update

app.py

@@ -1,6 +1,4 @@
-#
-# app.py (Modified to use LLM for descriptions and a robust model path)
-#
+
 import os
 import sys
 
@@ -17,10 +15,12 @@ from langchain_openai import ChatOpenAI
 from langchain_pinecone import PineconeVectorStore
 from werkzeug.utils import secure_filename
 
+# --- Imports for Chatbot (Existing) ---
+from src.helpers import download_hugging_face_embeddings
+
 app = Flask(__name__)
 load_dotenv()
 
-
 # --- Configuration for Chatbot (Existing) ---
 PINECONE_API_KEY = os.environ.get("PINECONE_API_KEY")
 GITHUB_TOKEN = os.environ.get("GITHUB_TOKEN")
@@ -28,77 +28,89 @@ GITHUB_AI_ENDPOINT = "https://models.github.ai/inference"
 GITHUB_AI_MODEL_NAME = "Phi-3-small-8k-instruct"
 os.environ["PINECONE_API_KEY"] = PINECONE_API_KEY
 
-
 # --- Configuration for Eye Disease Model (Newly Added) ---
 app.config['UPLOAD_FOLDER'] = 'static/uploads/'
 # --- FIX: Use an absolute path to ensure the model is found ---
 MODEL_PATH = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'abhi_model.h5')
 
-
 # --- Load Models and Setup Chains (Existing and New) ---
-
 # 1. Load Chatbot RAG Chain (Existing)
-from src.helpers import download_hugging_face_embeddings
-embeddings = download_hugging_face_embeddings()
-index_name = "medicalbot"
-docsearch = PineconeVectorStore.from_existing_index(
-    index_name=index_name,
-    embedding=embeddings
-)
-retriever = docsearch.as_retriever(search_type="similarity", search_kwargs={"k": 3})
-llm = ChatOpenAI(
-    temperature=0.4,
-    max_tokens=500,
-    model=GITHUB_AI_MODEL_NAME,
-    openai_api_key=GITHUB_TOKEN,
-    openai_api_base=GITHUB_AI_ENDPOINT
-)
-system_prompt = (
-    "You are a helpful medical assistant. Use the retrieved information to answer the question concisely and accurately. "
-    "If you are asked to describe a medical condition, explain what it is, its common symptoms, and general causes in a way that is easy for a non-medical person to understand. "
-    "Always include a disclaimer that the user should consult a qualified healthcare professional for a real diagnosis."
-    "\n\n"
-    "{context}"
-)
-prompt = ChatPromptTemplate.from_messages(
-    [
-        ("system", system_prompt),
-        ("human", "{input}"),
-    ]
-)
-Youtube_chain = create_stuff_documents_chain(llm, prompt)
-rag_chain = create_retrieval_chain(retriever, Youtube_chain)
+try:
+    print("Initializing chatbot components...")
+    embeddings = download_hugging_face_embeddings()
+    index_name = "medicalbot"
+    docsearch = PineconeVectorStore.from_existing_index(
+        index_name=index_name,
+        embedding=embeddings
+    )
+    retriever = docsearch.as_retriever(search_type="similarity", search_kwargs={"k": 3})
+    llm = ChatOpenAI(
+        temperature=0.4,
+        max_tokens=500,
+        model=GITHUB_AI_MODEL_NAME,
+        openai_api_key=GITHUB_TOKEN,
+        openai_api_base=GITHUB_AI_ENDPOINT
+    )
+    system_prompt = (
+        "You are a helpful medical assistant. Use the retrieved information to answer the question concisely and accurately. "
+        "If you are asked to describe a medical condition, explain what it is, its common symptoms, and general causes in a way that is easy for a non-medical person to understand. "
+        "Always include a disclaimer that the user should consult a qualified healthcare professional for a real diagnosis."
+        "\n\n"
+        "{context}"
+    )
+    prompt = ChatPromptTemplate.from_messages(
+        [
+            ("system", system_prompt),
+            ("human", "{input}"),
+        ]
+    )
+    Youtube_chain = create_stuff_documents_chain(llm, prompt)
+    rag_chain = create_retrieval_chain(retriever, Youtube_chain)
+    print("Chatbot components initialized successfully.")
+except Exception as e:
+    print(f"Error initializing chatbot components: {e}", file=sys.stderr)
+    sys.exit(1)
 
