initial

README.md

@@ -1,13 +1,132 @@
----
-title: Gemma
-emoji: 😻
-colorFrom: purple
-colorTo: green
-sdk: gradio
-sdk_version: 5.34.2
-app_file: app.py
-pinned: false
-short_description: gemma
+# 🚀 Gemma-3 Multimodal Chat Application
+
+A sophisticated Gradio-based chat application featuring multimodal capabilities with Google's Gemma-3 model (placeholder implementation).
+
+## ✨ Features
+
+- 💬 **Interactive Chat Interface**: Persistent conversation history with context awareness
+- 🖼️ **Vision Capabilities**: Upload and analyze images with AI-powered insights
+- 📄 **File Processing**: Support for PDF and TXT file uploads with text extraction
+- 🧠 **Contextual Responses**: Maintains conversation context for follow-up questions
+- 🎨 **Modern UI**: Clean, responsive interface built with Gradio
+- 🔄 **State Management**: Persistent chat history and file context across interactions
+
+## 🛠️ Technologies Used
+
+- **Frontend**: Gradio 4.0+
+- **AI Model**: Gemma-3 (placeholder implementation)
+- **File Processing**: PyPDF2 for PDFs, PIL for images
+- **Backend**: Python with Hugging Face Transformers
+- **Deployment**: Hugging Face Spaces
+
+## 🚀 Quick Start
+
+### Local Development
+
+1. **Clone the repository**:
+   ```bash
+   git clone <repository-url>
+   cd gemma
+   ```
+
+2. **Install dependencies**:
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+3. **Run the application**:
+   ```bash
+   python app.py
+   ```
+
+4. **Open your browser** and navigate to `http://localhost:7860`
+
+### Hugging Face Spaces Deployment
+
+1. Create a new Space on [Hugging Face Spaces](https://huggingface.co/spaces)
+2. Choose "Gradio" as the SDK
+3. Upload the files from this repository
+4. The app will automatically deploy and be accessible via your Space URL
+
+## 📖 How to Use
+
+### Basic Chat
+1. Type your message in the text input box
+2. Click "Submit" or press Enter
+3. View the AI response in the chat history
+
+### Image Analysis
+1. Upload an image using the image upload component
+2. Type a question about the image (e.g., "What do you see in this image?")
+3. Submit to get AI-powered image analysis
+
+### File Processing
+1. Upload a PDF or TXT file using the file upload component
+2. Ask questions about the file content
+3. The extracted text will be used as context for responses
+
+### Advanced Features
+- **Persistent Context**: Previous conversations are remembered
+- **File Context**: Uploaded file content persists for follow-up questions
+- **Clear Chat**: Reset conversation history and uploaded files
+
+## 🔧 Configuration
+
+### Model Configuration
+The application currently uses a placeholder for Gemma-3. To integrate the actual model:
+
+1. Replace the `gemma_3_placeholder_inference` function in `app.py`
+2. Add proper model loading and inference logic
+3. Update dependencies if needed
+
+### Customization
+- Modify the UI theme in the `gr.Blocks` configuration
+- Adjust file size limits and supported formats
+- Customize the chat history display format
+- Add additional file processing capabilities
+
+## 📁 Project Structure
+
+```
+gemma/
+├── app.py              # Main Gradio application
+├── requirements.txt    # Python dependencies
+├── README.md          # Project documentation
+└── .gitattributes     # Git configuration
+```
+
+## 🔮 Future Enhancements
+
+- [ ] Integration with actual Gemma-3 model
+- [ ] Support for additional file formats (DOCX, XLSX)
+- [ ] Advanced image processing capabilities
+- [ ] User authentication and personalized chat history
+- [ ] Export chat conversations
+- [ ] Multi-language support
+- [ ] Voice input/output capabilities
+
+## 🤝 Contributing
+
+1. Fork the repository
+2. Create a feature branch (`git checkout -b feature/amazing-feature`)
+3. Commit your changes (`git commit -m 'Add amazing feature'`)
+4. Push to the branch (`git push origin feature/amazing-feature`)
+5. Open a Pull Request
+
+## 📄 License
+
+This project is licensed under the MIT License - see the LICENSE file for details.
+
+## 🙏 Acknowledgments
+
+- Google for the Gemma model family
+- Hugging Face for the amazing ecosystem and Spaces platform
+- Gradio team for the intuitive UI framework
+
+## 📞 Support
+
+If you encounter any issues or have questions, please open an issue on the repository or contact the maintainers.
+
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+**Note**: This application currently uses a placeholder implementation for Gemma-3. Replace the placeholder functions with actual model integration when the model becomes available.

