i

app.py

@@ -3,6 +3,7 @@ import fitz  # PyMuPDF
 from transformers import pipeline
 from datetime import datetime
 
+# Streamlit page setup
 st.set_page_config(page_title="Health Report Analyzer", page_icon="🩺")
 st.title("🩺 Health Report Analyzer")
 
@@ -17,14 +18,14 @@ def extract_text(file):
         text += page.get_text()
     return text
 
-# Load Hugging Face Models
+# Load models from HuggingFace
 @st.cache_resource
 def load_models():
     summarizer = pipeline("summarization", model="philschmid/bart-large-cnn-samsum")
     explainer = pipeline("text2text-generation", model="google/flan-t5-base")
     return summarizer, explainer
 
-# MAIN LOGIC
+# Main logic
 if uploaded_file:
     text_data = extract_text(uploaded_file)
     st.success("PDF Text Extracted Successfully!")
@@ -49,11 +50,12 @@ if uploaded_file:
         st.subheader("Explanation")
         st.write(explanation)
 
+        # Store in session
         st.session_state['summary'] = summary
         st.session_state['explanation'] = explanation
 
-    # Chatbot Section
-    st.subheader("AI Chatbot: Ask Anything About Your Report or Medical Terms")
+    # AI Chatbot
+    st.subheader("💬 AI Chatbot: Ask Anything About Your Report or Medical Terms")
 
     if 'summary' in st.session_state and 'explanation' in st.session_state:
         user_question = st.text_input("Ask a question (e.g. 'meaning of CT scan', 'explain cholecystectomy'):")
@@ -72,11 +74,8 @@ if uploaded_file:
 
             st.success("AI's Response:")
             st.write(response)
-
     else:
-        st.info("Generate Summary & Explanation to activate chatbot.")
+        st.info("Please generate the summary and explanation first to enable the chatbot.")
 
 else:
     st.info("Upload a PDF Health Report to begin.")
-
-