Minimal requirements and proper gitignore for chat app

.gitignore

@@ -0,0 +1,68 @@
+# Virtual environments
+.venv/
+venv/
+env/
+ENV/
+
+# Python cache
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# IDE files
+.vscode/
+.idea/
+*.swp
+*.swo
+
+# OS files
+.DS_Store
+Thumbs.db
+
+# Logs
+*.log
+logs/
+
+# Environment variables
+.env
+.env.local
+.env.production
+
+# Data files
+*.pkl
+*.pickle
+*.h5
+*.hdf5
+*.pt
+*.bin
+
+# Hugging Face specific
+.huggingface/
+
+# Large directories
+data/
+models/
+assets/
+scripts/
+swahili_data/
+utils/

README.md

@@ -1,12 +1,46 @@
----
-title: KiswahiliChetu Space
-emoji: 💬
-colorFrom: yellow
-colorTo: purple
-sdk: gradio
-sdk_version: 5.0.1
-app_file: app.py
-pinned: false
----
-
-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).
+# 🌍 Multilingual AI Chat Interface
+
+A beautiful Gradio-based chatbot that supports both English and Swahili (Kiswahili) languages with seamless switching capability.
+
+![Multilingual Chat](https://img.shields.io/badge/Multilingual-English%20%26%20Swahili-blue)
+![Gradio](https://img.shields.io/badge/Interface-Gradio-orange)
+![HuggingFace](https://img.shields.io/badge/Deployed-HuggingFace%20Spaces-yellow)
+
+## ✨ Features
+
+- **🌐 Dual Language Support**: Switch effortlessly between English and Swahili
+- **🤖 Smart AI Responses**: Context-aware responses in the selected language
+- **🎨 Beautiful Interface**: Clean, modern UI with intuitive controls
+- **⚙️ Customizable Parameters**: Adjust temperature, tokens, and sampling
+- **📱 Responsive Design**: Works perfectly on desktop and mobile devices
+- **📋 Example Prompts**: Pre-loaded conversation starters in both languages
+
+## 🚀 Live Demo
+
+[![Hugging Face Spaces](https://img.shields.io/badge/🤗%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/Denis202/KiswahiliChetu_space)
+
+**Experience the chat here:**  
+👉 [https://huggingface.co/spaces/Denis202/KiswahiliChetu_space](https://huggingface.co/spaces/Denis202/KiswahiliChetu_space)
+
+## 🛠️ Technical Stack
+
+- **Framework**: Gradio 4.0+
+- **AI Model**: HuggingFaceH4/zephyr-7b-beta
+- **Backend**: Hugging Face Inference API
+- **Deployment**: Hugging Face Spaces
+- **Languages**: Python 3.8+
+
+## 📦 Installation
+
+### Local Development
+
+```bash
+# Clone the repository
+git clone https://huggingface.co/spaces/Denis202/KiswahiliChetu_space
+cd KiswahiliChetu_space
+
+# Install dependencies
+pip install -r requirements.txt
+
+# Run locally
+python app.py

app.py

@@ -1,64 +1,247 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
+import time
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
+# Initialize Hugging Face Inference Client
 client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
+# Language texts
+LANGUAGE_TEXTS = {
+    "English": {
+        "title": "AI Chat - Conversation Service",
+        "description": "Chat with your AI assistant",
+        "system_default": "You are a friendly assistant who speaks both English and Swahili. Respond in the user's preferred language.",
+        "placeholder": "Type your message here...",
+        "thinking": "Thinking...",
+        "clear": "Clear Chat",
+        "submit": "Submit",
+        "retry": "Retry",
+        "undo": "Undo",
+        "system_label": "System Message",
+        "max_tokens_label": "Max Tokens per Response",
+        "temperature_label": "Creativity (Temperature)",
+        "top_p_label": "Top-p Sampling",
+        "language_label": "Conversation Language",
+        "loading": "Loading...",
+        "error": "An error occurred. Please try again later.",
+        "examples": [
+            ["How are you? Can you tell me about Tanzania?"],
+            ["Please suggest some good Swahili books"],
+            ["Can you help me translate this to English?"],
+            ["Tell me about Swahili culture"]
+        ]
+    },
+    "Kiswahili": {
+        "title": "Mazungumzo ya AI - Huduma ya Mazungumzo",
+        "description": "Wasiliana na msaidizi wako wa AI",
+        "system_default": "Wewe ni msaidizi mwenye urafiki unaozungumza Kiingereza na Kiswahili. Jibu kwa lugha inayopendekezwa na mtumiaji.",
+        "placeholder": "Andika ujumbe wako hapa...",
+        "thinking": "Inakokotoa...",
+        "clear": "Futa Mazungumzo",
+        "submit": "Tuma",
+        "retry": "Jaribu Tenai",
+        "undo": "Rudisha",
+        "system_label": "Ujumbe wa Mfumo",
+        "max_tokens_label": "Upeo wa Vitokezi kwa Majibu",
+        "temperature_label": "Ubunifu (Joto)",
+        "top_p_label": "Uchaguzi wa Top-p",
+        "language_label": "Lugha ya Mazungumzo",
+        "loading": "Inapakia...",
+        "error": "Hitilafu imetokea. Tafadhali jaribu tena baadaye.",
+        "examples": [
+            ["Habari yako? Unaweza kuniambia kuhusu Tanzania?"],
+            ["Tafadhali nipe mapendekezo ya vitabu bora vya Kiswahili"],
+            ["Unaweza kunisaidia kutafsiri hii kwa Kiingereza?"],
+            ["Eleza kuhusu utamaduni wa Waswahili"]
+        ]
+    }
+}
+
+def get_localized_text(language, key):
+    """Get localized text based on selected language"""
+    return LANGUAGE_TEXTS.get(language, LANGUAGE_TEXTS["English"]).get(key, key)
 
