app.py

import gradio as gr
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
import time

# Load Phi-2 model (small, fast LLM)
model_name = "microsoft/phi-2"

try:
    tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
    model = AutoModelForCausalLM.from_pretrained(
        model_name,
        torch_dtype=torch.float16,
        device_map="auto",
        trust_remote_code=True
    )
    model_loaded = True
except Exception as e:
    model_loaded = False
    error_msg = str(e)

def chat_response(message, language="English"):
    """Generate chat response"""
    if not model_loaded:
        return f"⚠️ Model not loaded: {error_msg}. Using demo mode."
    
    try:
        if language == "Malayalam":
            prompt = f"നിങ്ങൾ ഒരു സഹായകമായ എഐ അസിസ്റ്റന്റാണ്. ചോദ്യത്തിന് ചെറിയ, സ്പഷ്ടമായ ഉത്തരം നൽകുക.\n\nUser: {message}\nAssistant:"
        else:
            prompt = f"You are a helpful AI assistant. Provide short, clear answers.\n\nUser: {message}\nAssistant:"
        
        inputs = tokenizer(prompt, return_tensors="pt", max_length=512, truncation=True)
        outputs = model.generate(
            **inputs,
            max_new_tokens=100,
            temperature=0.7,
            top_p=0.9,
            do_sample=True
        )
        
        response = tokenizer.decode(outputs[0], skip_special_tokens=True)
        response = response.split("Assistant:")[-1].strip()
        return response[:300]
    
    except Exception as e:
        return f"Error: {str(e)}. Try a simpler question."

# Create Gradio interface
with gr.Blocks(theme=gr.themes.Soft()) as demo:
    gr.Markdown(
        """
        # 🌍 Multilingual Voice Chatbot
        **AI Chatbot supporting English & Malayalam** | Built with Phi-2 LLM
        
        Ask me anything! Currently text-based (voice features coming soon)
        """
    )
    
    with gr.Row():
        with gr.Column(scale=3):
            language = gr.Radio(
                ["English", "Malayalam"],
                value="English",
                label="🌐 Select Language"
            )
            
            chatbot = gr.Chatbot(label="💬 Chat History", height=400)
            msg = gr.Textbox(
                label="Your Message",
                placeholder="Type your message here...",
                lines=2
            )
            
            with gr.Row():
                submit = gr.Button("Send 📤", variant="primary")
                clear = gr.Button("Clear 🗑️")
        
        with gr.Column(scale=1):
            gr.Markdown(
                """
                ### Features
                ✅ English & Malayalam
                ✅ Phi-2 LLM (2.7B)
                ✅ Fast responses
                ✅ Web-based UI
                
                ### Coming Soon
                🎤 Speech input
                🔊 Voice output
                🌏 More languages
                
                ### Tech Stack
                - **LLM**: Phi-2
                - **Framework**: Gradio
                - **Platform**: HF Spaces
                """
            )
    
    def respond(message, chat_history, lang):
        if not message.strip():
            return chat_history, ""
        
        bot_response = chat_response(message, lang)
        chat_history.append((message, bot_response))
        return chat_history, ""
    
    submit.click(
        respond,
        inputs=[msg, chatbot, language],
        outputs=[chatbot, msg]
    )
    
    msg.submit(
        respond,
        inputs=[msg, chatbot, language],
        outputs=[chatbot, msg]
    )
    
    clear.click(lambda: [], outputs=[chatbot])
    
    gr.Markdown(
        """
        ---
        Built with ❤️ by **Amal SP** | Data Science & AI Enthusiast | March 2026
        
        **Project Goal**: Demonstrate multilingual AI capabilities with production-ready deployment
        """
    )

if __name__ == "__main__":
    demo.launch()