Update app.py

app.py

@@ -50,8 +50,6 @@ st.markdown(
     unsafe_allow_html=True,
 )
 
-
-
 device = torch.device("cpu")
 
 testing_df = pd.read_csv("testing_df.csv")
@@ -59,14 +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)
 
-
 def show_predicted_caption(image):
     matches = predict_caption(
         image, model, text_embeddings, testing_df["caption"]
     )[0]
     return matches
 
-
 def generate_radiology_report(prompt):
     response = openai.Completion.create(
         engine="text-davinci-003",
@@ -78,7 +74,6 @@ def generate_radiology_report(prompt):
     )
     return response.choices[0].text.strip()
 
-
 def chatbot_response(prompt):
     response = openai.Completion.create(
         engine="text-davinci-003",
@@ -90,7 +85,6 @@ def chatbot_response(prompt):
     )
     return response.choices[0].text.strip()
 
-# Add this function to your code
 def save_as_docx(text, filename):
     document = Document()
     document.add_paragraph(text)
@@ -99,16 +93,21 @@ def save_as_docx(text, filename):
         output.seek(0)
         return output.getvalue()
 
-# Add this function to your code
 def download_link(content, filename, link_text):
     b64 = base64.b64encode(content).decode()
     href = f'<a href="data:application/octet-stream;base64,{b64}" download="{filename}">{link_text}</a>'
     return href
 
-
 st.title("RadiXGPT: An Evolution of machine doctors towards Radiology")
-st.write("Upload Scan to get Radiological Report:")
 
+# Collect user's personal information
+st.subheader("Personal Information")
+first_name = st.text_input("First Name")
+last_name = st.text_input("Last Name")
+age = st.number_input("Age", min_value=0, max_value=120, value=25, step=1)
+gender = st.selectbox("Gender", ["Male", "Female", "Other"])
+
+st.write("Upload Scan to get Radiological Report:")
 uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "png", "jpeg"])
 if uploaded_file is not None:
     image = Image.open(uploaded_file)
@@ -119,17 +118,21 @@ if uploaded_file is not None:
         with st.spinner("Generating caption..."):
             image_np = np.array(image)
             caption = show_predicted_caption(image_np)
-           
+
             st.success(f"Caption: {caption}")
 
-            # Add the OpenAI API call here and generate the radiology report
+            # 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)
-                st.markdown(download_link(save_as_docx(radiology_report, "radiology_report.docx"), "radiology_report.docx", "Download Report as DOCX"), unsafe_allow_html=True)
-            
+                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:")
@@ -143,7 +146,7 @@ if uploaded_file is not None:
                 if user_input.lower() == "thank you":
                     st.write("Bot: You're welcome! If you have any more questions, feel free to ask.")
                 else:
-                    # Add the OpenAI API call here and generate the answer to the user's question
+                    # 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']}"