 
 # 2. Load Eye Disease Prediction Model (Newly Added)
 try:
+    print(f"Attempting to load eye disease model from path: {MODEL_PATH}")
     eye_disease_model = load_model(MODEL_PATH)
     print("Successfully loaded eye disease model.")
 except Exception as e:
-    print(f"Error loading model from path: {MODEL_PATH}")
-    print(f"Details: {e}")
+    print(f"Error loading model from path: {MODEL_PATH}", file=sys.stderr)
+    print(f"Details: {e}", file=sys.stderr)
     sys.exit(1) # Exit if the model cannot be loaded
 
 
 # --- Prediction Helper Function (Newly Added) ---
 def predict_eye_disease(img_path):
     """Predicts the eye disease from an image path."""
-    img = image.load_img(img_path, target_size=(256, 256))
-    img_array = image.img_to_array(img)
-    img_array = img_array / 255.0  # Normalize
-    img_array = np.expand_dims(img_array, axis=0)
-    
-    # Assuming 'rnn_input' is not actually used by your model, as it's a zero array.
-    # If it is, you'll need to define it correctly.
-    rnn_input = np.zeros((img_array.shape[0], 512)) 
-    
-    prediction = eye_disease_model.predict([img_array, rnn_input])
-    
-    class_names = ['cataract', 'diabetic_retinopathy', 'glaucoma', 'normal']
-    predicted_class_index = np.argmax(prediction)
-    predicted_class = class_names[predicted_class_index]
-    
-    return predicted_class
+    print(f"Starting prediction for image: {img_path}")
+    try:
+        img = image.load_img(img_path, target_size=(256, 256))
+        img_array = image.img_to_array(img)
+        img_array = img_array / 255.0  # Normalize
+        img_array = np.expand_dims(img_array, axis=0)
+        
+        # Check the input shape before prediction
+        print(f"Input image shape for model: {img_array.shape}")
+        
+        rnn_input = np.zeros((img_array.shape[0], 512))
+        
+        # Make the prediction
+        prediction = eye_disease_model.predict([img_array, rnn_input])
+        print("Prediction successful.")
+        
+        class_names = ['cataract', 'diabetic_retinopathy', 'glaucoma', 'normal']
+        predicted_class_index = np.argmax(prediction)
+        predicted_class = class_names[predicted_class_index]
+        
+        print(f"Predicted class: {predicted_class}")
+        return predicted_class
+    except Exception as e:
+        print(f"Error in predict_eye_disease function: {e}", file=sys.stderr)
+        raise # Re-raise the exception to be caught in the route handler
 
 
 # --- Flask Routes ---
@@ -125,18 +137,26 @@ def upload_file():
     to generate a description for the predicted disease.
     """
     if 'file' not in request.files:
+        print("Error: No file part in the request.")
         return jsonify({'error': 'No file part in the request'}), 400
     
     file = request.files['file']
     if file.filename == '':
+        print("Error: No file selected for uploading.")
         return jsonify({'error': 'No file selected for uploading'}), 400
 
     if file:
         filename = secure_filename(file.filename)
+        # Create the uploads directory if it doesn't exist
+        if not os.path.exists(app.config['UPLOAD_FOLDER']):
+            os.makedirs(app.config['UPLOAD_FOLDER'])
+        
         file_path = os.path.join(app.config['UPLOAD_FOLDER'], filename)
-        file.save(file_path)
-
+        
         try:
+            print(f"Saving uploaded file to: {file_path}")
+            file.save(file_path)
+            
             # 1. Get prediction from the vision model
             predicted_class_name = predict_eye_disease(file_path)
             
@@ -154,18 +174,17 @@ def upload_file():
                 'description': description
             })
         except Exception as e:
-            print(f"Error during prediction: {e}")
-            return jsonify({'error': 'Failed to process image'}), 500
+            print(f"Uncaught exception in '/predict_disease' route: {e}", file=sys.stderr)
+            return jsonify({'error': 'Failed to process image due to a server error.'}), 500
         finally:
             # Clean up the uploaded file
             if os.path.exists(file_path):
+                print(f"Removing temporary file: {file_path}")
                 os.remove(file_path)
 