 def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
+    message: str,
+    history: list[tuple[str, str]] = None,
+    system_message: str = "",
+    max_tokens: int = 512,
+    temperature: float = 0.7,
+    top_p: float = 0.95,
+    language: str = "English"
 ):
-    messages = [{"role": "system", "content": system_message}]
+    """
+    Handles streaming responses from Hugging Face API and yields output.
+    """
+    history = history or []
+    
+    # Use provided system message or default based on language
+    if not system_message:
+        system_message = get_localized_text(language, "system_default")
+    
+    # Add language instruction to system message
+    if language == "Kiswahili":
+        enhanced_system_message = f"{system_message} Jibu kwa Kiswahili kila wakati."
+    else:
+        enhanced_system_message = f"{system_message} Respond in English always."
+    
+    messages = [{"role": "system", "content": enhanced_system_message}]
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+    for user_msg, assistant_msg in history:
+        if user_msg:
+            messages.append({"role": "user", "content": user_msg})
+        if assistant_msg:
+            messages.append({"role": "assistant", "content": assistant_msg})
 
     messages.append({"role": "user", "content": message})
 
     response = ""
+    try:
+        for chunk in client.chat_completion(
+            messages,
+            max_tokens=max_tokens,
+            stream=True,
+            temperature=temperature,
+            top_p=top_p,
+        ):
+            if chunk.choices and chunk.choices[0].delta.content:
+                token = chunk.choices[0].delta.content
+                response += token
+                yield response
+                
+    except Exception as e:
+        error_message = get_localized_text(language, "error")
+        yield f"❌ {error_message}\n\nError: {str(e)}"
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
+def update_ui_language(language):
+    """Update UI elements based on selected language"""
+    return [
+        gr.Markdown.update(value=f"# {get_localized_text(language, 'title')}"),
+        gr.Markdown.update(value=get_localized_text(language, "description")),
+        gr.Chatbot.update(label=get_localized_text(language, "title")),
+        gr.Textbox.update(
+            placeholder=get_localized_text(language, "placeholder"),
+            label=get_localized_text(language, "placeholder")
+        ),
+        gr.Button.update(value=get_localized_text(language, "submit")),
+        gr.Button.update(value=get_localized_text(language, "clear")),
+        gr.Textbox.update(
+            value=get_localized_text(language, "system_default"),
+            label=get_localized_text(language, "system_label")
         ),
-    ],
-)
+        gr.Slider.update(label=get_localized_text(language, "max_tokens_label")),
+        gr.Slider.update(label=get_localized_text(language, "temperature_label")),
+        gr.Slider.update(label=get_localized_text(language, "top_p_label")),
+        gr.Dropdown.update(label=get_localized_text(language, "language_label")),
+        gr.Examples.update(
+            examples=get_localized_text(language, "examples"),
+            label=f"{get_localized_text(language, 'examples')} ({language})"
+        )
+    ]
 
