app.py

import gradio as gr
import base64
import io
import os
from openai import OpenAI
import PyPDF2
import speech_recognition as sr
import tempfile
from pydub import AudioSegment
from typing import List, Tuple, Optional

class MultimodalChatbot:
    def __init__(self, api_key: str):
        self.client = OpenAI(
            base_url="https://openrouter.ai/api/v1",
            api_key=api_key,
        )
        self.model = "google/gemma-3n-e2b-it:free"
        self.conversation_history = []
        
    def extract_pdf_text(self, pdf_file) -> str:
        """Extract text from PDF file"""
        try:
            if hasattr(pdf_file, 'name'):
                pdf_path = pdf_file.name
            else:
                pdf_path = pdf_file
                
            text = ""
            with open(pdf_path, 'rb') as file:
                pdf_reader = PyPDF2.PdfReader(file)
                for page_num, page in enumerate(pdf_reader.pages):
                    page_text = page.extract_text()
                    if page_text.strip():
                        text += f"Page {page_num + 1}:\n{page_text}\n\n"
            return text.strip() if text.strip() else "No text could be extracted from this PDF."
        except Exception as e:
            return f"Error extracting PDF: {str(e)}"
    
    def convert_audio_to_wav(self, audio_file) -> str:
        """Convert audio file to WAV format for speech recognition"""
        try:
            if hasattr(audio_file, 'name'):
                audio_path = audio_file.name
            else:
                audio_path = audio_file
            
            file_ext = os.path.splitext(audio_path)[1].lower()
            if file_ext == '.wav':
                return audio_path
            
            audio = AudioSegment.from_file(audio_path)
            wav_path = tempfile.mktemp(suffix='.wav')
            audio.export(wav_path, format="wav", parameters=["-ac", "1", "-ar", "16000"])
            return wav_path
        except Exception as e:
            raise Exception(f"Error converting audio: {str(e)}")
    
    def transcribe_audio(self, audio_file) -> str:
        """Transcribe audio file to text"""
        try:
            recognizer = sr.Recognizer()
            wav_path = self.convert_audio_to_wav(audio_file)
            
            with sr.AudioFile(wav_path) as source:
                recognizer.adjust_for_ambient_noise(source, duration=0.2)
                audio_data = recognizer.record(source)
                
                try:
                    text = recognizer.recognize_google(audio_data)
                    return text
                except sr.UnknownValueError:
                    return "Could not understand the audio. Please try with clearer audio."
                except sr.RequestError as e:
                    try:
                        text = recognizer.recognize_sphinx(audio_data)
                        return text
                    except:
                        return f"Speech recognition service error: {str(e)}"
        except Exception as e:
            return f"Error transcribing audio: {str(e)}"
    
    def transcribe_recorded_audio(self, audio_data) -> str:
        """Transcribe recorded audio to text"""
        try:
            recognizer = sr.Recognizer()
            wav_path = tempfile.mktemp(suffix='.wav')
            
            # Convert raw audio data to WAV
            audio = AudioSegment.from_file(io.BytesIO(audio_data), format="wav")
            audio.export(wav_path, format="wav", parameters=["-ac", "1", "-ar", "16000"])
            
            with sr.AudioFile(wav_path) as source:
                recognizer.adjust_for_ambient_noise(source, duration=0.2)
                audio_data = recognizer.record(source)
                
                try:
                    text = recognizer.recognize_google(audio_data)
                    return text
                except sr.UnknownValueError:
                    return "Could not understand the recorded audio. Please try with clearer audio."
                except sr.RequestError as e:
                    try:
                        text = recognizer.recognize_sphinx(audio_data)
                        return text
                    except:
                        return f"Speech recognition service error: {str(e)}"
        except Exception as e:
            return f"Error transcribing recorded audio: {str(e)}"
    
    def create_multimodal_message(self, 
                                text_input: str = "",
                                pdf_file=None,
                                audio_file=None,
                                recorded_audio=None) -> dict:
        """Create a multimodal message for the API"""
        content_parts = []
        processing_info = []
        
        if text_input:
            content_parts.append({"type": "text", "text": text_input})
        