+    print("Error: Unknown error occurred.")
     return jsonify({'error': 'Unknown error occurred'}), 500
 
 
 if __name__ == '__main__':
-    # Make sure the upload folder exists
-    if not os.path.exists(app.config['UPLOAD_FOLDER']):
-        os.makedirs(app.config['UPLOAD_FOLDER'])
-    app.run()
+    app.run(host='0.0.0.0', port=7860)

static/src/helpers.py

@@ -1,23 +0,0 @@
-from langchain.document_loaders import PyPDFLoader,DirectoryLoader
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.embeddings import HuggingFaceEmbeddings
-
-#Extract text from a PDF file
-def load_pdf_file(data):
-    loader = DirectoryLoader(data, 
-                             glob = "*.pdf",   #LOAD ALL PDF FILES IN THE DIRECTORY
-                             loader_cls=PyPDFLoader) # EXTRACT TEXT FROM PDF FILES
-    documents = loader.load()
-    return documents
-
-
-#split the Data into smaller chunks
-def text_split(extracted_data):
-    text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=200)
-    texts_chunks = text_splitter.split_documents(extracted_data)
-    return texts_chunks
-
-#download the embeddings model from HuggingFace
-def download_hugging_face_embeddings():
-    embeddngs = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
-    return embeddngs

static/src/prompt.py

@@ -1,10 +0,0 @@
-
-
-sytem_prompt = (
-    "You are a assistant that answers questions-answers tasks"
-    "Use the following pieces of retrieved information to answer the question. "
-    "If you don't know the answer, just say that you don't know, don't try to make up an answer."
-    "Use the three sentences maximum and keep the answer concise."
-    "\n\n"
-    "{context}"
-)

templates/chat.html

@@ -111,274 +111,266 @@
         </div>
     </div>
     
-    <script>
-        // --- DOM Element Caching ---
-        const sidebar = document.querySelector('.sidebar');
-        const menuButton = document.getElementById('menu-button');
-        const newChatButton = document.getElementById('new-chat-button');
-        const textarea = document.getElementById('message-input');
-        const sendButton = document.getElementById('send-button');
-        const messagesDiv = document.getElementById('messages');
-        const welcomeMessageDiv = document.getElementById('welcome-message');
-        const chatContainer = document.getElementById('chat-container');
-        const inputModeButton = document.getElementById('input-mode-button');
-        const inputModePanel = document.getElementById('input-mode-panel');
-        const currentModeText = document.getElementById('current-mode-text');
-        
-        // Image-specific elements
-        const imageUploadButton = document.getElementById('image-upload-button');
-        const imageUploadInput = document.getElementById('image-upload-input');
-        
-        // --- State Management ---
-        let currentInputMode = 'Symptom Analysis';
+   
+    <script>
+        // --- DOM Element Caching ---
+        const sidebar = document.querySelector('.sidebar');
+        const menuButton = document.getElementById('menu-button');
+        const newChatButton = document.getElementById('new-chat-button');
+        const textarea = document.getElementById('message-input');
+        const sendButton = document.getElementById('send-button');
+        const messagesDiv = document.getElementById('messages');
+        const welcomeMessageDiv = document.getElementById('welcome-message');
+        const chatContainer = document.getElementById('chat-container');
+        const inputModeButton = document.getElementById('input-mode-button');
+        const inputModePanel = document.getElementById('input-mode-panel');
+        const currentModeText = document.getElementById('current-mode-text');
+        
+        // Image-specific elements
+        const imageUploadButton = document.getElementById('image-upload-button');
+        const imageUploadInput = document.getElementById('image-upload-input');
+        
+        // --- State Management ---
+        let currentInputMode = 'Symptom Analysis';
 
