Update app.py

app.py

@@ -4,21 +4,36 @@ import pandas as pd
 import torch
 from PIL import Image
 import numpy as np
-from main import predict_caption, CLIPModel , get_text_embeddings
+from main import predict_caption, CLIPModel, get_text_embeddings
+import openai
+import base64
+from io import BytesIO
+from reportlab.lib.pagesizes import letter
+from reportlab.pdfgen import canvas
 
+# 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;
+    }
 </style>
 """,
     unsafe_allow_html=True,
 )
 
-
 device = torch.device("cpu")
 
 testing_df = pd.read_csv("testing_df.csv")
@@ -33,8 +48,41 @@ def show_predicted_caption(image):
     )[0]
     return matches
 
-st.title("Medical Image Captioning")
-st.write("Upload an image to get a caption:")
+
+def generate_radiology_report(prompt):
+    response = openai.Completion.create(
+        engine="text-davinci-003",
+        prompt=prompt,
+        max_tokens=800,
+        n=1,
+        stop=None,
+        temperature=0.9,
+    )
+    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 create_pdf(caption, buffer):
+    c = canvas.Canvas(buffer, pagesize=letter)
+    c.drawString(50, 750, caption)
+    c.save()
+    buffer.seek(0)
+    return buffer
+
+
+st.title("RadiXGPT: An Evolution of machine doctors towrads 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:
@@ -42,10 +90,32 @@ if uploaded_file is not None:
     st.image(image, caption="Uploaded Image", use_column_width=True)
     st.write("")
 
-    if st.button("Generate Caption"):
-        with st.spinner("Generating caption..."):
+    if st.button("Generate Report"):
+        with st.spinner("Generating Report...hold on"):
             image_np = np.array(image)
             caption = show_predicted_caption(image_np)
             st.success(f"Caption: {caption}")
 
-            
+            prompt = f"Write Complete Radiology Report for this: {caption}"
+            report = generate_radiology_report(prompt)
+
+            st.markdown("<h2>Generated Radiology Report</h2>", unsafe_allow_html=True)
+            st.markdown(f"<div class='container'>{report}</div>", unsafe_allow_html=True)
+
+            # PDF download
+            buffer = BytesIO()
+            pdf_buffer = create_pdf(report, buffer)
+            b64 = base64.b64encode(pdf_buffer.getvalue()).decode()
+            href = f'<a href="data:application/octet-stream;base64,{b64}" download="Radiology_Report.pdf">Download Radiology Report as PDF</a>'
+            st.markdown(href, unsafe_allow_html=True)
+
+            # Chatbot for consultation
+            st.markdown("<h2>Need help understanding the Radiological Report?</h2>", unsafe_allow_html=True)
+            st.write("Ask your queries here:")
+            user_question = st.text_input("")
+
+            if st.button("Get Help"):
+                chat_prompt = f"The generated radiology report is: {report}. User asks: {user_question}"
+                chat_answer = chatbot_response(chat_prompt)
+                st.markdown("<h3>Answer:</h3>", unsafe_allow_html=True)
+                st.write(chat_answer)