UI changes push

.gitignore

@@ -1 +0,0 @@
-.env

README.md

@@ -1,84 +1,14 @@
 ---
-title: Gradio Chatbot
-emoji: 🚀
+title: SLM Chatbot
+emoji: 💬
 colorFrom: yellow
 colorTo: purple
 sdk: gradio
 sdk_version: 5.0.1
 app_file: app.py
-pinned: true
-short_description: Chatbot
+pinned: false
+license: mit
+short_description: A web UI to chat with SLMs via the HF Inference API.
 ---
 
-
-# Gradio Chatbot : HuggingFace SLMs
-
-A modular Gradio-based application for interacting with various small language models through the Hugging Face API.
-
-## Project Structure
-
-```
-slm-poc/
-├── main.py                 # Main application entry point
-├── modules/
-│   ├── __init__.py         # Package initialization
-│   ├── config.py           # Configuration settings and constants
-│   ├── document_processor.py  # Document handling and processing
-│   └── model_handler.py    # Model interaction and response generation
-├── Dockerfile              # Docker configuration
-├── requirements.txt        # Python dependencies
-└── README.md               # Project documentation
-```
-
-## Features
-
-- Interactive chat interface with multiple language model options
-- Document processing (PDF, DOCX, TXT) for question answering
-- Adjustable model parameters (temperature, top_p, max_length)
-- Streaming responses for better user experience
-- Docker support for easy deployment
-
-## Setup and Running
-
-### Local Development
-
-1. Clone the repository
-2. Install dependencies:
-   ```
-   pip install -r requirements.txt
-   ```
-3. Create a `.env` file with your HuggingFace API token:
-   ```
-   HF_TOKEN=hf_your_token_here
-   ```
-4. Run the application:
-   ```
-   python main.py
-   ```
-
-### Docker Deployment
-
-1. Build the Docker image:
-   ```
-   docker build -t slm-poc .
-   ```
-2. Run the container:
-   ```
-   docker run -p 7860:7860 -e HF_TOKEN=hf_your_token_here slm-poc
-   ```
-
-## Usage
-
-1. Access the web interface at http://localhost:7860
-2. Enter your HuggingFace API token if not provided via environment variables
-3. Select your preferred model and adjust parameters
-4. Start chatting with the model
-5. Optionally upload documents for document-based Q&A
-
-## Supported Models
-
-T2T Inference models provided by Hugging Face via the Inference API
-
-## License
-
-This project is licensed under the MIT License - see the LICENSE file for details.
+An example chatbot using [Gradio](https://gradio.app), [`huggingface_hub`](https://huggingface.co/docs/huggingface_hub/v0.22.2/en/index), and the [Hugging Face Inference API](https://huggingface.co/docs/api-inference/index).

dockerfile

@@ -1,28 +0,0 @@
-FROM python:3.10-slim
-
-WORKDIR /app
-
-# Install system dependencies
-RUN apt-get update && apt-get install -y \
-    build-essential \
-    python3-dev \
-    && rm -rf /var/lib/apt/lists/*
-
-# Copy requirements first for better caching
-COPY requirements.txt .
-RUN pip install --no-cache-dir -r requirements.txt
-
-# Copy application code
-COPY main.py .
-COPY modules/ ./modules/
-
-# Environment variables
-ENV HF_TOKEN=""
-ENV GRADIO_SERVER_NAME="0.0.0.0"
-ENV GRADIO_SERVER_PORT=7860
-
-# Run the application
-CMD ["python", "main.py"]
-
-# Expose port for the application
-EXPOSE 7860