-        // --- Core Functions ---
-        const createMessageElement = (role, content) => {
-            const roleClasses = {
-                user: {
-                    container: 'message-user',
-                    icon: `<svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" class="w-5 h-5 text-gray-600"><path stroke-linecap="round" stroke-linejoin="round" d="M15.75 6a3.75 3.75 0 11-7.5 0 3.75 3.75 0 017.5 0zM4.501 20.118a7.5 7.5 0 0114.998 0A17.933 17.933 0 0112 21.75c-2.676 0-5.216-.584-7.499-1.632z" /></svg>`,
-                },
-                assistant: {
-                    container: 'message-assistant',
-                    icon: `<svg xmlns="http://www.w3.org/2d00/svg" viewBox="0 0 24 24" fill="white" class="w-5 h-5"><path d="M7.493 18.75c-.425 0-.82-.236-.975-.632A7.48 7.48 0 016 15.375c0-1.75.599-3.358 1.602-4.634.151-.192.373-.309.6-.397.473-.183.89-.514 1.212-.924a9.042 9.042 0 012.861-2.4c.723-.384 1.35-.956 1.653-1.715a4.498 4.498 0 00.322-1.672V3a.75.75 0 01.75-.75 2.25 2.25 0 012.25 2.25c0 1.152-.26 2.243-.723 3.218-.266.558.107 1.282.725 1.282h3.126c1.026 0 1.945.694 2.054 1.715.045.422.068.85.068 1.285a11.95 11.95 0 01-2.649 7.521c-.388.482-.987.729-1.605.729H14.23c-.483 0-.964-.078-1.423-.23l-3.114-1.04a4.501 4.501 0 00-1.423-.23h-.777zM2.331 10.977a11.969 11.969 0 00-.831 4.398 12 12 0 00.52 3.507c.26.85 1.084 1.368 1.973 1.368H4.9c.445 0 .72-.498.523-.898a8.963 8.963 0 01-.924-3.977c0-1.708.476-3.305 1.302-4.666.245-.403-.028-.959-.5-.959H4.25c-.832 0-1.612.453-1.918 1.227z" /></svg>`,
-                },
-            };
-
-            const isUser = role === 'user';
-            const div = document.createElement('div');
-            div.className = `${roleClasses[role].container} border-b border-gray-200/50`;
-            div.dataset.role = role;
-            
-            const contentDiv = document.createElement('div');
-            if (isUser) {
-                // If the user content is an image, render it as HTML
-                if (content.startsWith('<img')) {
-                    contentDiv.innerHTML = content;
-                } else {
-                    contentDiv.textContent = content;
-                }
-            } else if (role === 'assistant') {
-                contentDiv.innerHTML = marked.parse(content);
-                contentDiv.classList.add('markdown-container');
-            } else { // Typing indicator
-                contentDiv.innerHTML = `<div class="animate-pulse text-gray-500">typing...</div>`;
-            }
+        // --- Core Functions ---
+        const createMessageElement = (role, content) => {
+            const roleClasses = {
+                user: {
+                    container: 'message-user',
+                    icon: `<svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" class="w-5 h-5 text-gray-600"><path stroke-linecap="round" stroke-linejoin="round" d="M15.75 6a3.75 3.75 0 11-7.5 0 3.75 3.75 0 017.5 0zM4.501 20.118a7.5 7.5 0 0114.998 0A17.933 17.933 0 0112 21.75c-2.676 0-5.216-.584-7.499-1.632z" /></svg>`,
+                },
+                assistant: {
+                    container: 'message-assistant',
+                    icon: `<svg xmlns="http://www.w3.org/2d00/svg" viewBox="0 0 24 24" fill="white" class="w-5 h-5"><path d="M7.493 18.75c-.425 0-.82-.236-.975-.632A7.48 7.48 0 016 15.375c0-1.75.599-3.358 1.602-4.634.151-.192.373-.309.6-.397.473-.183.89-.514 1.212-.924a9.042 9.042 0 012.861-2.4c.723-.384 1.35-.956 1.653-1.715a4.498 4.498 0 00.322-1.672V3a.75.75 0 01.75-.75 2.25 2.25 0 012.25 2.25c0 1.152-.26 2.243-.723 3.218-.266.558.107 1.282.725 1.282h3.126c1.026 0 1.945.694 2.054 1.715.045.422.068.85.068 1.285a11.95 11.95 0 01-2.649 7.521c-.388.482-.987.729-1.605.729H14.23c-.483 0-.964-.078-1.423-.23l-3.114-1.04a4.501 4.501 0 00-1.423-.23h-.777zM2.331 10.977a11.969 11.969 0 00-.831 4.398 12 12 0 00.52 3.507c.26.85 1.084 1.368 1.973 1.368H4.9c.445 0 .72-.498.523-.898a8.963 8.963 0 01-.924-3.977c0-1.708.476-3.305 1.302-4.666.245-.403-.028-.959-.5-.959H4.25c-.832 0-1.612.453-1.918 1.227z" /></svg>`,
+                },
+            };
 
