you said you could add open source models to projects and host it for free on awsome servers, wtf is this simulated shit?

index.html

@@ -79,17 +79,36 @@
                     handleFileUpload(file);
                 }
             });
-            
-            function sendMessage() {
+            async function sendMessage() {
                 const message = userInput.value.trim();
                 if (message) {
                     addMessage('user', message);
                     userInput.value = '';
                     
-                    // Simulate bot response (in a real app, this would call an API)
-                    setTimeout(() => {
-                        addMessage('bot', "I'm a simulated response. In a real implementation, this would call an API like GPT-3.5 Turbo or similar.");
-                    }, 1000);
+                    // Show loading state
+                    const loadingDiv = document.createElement('div');
+                    loadingDiv.className = 'flex justify-start mb-4';
+                    loadingDiv.innerHTML = `
+                        <div class="bg-gray-200 dark:bg-gray-700 text-gray-800 dark:text-white rounded-lg rounded-bl-none p-3 max-w-xs">
+                            <div class="flex space-x-2">
+                                <div class="w-2 h-2 rounded-full bg-gray-400 animate-bounce"></div>
+                                <div class="w-2 h-2 rounded-full bg-gray-400 animate-bounce" style="animation-delay: 0.2s"></div>
+                                <div class="w-2 h-2 rounded-full bg-gray-400 animate-bounce" style="animation-delay: 0.4s"></div>
+                            </div>
+                        </div>
+                    `;
+                    chatContainer.appendChild(loadingDiv);
+                    chatContainer.scrollTop = chatContainer.scrollHeight;
+                    
+                    try {
+                        const response = await callChatAPI(message);
+                        loadingDiv.remove();
+                        addMessage('bot', response);
+                    } catch (error) {
+                        loadingDiv.remove();
+                        addMessage('bot', "Sorry, I encountered an error processing your request. Please try again.");
+                        console.error(error);
+                    }
                 }
             }
             
@@ -108,23 +127,44 @@
                 chatContainer.scrollTop = chatContainer.scrollHeight;
             }
             
-            function handleFileUpload(file) {
+            async function handleFileUpload(file) {
                 if (file.type.match('image.*')) {
                     const reader = new FileReader();
-                    reader.onload = function(e) {
+                    reader.onload = async function(e) {
                         addMessage('user', `<img src="${e.target.result}" class="max-w-full h-auto rounded-lg" alt="Uploaded image">`);
                         
-                        // Simulate image understanding response
-                        setTimeout(() => {
-                            addMessage('bot', "I see you've uploaded an image! In a real implementation, this would use a multimodal model to analyze the image.");
-                        }, 1500);
+                        // Show loading state
+                        const loadingDiv = document.createElement('div');
+                        loadingDiv.className = 'flex justify-start mb-4';
+                        loadingDiv.innerHTML = `
+                            <div class="bg-gray-200 dark:bg-gray-700 text-gray-800 dark:text-white rounded-lg rounded-bl-none p-3 max-w-xs">
+                                <div class="flex space-x-2">
+                                    <div class="w-2 h-2 rounded-full bg-gray-400 animate-bounce"></div>
+                                    <div class="w-2 h-2 rounded-full bg-gray-400 animate-bounce" style="animation-delay: 0.2s"></div>
+                                    <div class="w-2 h-2 rounded-full bg-gray-400 animate-bounce" style="animation-delay: 0.4s"></div>
+                                </div>
+                            </div>
+                        `;
+                        chatContainer.appendChild(loadingDiv);
+                        chatContainer.scrollTop = chatContainer.scrollHeight;
+                        
+                        try {
+                            const base64Image = await fileToBase64(file);
+                            const response = await callChatAPI("", base64Image);
+                            loadingDiv.remove();
+                            addMessage('bot', response);
+                        } catch (error) {
+                            loadingDiv.remove();
+                            addMessage('bot', "Sorry, I couldn't process that image. Please try another one.");
+                            console.error(error);
+                        }
                     };
                     reader.readAsDataURL(file);
                 } else {
                     alert('Please upload an image file.');
                 }
             }
-        });
+});
     </script>
 <script src="https://huggingface.co/deepsite/deepsite-badge.js"></script>
 </body>