        if pdf_file is not None:
            pdf_text = self.extract_pdf_text(pdf_file)
            content_parts.append({
                "type": "text", 
                "text": f"PDF Content:\n{pdf_text}"
            })
            processing_info.append("📄 PDF processed")
        
        if audio_file is not None:
            audio_text = self.transcribe_audio(audio_file)
            content_parts.append({
                "type": "text", 
                "text": f"Audio Transcription:\n{audio_text}"
            })
            processing_info.append("🎤 Audio transcribed")
        
        if recorded_audio is not None:
            audio_text = self.transcribe_recorded_audio(recorded_audio)
            content_parts.append({
                "type": "text", 
                "text": f"Recorded Audio Transcription:\n{audio_text}"
            })
            processing_info.append("🎙️ Recorded audio transcribed")
        
        return {"role": "user", "content": content_parts}, processing_info
    
    def chat(self, 
             text_input: str = "",
             pdf_file=None,
             audio_file=None,
             recorded_audio=None,
             history: List[Tuple[str, str]] = None) -> Tuple[List[Tuple[str, str]], str]:
        """Main chat function"""
        if history is None:
            history = []
        
        try:
            user_message_parts = []
            if text_input:
                user_message_parts.append(f"Text: {text_input}")
            if pdf_file:
                user_message_parts.append("📄 PDF uploaded")
            if audio_file:
                user_message_parts.append("🎤 Audio uploaded")
            if recorded_audio:
                user_message_parts.append("🎙️ Recorded audio")
            
            user_display = " | ".join(user_message_parts)
            
            user_message, processing_info = self.create_multimodal_message(
                text_input, pdf_file, audio_file, recorded_audio
            )
            
            if processing_info:
                user_display += f"\n{' | '.join(processing_info)}"
            
            messages = [user_message]
            
            completion = self.client.chat.completions.create(
                extra_headers={
                    "HTTP-Referer": "https://multimodal-chatbot.local",
                    "X-Title": "Multimodal Chatbot",
                },
                model=self.model,
                messages=messages,
                max_tokens=2048,
                temperature=0.7
            )
            
            bot_response = completion.choices[0].message.content
            history.append((user_display, bot_response))
            
            return history, ""
            
        except Exception as e:
            error_msg = f"Error: {str(e)}"
            history.append((user_display if 'user_display' in locals() else "Error in input", error_msg))
            return history, ""

def create_interface():
    """Create the Gradio interface"""
    with gr.Blocks(title="Multimodal Chatbot with Gemma 3n", theme=gr.themes.Soft()) as demo:
        gr.Markdown("""
        # 🤖 Multimodal Chatbot with Gemma 3n
        
        This chatbot can process multiple types of input:
        - **Text**: Regular text messages
        - **PDF**: Extract and analyze document content  
        - **Audio**: Transcribe speech to text (supports WAV, MP3, M4A, FLAC, recorded audio)
        
        **Setup**: Enter your OpenRouter API key below to get started
        """)
        
        with gr.Row():
            with gr.Column():
                api_key_input = gr.Textbox(
                    label="🔑 OpenRouter API Key",
                    placeholder="Enter your OpenRouter API key here...",
                    type="password",
                    info="Your API key is not stored and only used for this session"
                )
                api_status = gr.Textbox(
                    label="Connection Status",
                    value="❌ API Key not provided",
                    interactive=False
                )
        
        with gr.Tabs():
            with gr.TabItem("💬 Text Chat"):
                with gr.Row():
                    with gr.Column(scale=1):
                        text_input = gr.Textbox(
                            label="💬 Text Input",
                            placeholder="Type your message here...",
                            lines=5
                        )
                        text_submit_btn = gr.Button("🚀 Send", variant="primary", size="lg", interactive=False)
                        text_clear_btn = gr.Button("🗑️ Clear", variant="secondary")
                    
                    with gr.Column(scale=2):
                        text_chatbot = gr.Chatbot(
                            label="Text Chat History",
                            height=600,
                            bubble_full_width=False,
                            show_copy_button=True
                        )
            