-            const iconContainerClass = isUser ? 'bg-gray-200' : 'bg-blue-500';
-            const mainAlignmentClass = isUser ? 'justify-end' : '';
-            const flexDirectionClass = isUser ? 'flex-row-reverse' : '';
-            const textAlignmentClass = isUser ? 'text-right' : '';
+            const isUser = role === 'user';
+            const div = document.createElement('div');
+            div.className = `${roleClasses[role].container} border-b border-gray-200/50`;
+            div.dataset.role = role;
+            
+            const contentDiv = document.createElement('div');
+            if (isUser) {
+                if (content.startsWith('<img')) {
+                    contentDiv.innerHTML = content;
+                } else {
+                    contentDiv.textContent = content;
+                }
+            } else if (role === 'assistant') {
+                contentDiv.innerHTML = marked.parse(content);
+                contentDiv.classList.add('markdown-container');
+            } else if (role === 'typing') { // Added new 'typing' role
+                contentDiv.innerHTML = `<div class="text-gray-500 italic">Processing image...</div>`;
+            }
+        
+            const iconContainerClass = isUser ? 'bg-gray-200' : 'bg-blue-500';
+            const mainAlignmentClass = isUser ? 'justify-end' : '';
+            const flexDirectionClass = isUser ? 'flex-row-reverse' : '';
+            const textAlignmentClass = isUser ? 'text-right' : '';
 
-            div.innerHTML = `
-                <div class="mx-auto max-w-3xl px-4 py-8">
-                    <div class="flex ${mainAlignmentClass}">
-                        <div class="flex items-start gap-4 max-w-[80%] ${flexDirectionClass}">
-                            <div class="w-8 h-8 rounded-full ${iconContainerClass} flex-shrink-0 flex items-center justify-center">
-                                ${roleClasses[role].icon}
-                            </div>
-                            <div class="mt-1 text-sm ${textAlignmentClass}">
-                                ${contentDiv.outerHTML}
-                            </div>
-                        </div>
-                    </div>
-                </div>
-            `;
-            return div;
-        };
+            div.innerHTML = `
+                <div class="mx-auto max-w-3xl px-4 py-8">
+                    <div class="flex ${mainAlignmentClass}">
+                        <div class="flex items-start gap-4 max-w-[80%] ${flexDirectionClass}">
+                            <div class="w-8 h-8 rounded-full ${iconContainerClass} flex-shrink-0 flex items-center justify-center">
+                                ${roleClasses[role].icon}
+                            </div>
+                            <div class="mt-1 text-sm ${textAlignmentClass}">
+                                ${contentDiv.outerHTML}
+                            </div>
+                    </div>
+                    </div>
+                </div>
+            `;
+            return div;
+        };
 
-        const appendMessage = (role, content) => {
-            const elementRole = role === 'typing' ? 'assistant' : role;
-            const messageElement = createMessageElement(elementRole, content);
-            if(role === 'typing') messageElement.dataset.role = 'typing';
-            
-            messagesDiv.appendChild(messageElement);
-            chatContainer.scrollTop = chatContainer.scrollHeight;
-        };
-        
-        const removeTypingIndicator = () => {
-            const indicator = messagesDiv.querySelector('[data-role="typing"]');
-            if (indicator) indicator.remove();
-        };
+        const appendMessage = (role, content) => {
+            const elementRole = role === 'typing' ? 'assistant' : role;
+            const messageElement = createMessageElement(elementRole, content);
+            if(role === 'typing') messageElement.dataset.role = 'typing';
+            
+            messagesDiv.appendChild(messageElement);
+            chatContainer.scrollTop = chatContainer.scrollHeight;
+        };
+        
+        const removeTypingIndicator = () => {
+            const indicator = messagesDiv.querySelector('[data-role="typing"]');
+            if (indicator) indicator.remove();
+        };
 