main.py

@@ -1,168 +0,0 @@
-# main.py
-import os
-import gradio as gr
-import tempfile
-from dotenv import load_dotenv
-from modules.document_processor import process_document
-from modules.model_handler import get_model_response, get_qa_response
-from modules.config import MODEL_MAPPING, DEFAULT_PARAMETERS
-
-# Load environment variables
-load_dotenv()
-
-def setup_api_key(api_key=None):
-    """Set up the HuggingFace API key from input or environment variables."""
-    if api_key and api_key.startswith('hf_'):
-        os.environ['HF_TOKEN'] = api_key
-        return True, "API key set successfully! ✅"
-    elif os.getenv('HF_TOKEN') and os.getenv('HF_TOKEN').startswith('hf_'):
-        return True, "API key already available! ✅"
-    else:
-        return False, "Please enter a valid HuggingFace API key. ⚠️"
-
-def create_chat_interface():
-    """Create the main chat interface for the application."""
-    with gr.Blocks(title="💬 Small Language Models - POC") as demo:
-        # Application header
-        gr.Markdown("# 💬 Small Language Models - POC")
-        gr.Markdown("This chatbot uses various Language Models such as Llama 3.2, Gemma 2, Gemma 3, Phi 3.5, DeepSeek-V3, and DeepSeek-R1.")
-        
-        with gr.Row():
-            with gr.Column(scale=1):
-                # Sidebar configuration
-                with gr.Group():
-                    api_key_input = gr.Textbox(
-                        label="HuggingFace API Token", 
-                        placeholder="Enter your HF API token (hf_...)", 
-                        type="password"
-                    )
-                    api_key_status = gr.Markdown("Please enter your API key.")
-                    api_key_button = gr.Button("Set API Key")
-                
-                with gr.Group():
-                    gr.Markdown("## Models and Parameters")
-                    model_dropdown = gr.Dropdown(
-                        choices=list(MODEL_MAPPING.keys()),
-                        label="Select Model",
-                        value=list(MODEL_MAPPING.keys())[0]
-                    )
-                    
-                    temperature_slider = gr.Slider(
-                        label="Temperature", 
-                        minimum=0.01, 
-                        maximum=1.0, 
-                        value=DEFAULT_PARAMETERS["temperature"],
-                        step=0.01
-                    )
-                    
-                    top_p_slider = gr.Slider(
-                        label="Top P", 
-                        minimum=0.01, 
-                        maximum=1.0, 
-                        value=DEFAULT_PARAMETERS["top_p"],
-                        step=0.01
-                    )
-                    
-                    max_length_slider = gr.Slider(
-                        label="Max Length", 
-                        minimum=20, 
-                        maximum=2040, 
-                        value=DEFAULT_PARAMETERS["max_length"],
-                        step=5
-                    )
-                    
-                    clear_button = gr.Button("Clear Chat History")
-                
-                with gr.Group():
-                    gr.Markdown("## Document Upload")
-                    file_upload = gr.File(
-                        label="Upload Document (PDF, DOCX, TXT)",
-                        file_types=["pdf", "docx", "txt"]
-                    )
-                    upload_status = gr.Markdown("")
-
-            with gr.Column(scale=2):
-                # Chat interface
-                chatbot = gr.Chatbot(
-                    label="Conversation",
-                    height=500,
-                    bubble_full_width=False
-                )
-                msg = gr.Textbox(
-                    label="Enter your message",
-                    placeholder="Type your message here...",
-                    show_label=False
-                )
-                
-                # State variables to track conversation and document processing
-                conversation_state = gr.State([])
-                document_store = gr.State(None)
-                api_key_state = gr.State(False)
-        
-        # Set up event handlers
-        api_key_button.click(
-            setup_api_key, 
-            inputs=[api_key_input],
-            outputs=[api_key_state, api_key_status]
-        )
-        
-        file_upload.upload(
-            process_document,
-            inputs=[file_upload, api_key_state],
-            outputs=[document_store, upload_status]
-        )
-        
-        # Function to handle chat messages
-        def respond(message, conversation, model_name, temp, top_p, max_len, doc_store, api_ready):
-            if not api_ready:
-                return conversation, conversation, "Please set a valid API key first. ⚠️"
-            
-            if not message.strip():
-                return conversation, conversation, upload_status.value
-            
-            # Update conversation with user message
-            conversation.append([message, None])
-            yield conversation, conversation, upload_status.value
-            
-            # Generate response based on whether document is uploaded
-            if doc_store is not None:
-                response = get_qa_response(
-                    message, 
-                    model_name, 
-                    doc_store, 
-                    {"temperature": temp, "top_p": top_p, "max_length": max_len}
-                )
-            else:
-                response = get_model_response(
-                    message, 
-                    conversation, 
-                    model_name, 
-                    {"temperature": temp, "top_p": top_p, "max_length": max_len}
-                )
-            
-            # Update conversation with assistant response
-            conversation[-1][1] = response
-            yield conversation, conversation, upload_status.value
-        
-        # Function to clear chat history
-        def clear_history():
-            return [], gr.update(value="Chat history cleared.")
-        
-        # Connect events
-        msg.submit(
-            respond,
-            [msg, conversation_state, model_dropdown, temperature_slider, top_p_slider, max_length_slider, document_store, api_key_state],
-            [chatbot, conversation_state, upload_status]
-        )
-        
-        clear_button.click(
-            clear_history,
-            outputs=[conversation_state, upload_status]
-        )
-        
-    return demo
-
-if __name__ == "__main__":
-    # Create and launch the application
-    app = create_chat_interface()
-    app.launch(share=False)

requirements.txt

@@ -1,10 +1 @@
-# requirements.txt
-gradio>=4.0.0
-huggingface_hub>=0.22.0
-langchain>=0.1.0
-langchain_community>=0.0.10
-faiss-cpu>=1.7.4
-python-dotenv>=1.0.0
-pypdf>=4.0.0
-docx2txt>=0.8
-sentence-transformers>=2.2.2
+huggingface_hub==0.25.2