app.py

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+import gradio as gr
+import os
+from PIL import Image
+import tempfile
+import PyPDF2
+import io
+from typing import List, Tuple, Optional
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+# Global variables for model and tokenizer
+model = None
+tokenizer = None
+
+def load_gemma_model():
+    """
+    Load Gemma model and tokenizer from Hugging Face.
+    """
+    global model, tokenizer
+    try:
+        model_name = "google/gemma-2-2b-it"  # Using Gemma-2 as it's available
+        print(f"Loading {model_name}...")
+        
+        tokenizer = AutoTokenizer.from_pretrained(model_name)
+        model = AutoModelForCausalLM.from_pretrained(
+            model_name,
+            torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
+            device_map="auto" if torch.cuda.is_available() else None
+        )
+        
+        # Add padding token if it doesn't exist
+        if tokenizer.pad_token is None:
+            tokenizer.pad_token = tokenizer.eos_token
+            
+        print("Model loaded successfully!")
+        return True
+    except Exception as e:
+        print(f"Error loading model: {e}")
+        return False
+
+def gemma_3_inference(prompt_text: str, pil_image: Optional[Image.Image] = None, chat_history: Optional[List[Tuple[str, str]]] = None) -> str:
+    """
+    Real Gemma model inference function.
+    """
+    global model, tokenizer
+    
+    # Load model if not already loaded
+    if model is None or tokenizer is None:
+        if not load_gemma_model():
+            return "❌ Error: Could not load Gemma model. Please check your internet connection and try again."
+    
+    try:
+        # Build conversation context
+        conversation = []
+        
+        # Add chat history for context (last 3 exchanges)
+        if chat_history:
+            for user_msg, bot_msg in chat_history[-3:]:
+                conversation.append({"role": "user", "content": user_msg})
+                conversation.append({"role": "assistant", "content": bot_msg})
+        
+        # Handle image input (note: Gemma-2 doesn't have native vision, so we'll describe the limitation)
+        if pil_image:
+            prompt_text = f"[Image uploaded - Note: This model doesn't have vision capabilities, but I can help with text-based questions about images] {prompt_text}"
+        
+        # Add current user message
+        conversation.append({"role": "user", "content": prompt_text})
+        
+        # Format conversation for Gemma
+        formatted_prompt = tokenizer.apply_chat_template(
+            conversation,
+            tokenize=False,
+            add_generation_prompt=True
+        )
+        
+        # Tokenize input
+        inputs = tokenizer(
+            formatted_prompt,
+            return_tensors="pt",
+            truncation=True,
+            max_length=2048
+        )
+        
+        # Move to same device as model
+        if torch.cuda.is_available() and model.device.type == 'cuda':
+            inputs = {k: v.to(model.device) for k, v in inputs.items()}
+        
+        # Generate response
+        with torch.no_grad():
+            outputs = model.generate(
+                **inputs,
+                max_new_tokens=512,
+                temperature=0.7,
+                do_sample=True,
+                pad_token_id=tokenizer.eos_token_id,
+                eos_token_id=tokenizer.eos_token_id
+            )
+        
+        # Decode response
+        response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+        
+        # Extract only the new generated part
+        response = response[len(formatted_prompt):].strip()
+        
+        return f"🤖 Gemma Response: {response}"
+        
+    except Exception as e:
+        return f"❌ Error generating response: {str(e)}. Please try again."
+
+def extract_text_from_pdf(file_path: str) -> str:
+    """
+    Extract text from PDF file using PyPDF2.
+    """
+    try:
+        with open(file_path, 'rb') as file:
+            pdf_reader = PyPDF2.PdfReader(file)
+            text = ""
+            for page in pdf_reader.pages:
+                text += page.extract_text() + "\n"
+            return text.strip()