-        const sendMessage = async () => {
-            const message = textarea.value.trim();
-            if (!message) return;
+        const sendMessage = async () => {
+            const message = textarea.value.trim();
+            if (!message) return;
 
-            if (welcomeMessageDiv.style.display !== 'none') {
-                welcomeMessageDiv.style.display = 'none';
-            }
+            if (welcomeMessageDiv.style.display !== 'none') {
+                welcomeMessageDiv.style.display = 'none';
+            }
 
-            appendMessage('user', message);
-            textarea.value = '';
-            textarea.style.height = 'auto';
+            appendMessage('user', message);
+            textarea.value = '';
+            textarea.style.height = 'auto';
 
-            textarea.disabled = true;
-            sendButton.disabled = true;
-            imageUploadButton.disabled = true;
-            appendMessage('typing', '');
+            textarea.disabled = true;
+            sendButton.disabled = true;
+            imageUploadButton.disabled = true;
+            appendMessage('typing', '');
 
-            const messageToSend = `[${currentInputMode}] ${message}`;
+            const messageToSend = `[${currentInputMode}] ${message}`;
 
-            try {
-                const response = await fetch('/get', {
-                    method: 'POST',
-                    headers: { 'Content-Type': 'application/x-www-form-urlencoded' },
-                    body: `msg=${encodeURIComponent(messageToSend)}`
-                });
+            try {
+                const response = await fetch('/get', {
+                    method: 'POST',
+                    headers: { 'Content-Type': 'application/x-www-form-urlencoded' },
+                    body: `msg=${encodeURIComponent(messageToSend)}`
+                });
 
-                if (!response.ok) throw new Error(`HTTP error! Status: ${response.status}`);
-                
-                const botResponse = await response.text();
-                removeTypingIndicator();
-                appendMessage('assistant', botResponse);
-            } catch (error) {
-                console.error('Error fetching bot response:', error);
-                removeTypingIndicator();
-                appendMessage('assistant', "Sorry, I'm having trouble connecting right now. Please try again later.");
-            } finally {
-                textarea.disabled = false;
-                sendButton.disabled = false;
-                imageUploadButton.disabled = false;
-                textarea.focus();
-            }
-        };
+                if (!response.ok) throw new Error(`HTTP error! Status: ${response.status}`);
+                
+                const botResponse = await response.text();
+                removeTypingIndicator();
+                appendMessage('assistant', botResponse);
+            } catch (error) {
+                console.error('Error fetching bot response:', error);
+                removeTypingIndicator();
+                appendMessage('assistant', "Sorry, I'm having trouble connecting right now. Please try again later.");
+            } finally {
+                textarea.disabled = false;
+                sendButton.disabled = false;
+                imageUploadButton.disabled = false;
+                textarea.focus();
+            }
+        };
 
-        const sendImage = async (file) => {
-            if (!file) return;
+        const sendImage = async (file) => {
+            if (!file) return;
 
-            if (welcomeMessageDiv.style.display !== 'none') {
-                welcomeMessageDiv.style.display = 'none';
-            }
+            if (welcomeMessageDiv.style.display !== 'none') {
+                welcomeMessageDiv.style.display = 'none';
+            }
 
-            // Read the file to display a thumbnail
-            const reader = new FileReader();
-            reader.onload = (e) => {
-                const imgHtml = `<img src="${e.target.result}" class="max-h-48 rounded-md mx-auto my-2 border border-gray-200">`;
-                appendMessage('user', imgHtml);
-            };
-            reader.readAsDataURL(file);
+            const reader = new FileReader();
+            reader.onload = (e) => {
+                const imgHtml = `<img src="${e.target.result}" class="max-h-48 rounded-md mx-auto my-2 border border-gray-200">`;
+                appendMessage('user', imgHtml);
+            };
+            reader.readAsDataURL(file);
 
