 debate_chain = LLMChain(llm=llm, prompt=debate_prompt)
                 debate = run_chain(debate_chain, {"summary": summary})
                 st.session_state["last_agent_output"] = debate
-                st.markdown("### 🎭 Debate")
-                st.write(debate)
 
             # Citation agent
             elif task == "Generate citation":
                 citation_chain = create_stuff_documents_chain(llm, citation_prompt)
                 citation = run_chain(citation_chain, {"context": docs})
                 st.session_state["last_agent_output"] = citation
-                st.markdown("### 📎 APA Citation")
-                st.code(citation, language="markdown")
-
-        # Translation toggle after agent response
-        if "last_agent_output" in st.session_state:
-            if st.toggle("🌍 Translate the response?"):
-                default_languages = ["Spanish", "French", "German", "Chinese", "Urdu", "Other"]
-                selected_language = st.selectbox("Choose translation language:", default_languages)
-                if selected_language == "Other":
-                    user_language = st.text_input("Please enter your desired language:", key="custom_lang")
-                else:
-                    user_language = selected_language
-
-                to_translate = st.session_state["last_agent_output"]
-                if isinstance(to_translate, dict):
-                    combined_text = "\n\n".join(str(value) for value in to_translate.values())
-                else:
-                    combined_text = str(to_translate)
-
-                translate_chain = LLMChain(llm=llm, prompt=translate_prompt)
-                translated = translate_chain.invoke({"language": user_language, "content": combined_text})
-                st.markdown(f"### 🌐 Translated Response ({user_language})")
-                st.write(translated)
+
+# 🧠 Always show last agent output if available
+if "last_agent_output" in st.session_state:
+    st.markdown("### 🤖 Agent Response")
+    output = st.session_state["last_agent_output"]
+    st.write(output)
+
+    # 🌍 Optional Translation
+    if st.toggle("🌍 Translate the response?"):
+        default_languages = ["Spanish", "French", "German", "Chinese", "Urdu", "Other"]
+        selected_language = st.selectbox("Choose translation language:", default_languages)
+        if selected_language == "Other":
+            user_language = st.text_input("Please enter your desired language:", key="custom_lang")
+        else:
+            user_language = selected_language
+
+        if user_language:
+            if isinstance(output, dict):
+                combined_text = "\n\n".join(str(v) for v in output.values())
+            else:
+                combined_text = str(output)
+
+            translate_chain = LLMChain(llm=llm, prompt=translate_prompt)
+            translated = translate_chain.invoke({
+                "language": user_language,
+                "content": combined_text
+            })
+
+            st.markdown(f"### 🌐 Translated Response ({user_language})")
+            st.write(translated)