+    except Exception as e:
+        return f"Error reading PDF: {str(e)}"
+
+def extract_text_from_txt(file_path: str) -> str:
+    """
+    Extract text from TXT file.
+    """
+    try:
+        with open(file_path, 'r', encoding='utf-8') as file:
+            return file.read().strip()
+    except Exception as e:
+        try:
+            # Try with different encoding if UTF-8 fails
+            with open(file_path, 'r', encoding='latin-1') as file:
+                return file.read().strip()
+        except Exception as e2:
+            return f"Error reading text file: {str(e2)}"
+
+def process_file_input(file_input) -> str:
+    """
+    Process uploaded file and extract text content.
+    """
+    if file_input is None:
+        return ""
+    
+    file_path = file_input.name
+    file_extension = os.path.splitext(file_path)[1].lower()
+    
+    if file_extension == '.pdf':
+        extracted_text = extract_text_from_pdf(file_path)
+        return f"📄 Content from PDF ({os.path.basename(file_path)}):\n{extracted_text[:1000]}{'...' if len(extracted_text) > 1000 else ''}"
+    elif file_extension == '.txt':
+        extracted_text = extract_text_from_txt(file_path)
+        return f"📝 Content from text file ({os.path.basename(file_path)}):\n{extracted_text[:1000]}{'...' if len(extracted_text) > 1000 else ''}"
+    else:
+        return f"❌ Unsupported file type: {file_extension}. Please upload PDF or TXT files only."
+
+def process_input(user_text: str, image_input: Optional[Image.Image], file_input, chat_history: List[Tuple[str, str]], file_context: str) -> Tuple[List[Tuple[str, str]], str, None, None, str]:
+    """
+    Main function to process user input and generate response.
+    Returns: (updated_chat_history, cleared_text, cleared_image, cleared_file, updated_file_context)
+    """
+    if not user_text.strip() and image_input is None and file_input is None:
+        return chat_history, "", None, None, file_context
+    
+    # Process file input if provided
+    current_file_context = ""
+    if file_input is not None:
+        current_file_context = process_file_input(file_input)
+    
+    # Combine file context with user text
+    combined_prompt = ""
+    if current_file_context:
+        combined_prompt = f"{current_file_context}\n\nUser Query: {user_text}"
+        # Update persistent file context
+        file_context = current_file_context
+    elif file_context and user_text.strip():  # Use previous file context if available
+        combined_prompt = f"{file_context}\n\nUser Query: {user_text}"
+    else:
+        combined_prompt = user_text
+    
+    # Generate response using Gemma model
+    if image_input is not None:
+        # Handle image + text input
+        bot_response = gemma_3_inference(combined_prompt, pil_image=image_input, chat_history=chat_history)
+        user_display = f"{user_text} [Image uploaded]"
+    else:
+        # Handle text-only input (potentially with file context)
+        bot_response = gemma_3_inference(combined_prompt, chat_history=chat_history)
+        if current_file_context:
+            user_display = f"{user_text} [File: {os.path.basename(file_input.name) if file_input else 'Unknown'}]"
+        else:
+            user_display = user_text
+    
+    # Update chat history
+    chat_history.append((user_display, bot_response))
+    
+    # Return updated history and clear inputs
+    return chat_history, "", None, None, file_context if current_file_context else file_context
+
+def clear_chat(chat_history: List[Tuple[str, str]], file_context: str) -> Tuple[List[Tuple[str, str]], str, None, None, str]:
+    """
+    Clear chat history and reset all inputs.
+    """
+    return [], "", None, None, ""
+
+# Create Gradio interface
+with gr.Blocks(title="Gemma-3 Multimodal Chat", theme=gr.themes.Soft()) as demo:
+    gr.Markdown(
+        """
+        # 🚀 Gemma-2 Multimodal Chat Application
+        
+        Welcome to the sophisticated Gemma-2 chat interface powered by Google's Gemma-2-2B-IT model! This application supports:
+        - 💬 **Text conversations** with persistent chat history
+        - 🖼️ **File processing** - upload PDF or TXT files for context