script.js

@@ -1,56 +1,82 @@
-// This would contain the actual API integration in a real implementation
-// For now, it's just a placeholder with the chat functionality from index.html
-
-// In a real implementation, you would:
-// 1. Choose an open-source model that fits free hosting constraints (like GPT-3.5 Turbo API)
-// 2. Implement API calls to the model
-// 3. Add error handling
-// 4. Implement proper rate limiting
-// 5. Add loading states
-// 6. Implement proper session management
-
-// Example API integration would look something like:
-/*
+
+// Configuration for Hugging Face Inference API
+const HF_API_KEY = 'hf_YOUR_API_KEY'; // Replace with your actual API key
+const TEXT_MODEL = 'mistralai/Mistral-7B-Instruct-v0.1'; // Free open-source text model
+const IMAGE_MODEL = 'nlpconnect/vit-gpt2-image-captioning'; // Free image captioning model
+
+// Main chat function
 async function callChatAPI(message, imageData = null) {
-    const apiKey = 'your_api_key'; // In real app, this would be secured
-    const endpoint = 'https://api.openai.com/v1/chat/completions';
-    
-    const messages = [
-        { role: 'system', content: 'You are a helpful assistant.' },
-        { role: 'user', content: message }
-    ];
-    
-    if (imageData) {
-        // For multimodal models, you would include the image data
-        messages[1].content = [
-            { type: 'text', text: message },
-            { type: 'image_url', image_url: imageData }
-        ];
-    }
-    
     try {
-        const response = await fetch(endpoint, {
+        if (imageData) {
+            // Handle image processing
+            const caption = await generateImageCaption(imageData);
+            return `I see an image that appears to be: ${caption}. ${message ? 'You asked: ' + message : ''}`;
+        } else {
+            // Handle text processing
+            return await generateTextResponse(message);
+        }
+    } catch (error) {
+        console.error('API Error:', error);
+        return "Sorry, I'm having trouble processing your request right now.";
+    }
+}
+
+// Generate text response using Mistral-7B
+async function generateTextResponse(prompt) {
+    const response = await fetch(
+        `https://api-inference.huggingface.co/models/${TEXT_MODEL}`,
+        {
             method: 'POST',
             headers: {
-                'Content-Type': 'application/json',
-                'Authorization': `Bearer ${apiKey}`
+                'Authorization': `Bearer ${HF_API_KEY}`,
+                'Content-Type': 'application/json'
             },
             body: JSON.stringify({
-                model: 'gpt-4-vision-preview', // Or another model
-                messages: messages,
-                max_tokens: 300
+                inputs: `<s>[INST] ${prompt} [/INST]`,
+                parameters: {
+                    max_new_tokens: 200,
+                    temperature: 0.7
+                }
             })
-        });
-        
-        if (!response.ok) {
-            throw new Error(`API request failed with status ${response.status}`);
         }
-        
-        const data = await response.json();
-        return data.choices[0].message.content;
-    } catch (error) {
-        console.error('Error calling API:', error);
-        return "Sorry, I encountered an error processing your request.";
+    );
+
+    if (!response.ok) {
+        throw new Error(`API request failed with status ${response.status}`);
+    }
+
+    const result = await response.json();
+    return result[0].generated_text.split('[/INST]').pop().trim();
+}
+
+// Generate image caption using ViT-GPT2
+async function generateImageCaption(imageData) {
+    const response = await fetch(
+        `https://api-inference.huggingface.co/models/${IMAGE_MODEL}`,
+        {
+            method: 'POST',
+            headers: {
+                'Authorization': `Bearer ${HF_API_KEY}`,
+                'Content-Type': 'application/json'
+            },
+            body: JSON.stringify({ inputs: imageData })
+        }
+    );
+
+    if (!response.ok) {
+        throw new Error(`API request failed with status ${response.status}`);
     }
+
+    const result = await response.json();
+    return result[0].generated_text;
+}
+
+// Utility function to convert file to base64
+function fileToBase64(file) {
+    return new Promise((resolve, reject) => {
+        const reader = new FileReader();
+        reader.readAsDataURL(file);
+        reader.onload = () => resolve(reader.result.split(',')[1]);
+        reader.onerror = error => reject(error);
+    });
 }
-*/

style.css

@@ -33,6 +33,11 @@ body {
 .dark ::-webkit-scrollbar-thumb:hover {
     background: #718096;
 }
+/* Loading animation */
+@keyframes bounce {
+    0%, 100% { transform: translateY(0); }
+    50% { transform: translateY(-5px); }
+}
 
 /* Markdown styling */
 .markdown p {