Update app.py

app.py

@@ -1,6 +1,7 @@
 import os
 import gradio as gr
 from huggingface_hub import InferenceClient
+from PIL import Image
 
 class XylariaChat:
     def __init__(self):
@@ -9,178 +10,101 @@ class XylariaChat:
         if not self.hf_token:
             raise ValueError("HuggingFace token not found in environment variables")
         
-        # Initialize the inference client
-        self.client = InferenceClient(
+        # Initialize the inference clients
+        self.chat_client = InferenceClient(
             model="Qwen/QwQ-32B-Preview", 
             api_key=self.hf_token
         )
+        self.image_client = InferenceClient(
+            model="SG161222/RealVisXL_V4.0", 
+            api_key=self.hf_token
+        )
         
         # Initialize conversation history and persistent memory
         self.conversation_history = []
         self.persistent_memory = {}
-        
-        # System prompt with more detailed instructions
-        self.system_prompt = """You are Xylaria 1.4 Senoa, an AI assistant developed by SK MD Saad Amin. 
-        Key capabilities:
-        - Provide helpful and engaging responses
-        - Generate links for images when requested
-        - Maintain context across the conversation
-        - Be creative and supportive
-        - Remember key information shared by the user"""
-
-    def store_information(self, key, value):
-        """Store important information in persistent memory"""
-        self.persistent_memory[key] = value
-
-    def retrieve_information(self, key):
-        """Retrieve information from persistent memory"""
-        return self.persistent_memory.get(key)
 
     def get_response(self, user_input):
-        # Prepare messages with conversation context and persistent memory
+        """Get text-based response from the model."""
         messages = [
-            {"role": "system", "content": self.system_prompt},
+            {"role": "system", "content": "Your name is Xylaria 1.4 Senoa, an advanced ai model developed by sk md saad amin"},
             *self.conversation_history,
             {"role": "user", "content": user_input}
         ]
         
-        # Add persistent memory context if available
-        if self.persistent_memory:
-            memory_context = "Remembered Information:\n" + "\n".join(
-                [f"{k}: {v}" for k, v in self.persistent_memory.items()]
-            )
-            messages.insert(1, {"role": "system", "content": memory_context})
-        
-        # Generate response with streaming
         try:
-            stream = self.client.chat.completions.create(
+            response_stream = self.chat_client.chat.completions.create(
                 messages=messages,
                 temperature=0.5,
                 max_tokens=10240,
                 top_p=0.7,
                 stream=True
             )
-            
-            return stream
-        
+            return response_stream
         except Exception as e:
             return f"Error generating response: {str(e)}"
 
+    def generate_image(self, prompt):
+        """Generate image based on prompt."""
+        try:
+            # Create an image from the prompt
+            image = self.image_client.text_to_image(prompt)
+            return image
+        except Exception as e:
+            return f"Error generating image: {str(e)}"
+
     def create_interface(self):
         def streaming_response(message, chat_history):
-            # Clear input textbox
+            """Handle text response streaming."""
             response_stream = self.get_response(message)
-            
-            # If it's an error, return immediately
             if isinstance(response_stream, str):
                 return "", chat_history + [[message, response_stream]]
             
-            # Prepare for streaming response
             full_response = ""
             updated_history = chat_history + [[message, ""]]
-            
-            # Streaming output
             for chunk in response_stream:
                 if chunk.choices[0].delta.content:
-                    chunk_content = chunk.choices[0].delta.content
-                    full_response += chunk_content
-                    
-                    # Update the last message in chat history with partial response
+                    full_response += chunk.choices[0].delta.content
                     updated_history[-1][1] = full_response
                     yield "", updated_history
             
-            # Update conversation history
-            self.conversation_history.append(
-                {"role": "user", "content": message}
-            )
-            self.conversation_history.append(
-                {"role": "assistant", "content": full_response}
-            )
-            
-            # Limit conversation history to prevent token overflow
+            self.conversation_history.append({"role": "user", "content": message})
+            self.conversation_history.append({"role": "assistant", "content": full_response})
             if len(self.conversation_history) > 10:
                 self.conversation_history = self.conversation_history[-10:]
-
-        # Custom CSS for Inter font
-        custom_css = """
-        @import url('https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&display=swap');
-        
-        body, .gradio-container {
-            font-family: 'Inter', sans-serif !important;
-        }
         
-        .chatbot-container .message {
-            font-family: 'Inter', sans-serif !important;
-        }
-        
-        .gradio-container input, 
-        .gradio-container textarea, 
-        .gradio-container button {
-            font-family: 'Inter', sans-serif !important;
-        }
-        """
+        def generate_image_response(prompt):
+            """Handle image generation."""
+            if not prompt.strip():
+                return None
+            return self.generate_image(prompt)
 
-        with gr.Blocks(theme='soft', css=custom_css) as demo:
-            # Chat interface with improved styling
-            with gr.Column():
-                chatbot = gr.Chatbot(
-                    label="Xylaria 1.4 Senoa",
-                    height=500,
-                    show_copy_button=True
-                )
-                
-                # Input row with improved layout
-                with gr.Row():
-                    txt = gr.Textbox(
-                        show_label=False, 
-                        placeholder="Type your message...", 
-                        container=False,
-                        scale=4
-                    )
-                    btn = gr.Button("Send", scale=1)
-                
-                # Clear history and memory buttons
-                clear = gr.Button("Clear Conversation")
-                clear_memory = gr.Button("Clear Memory")
-            
-            # Submit functionality with streaming
-            btn.click(
-                fn=streaming_response, 
-                inputs=[txt, chatbot], 
-                outputs=[txt, chatbot]
-            )
-            txt.submit(
-                fn=streaming_response, 
-                inputs=[txt, chatbot], 
-                outputs=[txt, chatbot]
-            )
+        with gr.Blocks() as demo:
+            chatbot = gr.Chatbot(label="Xylaria 1.4 Senoa", height=500)
+
+            with gr.Row():
+                txt = gr.Textbox(show_label=False, placeholder="Type your message...", scale=8)
+                send_btn = gr.Button("💬", scale=1)
+                img_btn = gr.Button("🖼️", scale=1)
             
-            # Clear conversation history
-            clear.click(
-                fn=lambda: None, 
-                inputs=None, 
-                outputs=[chatbot],
-                queue=False
-            )
+            clear_btn = gr.Button("Clear Conversation")
+            clear_memory_btn = gr.Button("Clear Memory")
+
+            send_btn.click(fn=streaming_response, inputs=[txt, chatbot], outputs=[txt, chatbot])
+            txt.submit(fn=streaming_response, inputs=[txt, chatbot], outputs=[txt, chatbot])
             
-            # Clear persistent memory
-            clear_memory.click(
-                fn=lambda: None,
-                inputs=None,
-                outputs=[],
-                queue=False
-            )
-        
+            img_btn.click(fn=generate_image_response, inputs=txt, outputs=chatbot)
+
+            clear_btn.click(fn=lambda: None, inputs=None, outputs=chatbot)
+            clear_memory_btn.click(fn=lambda: None, inputs=None, outputs=[])
+
         return demo
 
 # Launch the interface
 def main():
     chat = XylariaChat()
     interface = chat.create_interface()
-    interface.launch(
-        share=True,  # Optional: create a public link
-        debug=True   # Show detailed errors
-    )
+    interface.launch(share=True, debug=True)
 
 if __name__ == "__main__":
-    main()
+    main()