+        - 📄 **Document analysis** - extract and analyze text from uploaded files
+        - 🧠 **Contextual responses** - the model remembers your conversation
+        
+        **How to use:**
+        1. Type your message in the text box
+        2. Optionally upload a file (PDF/TXT) for document analysis
+        3. Click Submit or press Enter
+        4. Use Clear to reset the conversation
+        
+        *Note: This application uses the real Gemma-2-2B-IT model from Hugging Face. First message may take longer as the model loads.*
+        """
+    )
+    
+    # State variables
+    chat_history_state = gr.State([])
+    file_context_state = gr.State("")
+    
+    with gr.Row():
+        with gr.Column(scale=2):
+            # Chat interface
+            chatbot = gr.Chatbot(
+                label="Chat History",
+                height=400,
+                show_label=True,
+                container=True,
+                bubble_full_width=False
+            )
+            
+            # Input area
+            with gr.Row():
+                user_input = gr.Textbox(
+                    label="Your message",
+                    placeholder="Type your message here...",
+                    lines=2,
+                    scale=4
+                )
+                submit_btn = gr.Button("Submit", variant="primary", scale=1)
+            
+            # Clear button
+            clear_btn = gr.Button("🗑️ Clear Chat", variant="secondary")
+        
+        with gr.Column(scale=1):
+            # File upload area
+            gr.Markdown("### 📎 Upload Content")
+            
+            image_input = gr.Image(
+                label="Upload Image (for vision tasks)",
+                type="pil",
+                height=200
+            )
+            
+            file_input = gr.File(
+                label="Upload File (PDF or TXT)",
+                file_types=[".pdf", ".txt"],
+                height=100
+            )
+            
+            gr.Markdown(
+                """
+                **Tips:**
+                - Upload either an image OR a file per message
+                - PDF files will have their text extracted
+                - File content persists as context for follow-up questions
+                - Images are processed with vision capabilities
+                """
+            )
+    
+    # Event handlers
+    submit_btn.click(
+        fn=process_input,
+        inputs=[user_input, image_input, file_input, chat_history_state, file_context_state],
+        outputs=[chatbot, user_input, image_input, file_input, file_context_state]
+    ).then(
+        lambda: gr.update(value=chat_history_state.value),
+        outputs=[chat_history_state]
+    )
+    
+    user_input.submit(
+        fn=process_input,
+        inputs=[user_input, image_input, file_input, chat_history_state, file_context_state],
+        outputs=[chatbot, user_input, image_input, file_input, file_context_state]
+    ).then(
+        lambda: gr.update(value=chat_history_state.value),
+        outputs=[chat_history_state]
+    )
+    
+    clear_btn.click(
+        fn=clear_chat,
+        inputs=[chat_history_state, file_context_state],
+        outputs=[chatbot, user_input, image_input, file_input, file_context_state]
+    ).then(
+        lambda: gr.update(value=[]),
+        outputs=[chat_history_state]
+    )
+
+if __name__ == "__main__":
+    demo.launch(
+        server_name="0.0.0.0",
+        server_port=7860,
+        share=True,
+        show_error=True
+    )

app_config.yaml

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+title: Gemma-3 Multimodal Chat
+emoji: 🚀
+colorFrom: blue
+colorTo: purple
+sdk: gradio
+sdk_version: 4.0.0
+app_file: app.py
+pinned: false
+license: mit
+short_description: A sophisticated multimodal chat application with vision and file processing capabilities
+tags:
+  - chatbot
+  - multimodal
+  - vision
+  - file-processing
+  - gemma
+  - gradio
+python_version: 3.9
+models:
+  - google/gemma-2b-it
+hardware: cpu-basic
+suggested_storage: small

requirements.txt

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+gradio>=4.0.0
+Pillow>=9.0.0
+PyPDF2>=3.0.0
+transformers>=4.30.0
+huggingface-hub>=0.16.0
+torch>=2.0.0
+numpy>=1.21.0
+requests>=2.28.0