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, second_attempt=False):
     matches = predict_caption(
-        image, model, text_embeddings, testing_df["caption"]
-    )[0]
+        image, model, text_embeddings, testing_df["caption"], second_attempt
+    )[0 if not second_attempt else 1]
     return matches
 
 def generate_radiology_report(prompt):
@@ -80,30 +74,96 @@ def generate_radiology_report(prompt):
     )
     return response.choices[0].text.strip()
 
-def chatbot_response(prompt):
+st.title("RadiXGPT: An Evolution of machine doctors towards Radiology")
+
+# 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)
+    st.image(image
+import streamlit as st
+import pickle
+import pandas as pd
+import torch
+from PIL import Image
+import numpy as np
+from main import predict_caption, CLIPModel, get_text_embeddings
+import openai
+import base64
+from docx import Document
+from docx.enum.text import WD_PARAGRAPH_ALIGNMENT
+from io import BytesIO
+
+# Set up OpenAI API
+openai.api_key = "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"
+
+# Custom CSS for the page
+st.markdown(
+    """
+<style>
+    body {
+        background-color: transparent;
+    }
+    .container {
+        display: flex;
+        justify-content: center;
+        align-items: center;
+        background-color: rgba(255, 255, 255, 0.7);
+        border-radius: 15px;
+        padding: 20px;
+    }
+    .stApp {
+        background-color: transparent;
+    }
+    .stText, .stMarkdown, .stTextInput>label, .stButton>button>span {
+        color: #1c1c1c !important; /* Set the dark text color for text elements */
+    }
+    .stButton>button>span {
+        color: initial !important; /* Reset the text color for the 'Generate Caption' button */
+    }
+    .stMarkdown h1, .stMarkdown h2 {
+        color: #ff6b81 !important; /* Set the text color of h1 and h2 elements to soft red-pink */
+        font-weight: bold; /* Set the font weight to bold */
+        border: 2px solid #ff6b81; /* Add a bold border around the headers */
+        padding: 10px; /* Add padding to the headers */
+        border-radius: 5px; /* Add border-radius to the headers */
+    }
+</style>
+""",
+    unsafe_allow_html=True,
+)
+
+device = torch.device("cpu")
+
+testing_df = pd.read_csv("testing_df.csv")
+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, second_attempt=False):
+    matches = predict_caption(
+        image, model, text_embeddings, testing_df["caption"], second_attempt
+    )[0 if not second_attempt else 1]
+    return matches
+
+def generate_radiology_report(prompt):
     response = openai.Completion.create(
         engine="text-davinci-003",
         prompt=prompt,
-        max_tokens=500,
+        max_tokens=800,
         n=1,
         stop=None,
-        temperature=0.8,
+        temperature=1,
     )
     return response.choices[0].text.strip()
 
-def save_as_docx(text, filename):
-    document = Document()
-    document.add_paragraph(text)
-    with BytesIO() as output:
-        document.save(output)
-        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
@@ -139,13 +199,20 @@ if uploaded_file is not None:
                 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)
 
-            # 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...")
+                st.header("Self Adaptive Feedback System")
+
+                feedback_options = st.radio("Is the radiology report satisfactory?", ["Better", "Satisfied", "Worse"])
 
-            if st.button("Submit Feedback"):
-                st.success("Thank you for your feedback! Your input helps us improve the RadiXGPT system.")
+                if feedback_options == "Worse":
+                    caption = show_predicted_caption(image_np, second_attempt=True)
+                    radiology_report = generate_radiology_report(f"Write Complete Radiology Report for this: {caption}")
+                    radiology_report_with_personal_info = f"Patient Name: {first_name} {last_name}\nAge: {age}\nGender: {gender}\n\n{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)
+                elif feedback_options == "Satisfied":
+                    st.success("Thank you for your feedback! Your input helps us improve the RadiXGPT system.")
+                elif feedback_options == "Better":
+                    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
+                # logging the feedback, or triggering an alert for manual review