Create app.py

app.py

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+import os
+import base64
+import zipfile
+from pathlib import Path
+import streamlit as st
+from byaldi import RAGMultiModalModel
+from openai import OpenAI
+
+# Function to unzip a folder if it does not exist
+def unzip_folder_if_not_exist(zip_path, extract_to):
+    if not os.path.exists(extract_to):
+        with zipfile.ZipFile(zip_path, 'r') as zip_ref:
+            zip_ref.extractall(extract_to)
+
+# Example usage
+zip_path = 'medical_index.zip'
+extract_to = 'medical_index'
+unzip_folder_if_not_exist(zip_path, extract_to)
+
+# Preload the RAGMultiModalModel
+@st.cache_resource
+def load_model():
+    return RAGMultiModalModel.from_index("medical_index")
+
+RAG = load_model()
+
+# OpenAI API key from environment
+api_key = os.getenv("OPENAI_API_KEY")
+client = OpenAI(api_key=api_key)
+
+# Streamlit UI
+st.title("Medical Diagnostic Assistant")
+st.write("Enter a medical query and get diagnostic recommendations along with visual references.")
+
+# User input
+query = st.text_input("Query", "What should be the appropriate diagnostic test for peptic ulcer?")
+
+if st.button("Submit"):
+    if query:
+        # Search using RAG model
+        with st.spinner('Retrieving information...'):
+            try:
+                returned_page = RAG.search(query, k=1)[0].base64
+
+                # Decode and display the retrieved image
+                image_bytes = base64.b64decode(returned_page)
+                filename = 'retrieved_image.jpg'
+                with open(filename, 'wb') as f:
+                    f.write(image_bytes)
+
+                # Display image in Streamlit
+                st.image(filename, caption="Reference Image", use_column_width=True)
+
+                # Get model response
+                response = client.chat.completions.create(
+                    model="gpt-4o-mini-2024-07-18",
+                    messages=[
+                        {"role": "system", "content": "You are a helpful assistant. You only answer the question based on the provided image"},
+                        {
+                            "role": "user",
+                            "content": [
+                                {"type": "text", "text": query},
+                                {
+                                    "type": "image_url",
+                                    "image_url": {"url": f"data:image/jpeg;base64,{returned_page}"},
+                                },
+                            ],
+                        },
+                    ],
+                    max_tokens=300,
+                )
+                
+                # Display the response
+                st.success("Model Response:")
+                st.write(response.choices[0].message.content)
+            except Exception as e:
+                st.error(f"An error occurred: {e}")
+    else:
+        st.warning("Please enter a query.")