Update app.py

app.py

@@ -57,16 +57,10 @@ 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):
+def show_predicted_caption(image, top_k=1):
     matches = predict_caption(
         image, model, text_embeddings, testing_df["caption"]
-    )[0]
+    )[:top_k]
     return matches
 
 def generate_radiology_report(prompt):
@@ -80,17 +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",
-        prompt=prompt,
-        max_tokens=500,
-        n=1,
-        stop=None,
-        temperature=0.8,
-    )
-    return response.choices[0].text.strip()
-
 def save_as_docx(text, filename):
     document = Document()
     document.add_paragraph(text)
@@ -99,11 +82,6 @@ def save_as_docx(text, filename):
         output.seek(0)
         return output.getvalue()
 
-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")
 
 # Collect user's personal information
@@ -123,7 +101,7 @@ if uploaded_file is not None:
     if st.button("Generate Caption"):
         with st.spinner("Generating caption..."):
             image_np = np.array(image)
-            caption = show_predicted_caption(image_np)
+            caption = show_predicted_caption(image_np)[0]
 
             st.success(f"Caption: {caption}")
 
@@ -141,11 +119,21 @@ if uploaded_file is not None:
 
             # 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
+            feedback_options = ["Better", "Satisfied", "Worse"]
+            feedback = st.radio("Rate the generated report:", feedback_options)
+
+            top_k = 1
+            while feedback == "Worse":
+                top_k += 1
+                with st.spinner("Regenerating report..."):
+                    new_caption = show_predicted_caption(image_np, top_k=top_k)[-1]
+                    radiology_report = generate_radiology_report(f"Write Complete Radiology Report for this: {new_caption}")
+
+                    radiology_report_with_personal_info = f"Patient Name: {first_name} {last_name}\nAge: {age}\nGender: {gender}\n\n{radiology_report}"
+
+                with container:
+                    container.empty()
+                    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)
+                feedback = st.radio("Rate the regenerated report:", feedback_options)