Update app.py

app.py

@@ -50,7 +50,6 @@ st.markdown(
     unsafe_allow_html=True,
 )
 
-
 device = torch.device("cpu")
 
 testing_df = pd.read_csv("testing_df.csv")
@@ -58,6 +57,12 @@ model = CLIPModel().to(device)
 model.load_state_dict(torch.load("weights.pt", map_location=torch.device('cpu')))
 text_embeddings = torch.load('saved_text_embeddings.pt', map_location=device)
 
+# Initialize the session state
+if 'user_input' not in st.session_state:
+    st.session_state.user_input = ''
+if 'chat_history' not in st.session_state:
+    st.session_state.chat_history = []
+
 def show_predicted_caption(image):
     matches = predict_caption(
         image, model, text_embeddings, testing_df["caption"]
@@ -124,36 +129,23 @@ if uploaded_file is not None:
 
             # Generate the radiology report
             radiology_report = generate_radiology_report(f"Write Complete Radiology Report for this: {caption}")
-            
+
             # Add personal information to the radiology report
             radiology_report_with_personal_info = f"Patient Name: {first_name} {last_name}\nAge: {age}\nGender: {gender}\n\n{radiology_report}"
-            
+
             container = st.container()
             with container:
                 st.header("Radiology Report")
                 st.write(radiology_report_with_personal_info)
                 st.markdown(download_link(save_as_docx(radiology_report_with_personal_info, "radiology_report.docx"), "radiology_report.docx", "Download Report as DOCX"), unsafe_allow_html=True)
 
-            # Add the chatbot functionality
-            st.header("1-to-1 Consultation")
-            st.write("Ask any questions you have about the radiology report:")
-
-            user_input = st.text_input("Enter your question:")
-            chat_history = []
-
-            if user_input:
-                chat_history.append({"user": user_input})
-
-                if user_input.lower() == "thank you":
-                    st.write("Bot: You're welcome! If you have any more questions, feel free to ask.")
-                else:
-                    # Generate the answer to the user's question
-                    prompt = f"Answer to the user's question based on the generated radiology report: {user_input}"
-                    for history_item in chat_history:
-                        prompt += f"\nUser: {history_item['user']}"
-                        if 'bot' in history_item:
-                            prompt += f"\nBot: {history_item['bot']}"
-
-                    answer = chatbot_response(prompt)
-                    chat_history[-1]["bot"] = answer
-                    st.write(f"Bot: {answer}")
+            # Advanced Feedback System
+            st.header("Advanced Feedback System")
+            st.write("Provide feedback on the RadiXGPT system to help it learn and adapt to technical terms:")
+            feedback = st.text_area("Enter your feedback here...")
+
+            if st.button("Submit Feedback"):
+                st.success("Thank you for your feedback! Your input helps us improve the RadiXGPT system.")
+                # Add your feedback processing logic here
+                # This could include updating the model with the new information,
+                # logging the feedback, or triggering an alert for manual review