Update app.py

app.py

@@ -55,26 +55,28 @@ def identify_research_gaps():
         for j, chunk_2 in enumerate(metadata_store):
             if i >= j:  # Avoid duplicate comparisons
                 continue
-            # Simple comparison: finding contradictions or lack of mentions
             if "not" in chunk_1["chunk"] and "is" in chunk_2["chunk"]:
                 gap_summary.append(f"Potential contradiction between chunks {i} and {j}")
     return gap_summary
 
 def ask_groq_api(question, context):
     chat_completion = client.chat.completions.create(
-        messages=[
-            {"role": "user", "content": f"{context}\n\n{question}"}
-        ],
+        messages=[{"role": "user", "content": f"{context}\n\n{question}"}],
         model="llama3-8b-8192"
     )
     return chat_completion.choices[0].message.content
 
 # Streamlit App
-st.title("RAG-Based Research Paper Analyzer with Gap Identification")
+st.set_page_config(page_title="Research Analyzer", page_icon="📚")
 
-uploaded_files = st.file_uploader("Upload PDF Files", accept_multiple_files=True, type="pdf")
+st.title("📄 Research Paper Analyzer with Gap Identification")
+st.write("Easily analyze and identify gaps in your research papers. 🧠")
+
+uploaded_files = st.file_uploader("📤 Upload PDF Files", accept_multiple_files=True, type="pdf")
 
 if uploaded_files:
+    st.success("📂 Files uploaded successfully! Processing...")
+    
     all_chunks = []
     all_metadata = []
     
@@ -86,14 +88,14 @@ if uploaded_files:
         store_embeddings(embeddings, metadata)
         all_chunks.extend(chunks)
         all_metadata.extend(metadata)
-    
-    st.success("Files uploaded and processed successfully!")
 
-    if st.button("View Topic Summaries"):
+    st.success("✅ Files processed and embeddings created!")
+
+    if st.button("📚 View Topic Summaries"):
         for chunk in all_chunks[:3]:
-            st.write(chunk)
+            st.write("🔹", chunk)
 
-    user_question = st.text_input("Ask a question about the uploaded papers:")
+    user_question = st.text_input("❓ Ask a question about the uploaded papers:")
     if user_question:
         relevant_chunks = retrieve_relevant_chunks(user_question)
         if relevant_chunks:
@@ -101,19 +103,19 @@ if uploaded_files:
             answer = ask_groq_api(user_question, context)
             st.write("**Answer:**", answer)
         else:
-            st.write("No relevant sections found for your question.")
+            st.write("⚠️ No relevant sections found for your question.")
 
-    if st.button("Identify Research Gaps"):
+    if st.button("🔍 Identify Research Gaps"):
         research_gaps = identify_research_gaps()
         if research_gaps:
-            st.write("### Research Gaps Identified:")
+            st.write("### 🛠️ Research Gaps Identified:")
             for gap in research_gaps:
-                st.write(f"- {gap}")
+                st.write(f"🔸 {gap}")
         else:
-            st.write("No significant research gaps identified.")
+            st.write("✅ No significant research gaps identified.")
 
-    if st.button("Generate Scatter Plot"):
-        st.write("Generating scatter plot for methods vs. results...")
+    if st.button("📊 Generate Scatter Plot"):
+        st.write("📈 Generating scatter plot for methods vs. results...")
         # Example scatter plot (replace with real data)
         x = np.random.rand(10)
         y = np.random.rand(10)
@@ -122,4 +124,4 @@ if uploaded_files:
         plt.ylabel("Results")
         st.pyplot(plt)
 
-    st.text_area("Annotate Your Insights:", height=100, key="annotations")
+    st.text_area("📝 Annotate Your Insights:", height=100, key="annotations")