+# Create the Gradio interface
+with gr.Blocks(
+    title="AI Chat - Multilingual",
+    theme=gr.themes.Soft(),
+    css="""
+    .gradio-container {max-width: 900px !important; margin: auto;}
+    .chatbot {min-height: 400px; max-height: 500px;}
+    """
+) as demo:
+    
+    # Language state
+    current_language = gr.State(value="English")
+    
+    # Header
+    title_md = gr.Markdown(value=f"# {get_localized_text('English', 'title')}")
+    description_md = gr.Markdown(value=get_localized_text('English', 'description'))
+    
+    with gr.Row():
+        with gr.Column(scale=3):
+            chatbot = gr.Chatbot(
+                label=get_localized_text('English', 'title'),
+                show_copy_button=True,
+                bubble_full_width=False
+            )
+            
+            msg = gr.Textbox(
+                placeholder=get_localized_text('English', 'placeholder'),
+                label=get_localized_text('English', 'placeholder'),
+                lines=2
+            )
+            
+            with gr.Row():
+                submit_btn = gr.Button(get_localized_text('English', 'submit'), variant="primary")
+                clear_btn = gr.Button(get_localized_text('English', 'clear'))
+        
+        with gr.Column(scale=1):
+            system_msg = gr.Textbox(
+                value=get_localized_text('English', 'system_default'),
+                label=get_localized_text('English', 'system_label'),
+                lines=3
+            )
+            
+            max_tokens = gr.Slider(
+                minimum=64, maximum=2048, value=512, step=64,
+                label=get_localized_text('English', 'max_tokens_label')
+            )
+            
+            temperature = gr.Slider(
+                minimum=0.1, maximum=2.0, value=0.7, step=0.1,
+                label=get_localized_text('English', 'temperature_label')
+            )
+            
+            top_p = gr.Slider(
+                minimum=0.1, maximum=1.0, value=0.95, step=0.05,
+                label=get_localized_text('English', 'top_p_label')
+            )
+            
+            language_dropdown = gr.Dropdown(
+                choices=["English", "Kiswahili"],
+                value="English",
+                label=get_localized_text('English', 'language_label')
+            )
+    
+    # Examples section
+    examples = gr.Examples(
+        examples=get_localized_text('English', 'examples'),
+        inputs=msg,
+        label=f"{get_localized_text('English', 'examples')} (English)"
+    )
+    
+    # Event handlers
+    submit_event = msg.submit(
+        respond,
+        inputs=[msg, chatbot, system_msg, max_tokens, temperature, top_p, current_language],
+        outputs=chatbot
+    ).then(lambda: "", None, msg)
+    
+    submit_btn.click(
+        respond,
+        inputs=[msg, chatbot, system_msg, max_tokens, temperature, top_p, current_language],
+        outputs=chatbot
+    ).then(lambda: "", None, msg)
+    
+    clear_btn.click(lambda: None, None, chatbot, queue=False)
+    
+    # Language change handler
+    language_dropdown.change(
+        update_ui_language,
+        inputs=language_dropdown,
+        outputs=[
+            title_md, description_md, chatbot, msg, submit_btn, clear_btn,
+            system_msg, max_tokens, temperature, top_p, language_dropdown, examples
+        ]
+    ).then(
+        lambda x: x,  # Update the current language state
+        inputs=language_dropdown,
+        outputs=current_language
+    )
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch(
+        server_name="0.0.0.0",
+        share=False,
+        favicon_path=None,
+        show_error=True
+    )

chat.py

@@ -0,0 +1,66 @@
+# chat.py
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+class KiswahiliChatbot:
+    def __init__(self, model_name: str = "gpt2", device: str = None):
+        """
+        Anzisha chatbot ya Kiswahili.
+        :param model_name: Jina la modeli ya Hugging Face au path
+        :param device: "cuda" au "cpu"; inagundua kiotomatiki ikiwa ni None
+        """
+        self.device = device or ("cuda" if torch.cuda.is_available() else "cpu")
+        print(f"Inatumia kifaa: {self.device}")
+
+        print(f"Inapakia modeli '{model_name}'...")
+        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
+        self.model = AutoModelForCausalLM.from_pretrained(model_name)
+        self.model.to(self.device)
+        self.model.eval()
+
+        self.chat_history = []
+
+    def chat(self, user_input: str, max_new_tokens: int = 200):
+        """
+        Tengeneza jibu kutoka kwa chatbot.
+        :param user_input: Maandishi ya mtumiaji
+        :param max_new_tokens: Idadi ya tokeni mpya za kuzalisha
+        :return: Jibu la chatbot kama string
+        """
+        self.chat_history.append(f"Mtumiaji: {user_input}")
+        prompt = "\n".join(self.chat_history) + "\nBot:"
+
+        inputs = self.tokenizer(prompt, return_tensors="pt").to(self.device)
+        with torch.no_grad():
+            output = self.model.generate(
+                **inputs,
+                max_new_tokens=max_new_tokens,
+                pad_token_id=self.tokenizer.eos_token_id,
+                do_sample=True,
+                top_k=50,
+                top_p=0.95,
+                temperature=0.7
+            )
+
+        response = self.tokenizer.decode(output[0], skip_special_tokens=True)
+        response = response.split("Bot:")[-1].strip()
+
+        self.chat_history.append(f"Bot: {response}")
+        return response
+
+    def reset_history(self):
+        """Futa historia ya mazungumzo."""
+        self.chat_history = []
+
+if __name__ == "__main__":
+    bot = KiswahiliChatbot(model_name="gpt2")  # Badilisha na modeli yako ya Kiswahili
+    print("Chatbot ya Kiswahili iko tayari! Andika 'toka' kuondoka.\n")
+
+    while True:
+        user_input = input("Wewe: ")
+        if user_input.lower() in ["toka", "ondoka"]:
+            print("Chatbot inafunga...")
+            break
+        response = bot.chat(user_input)
+        print(f"Bot: {response}\n")
+

requirements.txt

@@ -1 +1,2 @@
-huggingface_hub==0.25.2
+gradio>=4.0.0
+huggingface-hub>=0.20.0