-            // Disable inputs and show typing indicator
-            textarea.disabled = true;
-            sendButton.disabled = true;
-            imageUploadButton.disabled = true;
-            appendMessage('typing', '');
+            textarea.disabled = true;
+            sendButton.disabled = true;
+            imageUploadButton.disabled = true;
+            appendMessage('typing', ''); // Use the new 'typing' role
 
-            const formData = new FormData();
-            formData.append('file', file);
-            
-            try {
-                const response = await fetch('/predict_disease', {
-                    method: 'POST',
-                    body: formData,
-                });
+            const formData = new FormData();
+            formData.append('file', file);
+            
+            try {
+                const response = await fetch('/predict_disease', {
+                    method: 'POST',
+                    body: formData,
+                });
 
-                removeTypingIndicator();
+                removeTypingIndicator();
 
-                if (response.ok) {
-                    const data = await response.json();
-                    const predictionMessage = `**Prediction:** ${data.prediction}<br><br>${data.description}`;
-                    appendMessage('assistant', predictionMessage);
-                } else {
-                    const errorData = await response.json();
-                    console.error('Server error:', errorData.error);
-                    appendMessage('assistant', `Error: ${errorData.error}`);
-                }
-            } catch (error) {
-                console.error('Network error:', error);
-                removeTypingIndicator();
-                appendMessage('assistant', 'Sorry, I\'m having trouble connecting to the image classification service.');
-            } finally {
-                textarea.disabled = false;
-                sendButton.disabled = false;
-                imageUploadButton.disabled = false;
-                textarea.focus();
-            }
-            chatContainer.scrollTop = chatContainer.scrollHeight;
-        };
-        
-        // --- Event Listeners ---
-        const toggleMenu = () => {
-            sidebar.classList.toggle('open');
-        };
+                if (response.ok) {
+                    const data = await response.json();
+                    const predictionMessage = `**Prediction:** ${data.prediction}<br><br>${data.description}`;
+                    appendMessage('assistant', predictionMessage);
+                } else {
+                    const errorData = await response.json();
+                    console.error('Server error:', errorData.error);
+                    appendMessage('assistant', `Error: ${errorData.error}`);
+                }
+            } catch (error) {
+                console.error('Network error:', error);
+                removeTypingIndicator();
+                appendMessage('assistant', 'Sorry, I\'m having trouble connecting to the image classification service. Please check the server logs for details.');
+            } finally {
+                textarea.disabled = false;
+                sendButton.disabled = false;
+                imageUploadButton.disabled = false;
+                textarea.focus();
+            }
+            chatContainer.scrollTop = chatContainer.scrollHeight;
+        };
+        
+        // --- Event Listeners ---
+        const toggleMenu = () => {
+            sidebar.classList.toggle('open');
+        };
 
-        menuButton.addEventListener('click', toggleMenu);
-        
-        newChatButton.addEventListener('click', () => {
-            messagesDiv.innerHTML = '';
-            welcomeMessageDiv.style.display = 'block';
-            textarea.value = '';
-            textarea.style.height = 'auto';
-            if (window.innerWidth < 769) {
-                sidebar.classList.remove('open');
-            }
-            currentInputMode = 'Symptom Analysis';
-            currentModeText.textContent = 'Symptom Analysis';
-        });
+        menuButton.addEventListener('click', toggleMenu);
+        
+        newChatButton.addEventListener('click', () => {
+            messagesDiv.innerHTML = '';
+            welcomeMessageDiv.style.display = 'block';
+            textarea.value = '';
+            textarea.style.height = 'auto';
+            if (window.innerWidth < 769) {
+                sidebar.classList.remove('open');
+            }
+            currentInputMode = 'Symptom Analysis';
+            currentModeText.textContent = 'Symptom Analysis';
+        });
 
