Update app.py

app.py

@@ -1,29 +1,115 @@
 import streamlit as st
+import pickle
+import pandas as pd
+import torch
 from PIL import Image
 import numpy as np
-import main
-import pandas as pd
-import cv2
-
-def get_image_np(uploaded_file):
-    image = Image.open(uploaded_file)
-    return np.array(image)
-
-def show_predicted_caption(image_np, top_k=1):
-    image = cv2.cvtColor(image_np, cv2.COLOR_RGB2BGR)
-    captions = predict_caption(image, model, text_embeddings, valid_df['caption'].values, n=top_k)
-    return captions
-
-# Load the model and text embeddings
-valid_df = pd.read_csv('testing_df.csv')
-model, text_embeddings = main.get_text_embeddings(valid_df)
-
-# App code
-st.title("Medical Radiology Report Generator")
-
-st.header("Personal Information")
-first_name = st.text_input("First Name", "John")
-last_name = st.text_input("Last Name", "Doe")
+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)
+
+# 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"]
+    )[0]
+    return matches
+
+def generate_radiology_report(prompt):
+    response = openai.Completion.create(
+        engine="text-davinci-003",
+        prompt=prompt,
+        max_tokens=800,
+        n=1,
+        stop=None,
+        temperature=1,
+    )
+    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)
+    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
+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"])
 
@@ -37,7 +123,7 @@ 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)[0]
+            caption = show_predicted_caption(image_np)
 
             st.success(f"Caption: {caption}")
 
@@ -55,22 +141,11 @@ if uploaded_file is not None:
 
             # Advanced Feedback System
             st.header("Advanced Feedback System")
-            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 generated report:", feedback_options)
+            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