            with gr.TabItem("📄 PDF Chat"):
                with gr.Row():
                    with gr.Column(scale=1):
                        pdf_input = gr.File(
                            label="📄 PDF Upload",
                            file_types=[".pdf"],
                            type="filepath"
                        )
                        pdf_text_input = gr.Textbox(
                            label="💬 Question about PDF",
                            placeholder="Ask something about the PDF...",
                            lines=3
                        )
                        pdf_submit_btn = gr.Button("🚀 Send", variant="primary", size="lg", interactive=False)
                        pdf_clear_btn = gr.Button("🗑️ Clear", variant="secondary")
                    
                    with gr.Column(scale=2):
                        pdf_chatbot = gr.Chatbot(
                            label="PDF Chat History",
                            height=600,
                            bubble_full_width=False,
                            show_copy_button=True
                        )
            
            with gr.TabItem("🎤 Audio Chat"):
                with gr.Row():
                    with gr.Column(scale=1):
                        audio_input = gr.Audio(
                            label="🎤 Audio Upload", 
                            type="filepath"
                        )
                        audio_recorder = gr.Microphone(
                            label="🎙️ Record Audio",
                            type="numpy"
                        )
                        audio_text_input = gr.Textbox(
                            label="💬 Question about Audio",
                            placeholder="Ask something about the audio...",
                            lines=3
                        )
                        audio_submit_btn = gr.Button("🚀 Send", variant="primary", size="lg", interactive=False)
                        audio_clear_btn = gr.Button("🗑️ Clear", variant="secondary")
                    
                    with gr.Column(scale=2):
                        audio_chatbot = gr.Chatbot(
                            label="Audio Chat History",
                            height=600,
                            bubble_full_width=False,
                            show_copy_button=True
                        )
            
            with gr.TabItem("🌟 Combined Chat"):
                with gr.Row():
                    with gr.Column(scale=1):
                        combined_text_input = gr.Textbox(
                            label="💬 Text Input",
                            placeholder="Type your message here...",
                            lines=3
                        )
                        combined_pdf_input = gr.File(
                            label="📄 PDF Upload",
                            file_types=[".pdf"],
                            type="filepath"
                        )
                        combined_audio_input = gr.Audio(
                            label="🎤 Audio Upload", 
                            type="filepath"
                        )
                        combined_audio_recorder = gr.Microphone(
                            label="🎙️ Record Audio",
                            type="numpy"
                        )
                        combined_submit_btn = gr.Button("🚀 Send All", variant="primary", size="lg", interactive=False)
                        combined_clear_btn = gr.Button("🗑️ Clear All", variant="secondary")
                    
                    with gr.Column(scale=2):
                        combined_chatbot = gr.Chatbot(
                            label="Combined Chat History",
                            height=600,
                            bubble_full_width=False,
                            show_copy_button=True
                        )
        
        def validate_api_key(api_key):
            if not api_key or len(api_key.strip()) == 0:
                return "❌ API Key not provided", *[gr.update(interactive=False) for _ in range(4)]
            
            try:
                test_client = OpenAI(
                    base_url="https://openrouter.ai/api/v1",
                    api_key=api_key.strip(),
                )
                return "✅ API Key validated successfully", *[gr.update(interactive=True) for _ in range(4)]
            except Exception as e:
                return f"❌ API Key validation failed: {str(e)}", *[gr.update(interactive=False) for _ in range(4)]
        
        def process_text_input(api_key, text, history):
            if not api_key or len(api_key.strip()) == 0:
                if history is None:
                    history = []
                history.append(("Error", "❌ Please provide a valid API key first"))
                return history, ""
            
            chatbot = MultimodalChatbot(api_key.strip())
            return chatbot.chat(text_input=text, history=history)
        
        def process_pdf_input(api_key, pdf, text, history):
            if not api_key or len(api_key.strip()) == 0:
                if history is None:
                    history = []
                history.append(("Error", "❌ Please provide a valid API key first"))
                return history, ""
            
            chatbot = MultimodalChatbot(api_key.strip())
            return chatbot.chat(text_input=text, pdf_file=pdf, history=history)
        
        def process_audio_input(api_key, audio, recorded_audio, text, history):
            if not api_key or len(api_key.strip()) == 0:
                if history is None:
                    history = []
                history.append(("Error", "❌ Please provide a valid API key first"))
                return history, ""
            
            chatbot = MultimodalChatbot(api_key.strip())
            return chatbot.chat(text_input=text, audio_file=audio, recorded_audio=recorded_audio, history=history)
        
