app.py

import os
import base64
import requests
import gradio as gr
from huggingface_hub import InferenceClient
from dataclasses import dataclass
import speech_recognition as sr
import easyocr
from PIL import Image

@dataclass
class ChatMessage:
    role: str
    content: str

    def to_dict(self):
        return {"role": self.role, "content": self.content}

class XylariaChat:
    def __init__(self):
        self.hf_token = os.getenv("HF_TOKEN")
        if not self.hf_token:
            raise ValueError("HuggingFace token not found in environment variables")

        self.client = InferenceClient(
            model="Qwen/QwQ-32B-Preview",
            api_key=self.hf_token
        )

        self.image_api_url = "https://api-inference.huggingface.co/models/Salesforce/blip-image-captioning-large"
        self.image_api_headers = {"Authorization": f"Bearer {self.hf_token}"}

        self.conversation_history = []
        self.persistent_memory = {}

        self.system_prompt = """You are a helpful and harmless assistant. You are Xylaria developed by Sk Md Saad Amin . You should think step-by-step."""

        self.reader = easyocr.Reader(['ch_sim','en'], gpu=False)

    def store_information(self, key, value):
        self.persistent_memory[key] = value
        return f"Stored: {key} = {value}"

    def retrieve_information(self, key):
        return self.persistent_memory.get(key, "No information found for this key.")

    def reset_conversation(self):
        self.conversation_history = []
        self.persistent_memory.clear()

        try:
            self.client = InferenceClient(
                model="Qwen/QwQ-32B-Preview",
                api_key=self.hf_token
            )
        except Exception as e:
            print(f"Error resetting API client: {e}")

        return None

    def caption_image(self, image):
        try:
            if isinstance(image, str) and os.path.isfile(image):
                with open(image, "rb") as f:
                    data = f.read()
            elif isinstance(image, str):
                if image.startswith('data:image'):
                    image = image.split(',')[1]
                data = base64.b64decode(image)
            else:
                data = image.read()

            response = requests.post(
                self.image_api_url,
                headers=self.image_api_headers,
                data=data
            )

            if response.status_code == 200:
                caption = response.json()[0].get('generated_text', 'No caption generated')
                return caption
            else:
                return f"Error captioning image: {response.status_code} - {response.text}"

        except Exception as e:
            return f"Error processing image: {str(e)}"

    def perform_math_ocr(self, image_path):
        try:
            img = Image.open(image_path)
            result = self.reader.readtext(image_path)
            text = ' '.join([item[1] for item in result])
            return text.strip()
        except Exception as e:
            return f"Error during Math OCR: {e}"

    def get_response(self, user_input, image=None):
        try:
            messages = []

            messages.append(ChatMessage(
                role="system",
                content=self.system_prompt
            ).to_dict())

            if self.persistent_memory:
                memory_context = "Remembered Information:\n" + "\n".join(
                    [f"{k}: {v}" for k, v in self.persistent_memory.items()]
                )
                messages.append(ChatMessage(
                    role="system",
                    content=memory_context
                ).to_dict())

            for msg in self.conversation_history:
                messages.append(msg)

            if image:
                image_caption = self.caption_image(image)
                user_input = f"description of an image: {image_caption}\n\nUser's message about it: {user_input}"

            messages.append(ChatMessage(
                role="user",
                content=user_input
            ).to_dict())

            input_tokens = sum(len(msg['content'].split()) for msg in messages)
            max_new_tokens = 16384 - input_tokens - 50

            max_new_tokens = min(max_new_tokens, 10020)

            stream = self.client.chat_completion(
                messages=messages,
                model="Qwen/QwQ-32B-Preview",
                temperature=0.7,
                max_tokens=max_new_tokens,
                top_p=0.9,
                stream=True
            )

            return stream

        except Exception as e:
            print(f"Detailed error in get_response: {e}")
            return f"Error generating response: {str(e)}"

    def messages_to_prompt(self, messages):
        prompt = ""
        for msg in messages:
            if msg["role"] == "system":
                prompt += f"<|system|>\n{msg['content']}<|end|>\n"
            elif msg["role"] == "user":
                prompt += f"<|user|>\n{msg['content']}<|end|>\n"
            elif msg["role"] == "assistant":
                prompt += f"<|assistant|>\n{msg['content']}<|end|>\n"
        prompt += "<|assistant|>\n"
        return prompt

    def recognize_speech(self, audio_file):
        recognizer = sr.Recognizer()

        try:
            with sr.AudioFile(audio_file) as source:
                audio_data = recognizer.record(source)
                text = recognizer.recognize_google(audio_data)
                return text
        except sr.UnknownValueError:
            return "Could not understand audio"
        except sr.RequestError:
            return "Could not request results from Google Speech Recognition service"

    def create_interface(self):
        def streaming_response(message, chat_history, image_filepath, math_ocr_image_path, audio_file):
            if audio_file:
                voice_message = self.recognize_speech(audio_file)
                if not voice_message.startswith("Error"):
                    message = voice_message