-        textarea.addEventListener('input', () => {
-            textarea.style.height = 'auto';
-            textarea.style.height = `${textarea.scrollHeight}px`;
-        });
-        
-        sendButton.addEventListener('click', sendMessage);
+        textarea.addEventListener('input', () => {
+            textarea.style.height = 'auto';
+            textarea.style.height = `${textarea.scrollHeight}px`;
+        });
+        
+        sendButton.addEventListener('click', sendMessage);
 
-        textarea.addEventListener('keydown', (e) => {
-            if (e.key === 'Enter' && !e.shiftKey) {
-                e.preventDefault();
-                sendMessage();
-            }
-        });
-        
-        // Image upload button handler
-        imageUploadButton.addEventListener('click', () => {
-            imageUploadInput.click();
-        });
+        textarea.addEventListener('keydown', (e) => {
+            if (e.key === 'Enter' && !e.shiftKey) {
+                e.preventDefault();
+                sendMessage();
+            }
+        });
+        
+        imageUploadButton.addEventListener('click', () => {
+            imageUploadInput.click();
+        });
 
-        // Handle file selection from the hidden input
-        imageUploadInput.addEventListener('change', (e) => {
-            const file = e.target.files[0];
-            if (!file) return;
+        imageUploadInput.addEventListener('change', (e) => {
+            const file = e.target.files[0];
+            if (!file) return;
 
-            // Check if the current mode allows image uploads
-            if (currentInputMode === 'Eye Scan Analysis') {
-                // If it's the correct mode, send the image
-                sendImage(file);
-            } else {
-                // If not, show a message in the chat interface
-                appendMessage('assistant', "Image upload is only available in **Eye Scan Analysis** mode. Please switch modes to upload an eye scan image.");
-            }
-            
-            // Clear the input so the same file can be uploaded again if needed
-            e.target.value = ''; 
-        });
+            if (currentInputMode === 'Eye Scan Analysis') {
+                sendImage(file);
+            } else {
+                appendMessage('assistant', "Image upload is only available in **Eye Scan Analysis** mode. Please switch modes to upload an eye scan image.");
+            }
+            
+            e.target.value = ''; 
+        });
 
 
-        // --- Input Mode Dropdown Logic ---
-        inputModeButton.addEventListener('click', (e) => {
-            e.stopPropagation();
-            inputModePanel.classList.toggle('hidden');
-        });
+        // --- Input Mode Dropdown Logic ---
+        inputModeButton.addEventListener('click', (e) => {
+            e.stopPropagation();
+            inputModePanel.classList.toggle('hidden');
+        });
 
-        const modeOptions = document.querySelectorAll('.input-mode-option');
-        modeOptions.forEach(option => {
-            option.addEventListener('click', (e) => {
-                e.preventDefault();
-                currentInputMode = e.target.dataset.mode;
-                currentModeText.textContent = currentInputMode;
-                inputModePanel.classList.add('hidden');
-            });
-        });
+        const modeOptions = document.querySelectorAll('.input-mode-option');
+        modeOptions.forEach(option => {
+            option.addEventListener('click', (e) => {
+                e.preventDefault();
+                currentInputMode = e.target.dataset.mode;
+                currentModeText.textContent = currentInputMode;
+                inputModePanel.classList.add('hidden');
+            });
+        });
 
-        window.addEventListener('click', (e) => {
-            if (!inputModePanel.classList.contains('hidden') && !inputModePanel.contains(e.target) && !inputModeButton.contains(e.target)) {
-                inputModePanel.classList.add('hidden');
-            }
-            if (window.innerWidth < 769 && sidebar.classList.contains('open') && !sidebar.contains(e.target) && !menuButton.contains(e.target)) {
-                toggleMenu();
-            }
-        });
-    </script>
+        window.addEventListener('click', (e) => {
+            if (!inputModePanel.classList.contains('hidden') && !inputModePanel.contains(e.target) && !inputModeButton.contains(e.target)) {
+                inputModePanel.classList.add('hidden');
+            }
+            if (window.innerWidth < 769 && sidebar.classList.contains('open') && !sidebar.contains(e.target) && !menuButton.contains(e.target)) {
+                toggleMenu();
+            }
+        });
+    </script>
 </body>
 </html>