        def process_combined_input(api_key, text, pdf, audio, recorded_audio, history):
            if not api_key or len(api_key.strip()) == 0:
                if history is None:
                    history = []
                history.append(("Error", "❌ Please provide a valid API key first"))
                return history, ""
            
            chatbot = MultimodalChatbot(api_key.strip())
            return chatbot.chat(text, pdf, audio, recorded_audio, history)
        
        def clear_chat():
            return [], ""
        
        def clear_audio_inputs():
            return [], "", None, None
        
        def clear_all_inputs():
            return [], "", None, None, None
        
        api_key_input.change(
            validate_api_key,
            inputs=[api_key_input],
            outputs=[api_status, text_submit_btn, pdf_submit_btn, audio_submit_btn, combined_submit_btn]
        )
        
        text_submit_btn.click(
            process_text_input,
            inputs=[api_key_input, text_input, text_chatbot],
            outputs=[text_chatbot, text_input]
        )
        text_input.submit(
            process_text_input,
            inputs=[api_key_input, text_input, text_chatbot],
            outputs=[text_chatbot, text_input]
        )
        text_clear_btn.click(clear_chat, outputs=[text_chatbot, text_input])
        
        pdf_submit_btn.click(
            process_pdf_input,
            inputs=[api_key_input, pdf_input, pdf_text_input, pdf_chatbot],
            outputs=[pdf_chatbot, pdf_text_input]
        )
        pdf_clear_btn.click(lambda: ([], "", None), outputs=[pdf_chatbot, pdf_text_input, pdf_input])
        
        audio_submit_btn.click(
            process_audio_input,
            inputs=[api_key_input, audio_input, audio_recorder, audio_text_input, audio_chatbot],
            outputs=[audio_chatbot, audio_text_input]
        )
        audio_clear_btn.click(clear_audio_inputs, outputs=[audio_chatbot, audio_text_input, audio_input, audio_recorder])
        
        combined_submit_btn.click(
            process_combined_input,
            inputs=[api_key_input, combined_text_input, combined_pdf_input, 
                   combined_audio_input, combined_audio_recorder, combined_chatbot],
            outputs=[combined_chatbot, combined_text_input]
        )
        combined_clear_btn.click(clear_all_inputs, 
                               outputs=[combined_chatbot, combined_text_input, 
                                      combined_pdf_input, combined_audio_input, 
                                      combined_audio_recorder])
        
        gr.Markdown("""
        ### 🎯 How to Use Each Tab:
        
        **💬 Text Chat**: Simple text conversations with the AI
        
        **📄 PDF Chat**: Upload a PDF and ask questions about its content
        
        **🎤 Audio Chat**: Upload or record audio files for transcription and analysis
        - Supports: WAV, MP3, M4A, FLAC, OGG formats for uploads
        - Recorded audio is processed directly from your microphone
        - Best results with clear speech and minimal background noise
        
        **🌟 Combined Chat**: Use multiple input types together for comprehensive analysis
        
        ### 🔑 Getting an API Key:
        1. Go to [OpenRouter.ai](https://openrouter.ai)
        2. Sign up for an account
        3. Navigate to the API Keys section
        4. Create a new API key
        5. Copy and paste it in the field above
        
        ### ⚠️ Current Limitations:
        - Audio transcription requires internet connection for best results
        - Large files may take longer to process
        - Recorded audio quality depends on your microphone
        """)
    
    return demo

if __name__ == "__main__":
    required_packages = [
        "gradio",
        "openai", 
        "PyPDF2",
        "SpeechRecognition",
        "pydub"
    ]
    
    print("🚀 Multimodal Chatbot with Gemma 3n")
    print("=" * 50)
    print("Required packages:", ", ".join(required_packages))
    print("\n📦 To install: pip install " + " ".join(required_packages))
    print("\n🎤 For audio processing, you may also need:")
    print("   - ffmpeg (for audio conversion)")
    print("   - sudo apt-get install espeak espeak-data libespeak1 libespeak-dev (for offline speech recognition)")
    print("\n🔑 Get your API key from: https://openrouter.ai")
    print("💡 Enter your API key in the web interface when it carries")
    
    demo = create_interface()
    demo.launch(
        share=True
    )