            ocr_text = ""
            if math_ocr_image_path:
                ocr_text = self.perform_math_ocr(math_ocr_image_path)
                if ocr_text.startswith("Error"):
                    updated_history = chat_history + [[{"role": "user", "content": message}, {"role": "assistant", "content": ocr_text}]]
                    yield "", updated_history, None, None, None
                    return
                elif len(ocr_text) > 500:
                    ocr_text = "OCR output is too large to be processed."
                    updated_history = chat_history + [[{"role": "user", "content": message}, {"role": "assistant", "content": ocr_text}]]
                    yield "", updated_history, None, None, None
                    return
                else:
                    message = f"Math OCR Result: {ocr_text}\n\nUser's message: {message}"

            if image_filepath:
                response_stream = self.get_response(message, image_filepath)
            else:
                response_stream = self.get_response(message)

            if isinstance(response_stream, str):
                updated_history = chat_history + [[{"role": "user", "content": message}, {"role": "assistant", "content": response_stream}]]
                yield "", updated_history, None, None, None
                return

            full_response = ""
            updated_history = chat_history + [[{"role": "user", "content": message}, {"role": "assistant", "content": ""}]]

            try:
                for chunk in response_stream:
                    if chunk.choices and chunk.choices[0].delta and chunk.choices[0].delta.content:
                        chunk_content = chunk.choices[0].delta.content
                        full_response += chunk_content

                        updated_history[-1][1]["content"] = full_response
                        yield "", updated_history, None, None, None
            except Exception as e:
                print(f"Streaming error: {e}")
                updated_history[-1][1]["content"] = f"Error during response: {e}"
                yield "", updated_history, None, None, None
                return

            self.conversation_history.append(ChatMessage(role="user", content=message).to_dict())
            self.conversation_history.append(ChatMessage(role="assistant", content=full_response).to_dict())

            if len(self.conversation_history) > 10:
                self.conversation_history = self.conversation_history[-10:]

        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;
        }
        .image-container {
            display: flex;
            gap: 10px;
            margin-bottom: 10px;
        }
        .image-upload {
            border: 1px solid #ccc;
            border-radius: 8px;
            padding: 10px;
            background-color: #f8f8f8;
        }
        .image-preview {
            max-width: 200px;
            max-height: 200px;
            border-radius: 8px;
        }
        .clear-button {
            display: none;
        }
        .chatbot-container .message {
            opacity: 0;
            animation: fadeIn 0.5s ease-in-out forwards;
        }
        @keyframes fadeIn {
            from {
                opacity: 0;
                transform: translateY(20px);
            }
            to {
                opacity: 1;
                transform: translateY(0);
            }
        }
        .gradio-accordion {
            overflow: hidden;
            transition: max-height 0.3s ease-in-out;
            max-height: 0;
        }
        .gradio-accordion.open {
            max-height: 500px;
        }
        """

        with gr.Blocks(theme='soft', css=custom_css) as demo:
            with gr.Column():
                chatbot = gr.Chatbot(
                    label="Xylaria 1.5 Senoa (EXPERIMENTAL)",
                    height=500,
                    show_copy_button=True,
                    type='messages'
                )

                with gr.Accordion("Image Input", open=False) as accordion:
                    with gr.Row(elem_classes="image-container"):
                        with gr.Column(elem_classes="image-upload"):
                            img = gr.Image(
                                sources=["upload", "webcam"],
                                type="filepath",
                                label="Upload Image",
                                elem_classes="image-preview"
                            )
                        with gr.Column(elem_classes="image-upload"):
                            math_ocr_img = gr.Image(
                                sources=["upload", "webcam"],
                                type="filepath",
                                label="Upload Image for Math OCR",
                                elem_classes="image-preview"
                            )

                with gr.Row():
                    with gr.Column(scale=4):
                        txt = gr.Textbox(
                            show_label=False,
                            placeholder="Type your message...",
                            container=False
                        )
                    with gr.Column(scale=1):
                        audio_input = gr.Audio(
                            sources=["microphone"],
                            type="filepath",
                            label="Voice Input"
                        )
                    btn = gr.Button("Send", scale=1)

                with gr.Row():
                    clear = gr.Button("Clear Conversation")
                    clear_memory = gr.Button("Clear Memory")

                btn.click(
                    fn=streaming_response,
                    inputs=[txt, chatbot, img, math_ocr_img, audio_input],
                    outputs=[txt, chatbot, img, math_ocr_img, audio_input]
                )
                txt.submit(
                    fn=streaming_response,
                    inputs=[txt, chatbot, img, math_ocr_img, audio_input],
                    outputs=[txt, chatbot, img, math_ocr_img, audio_input]
                )

                clear.click(
                    fn=lambda: None,
                    inputs=None,
                    outputs=[chatbot],
                    queue=False
                )

                clear_memory.click(
                    fn=self.reset_conversation,
                    inputs=None,
                    outputs=[chatbot],
                    queue=False
                )

                demo.load(None, None, None, _js="""
                () => {
                    const accordion = document.querySelector(".gradio-accordion");

                    if (accordion) {
                        const accordionHeader = accordion.querySelector(".label-wrap");

                        accordionHeader.addEventListener("click", () => {
                            accordion.classList.toggle("open");
                        });
                    }
                }
                """)

                demo.load(self.reset_conversation, None, None)

        return demo

def main():
    chat = XylariaChat()
    interface = chat.create_interface()
    interface.launch(
        share=False,
        debug=True
    )

if __name__ == "__main__":
    main()