app.py

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>
    @import url('https://fonts.googleapis.com/css2?family=Roboto+Mono&display=swap');
    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 */
        font-family: 'Roboto Mono', monospace;
    }
    .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 */
    }
    .stMarkdown p {
        font-family: 'Roboto Mono', monospace;
    }

    /* Animations */
    @keyframes fadeIn {
        from {opacity: 0;}
        to {opacity: 1;}
    }

    .stMarkdown h1 {
        animation: fadeIn 1s linear 0s 1 normal forwards;
    }

    .stMarkdown h2 {
        animation: fadeIn 1s linear 1s 1 normal forwards;
    }

    .stMarkdown p {
        animation: fadeIn 1s linear 2s 1 normal forwards;
    }
</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):
    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()

# Add this function to your code
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()

# 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:")

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, caption="Uploaded Image", use_column_width=True)
    st.write("")

    if st.button("Generate Caption"):
        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
            radiology_report = generate_radiology_report(f"Write Complete Radiology Report for this: {caption}")
            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)
            
            # 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:
                    # Add the OpenAI API call here and 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}")