Update app.py

app.py

@@ -1,54 +1,37 @@
 import gradio as gr
-from transformers import AutoModelForCausalLM, AutoTokenizer
-import torch
 import requests
 
-model_name = "microsoft/DialoGPT-small"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForCausalLM.from_pretrained(model_name)
-
-def translate(text, source_lang, target_lang):
-    response = requests.post("https://libretranslate.de/translate", data={
-        "q": text,
-        "source": source_lang,
-        "target": target_lang,
-        "format": "text"
-    })
-    return response.json()["translatedText"]
-
-def detect_language(text):
-    if any(word in text.lower() for word in ["ako", "ikaw", "bakit", "ng", "hindi", "oo"]):
-        return "tl"
-    return "en"
-
-def chat_bot(message, history=[]):
-    lang = detect_language(message)
-
-    if lang == "tl":
-        translated_msg = translate(message, "tl", "en")
-        inputs = tokenizer.encode(translated_msg + tokenizer.eos_token, return_tensors="pt")
-        reply_ids = model.generate(inputs, max_length=100, pad_token_id=tokenizer.eos_token_id)
-        reply_en = tokenizer.decode(reply_ids[:, inputs.shape[-1]:][0], skip_special_tokens=True)
-        reply = translate(reply_en, "en", "tl")
-    else:
-        inputs = tokenizer.encode(message + tokenizer.eos_token, return_tensors="pt")
-        reply_ids = model.generate(inputs, max_length=100, pad_token_id=tokenizer.eos_token_id)
-        reply = tokenizer.decode(reply_ids[:, inputs.shape[-1]:][0], skip_special_tokens=True)
+API_URL = "https://api-inference.huggingface.co/models/meta-llama/Llama-2-7b-chat"
+headers = {
+    # Optional: If rate-limited, use your Hugging Face token here
+    # "Authorization": f"Bearer hf_your_token"
+}
+
+def smart_bot(message):
+    prompt = f"""You are a helpful, friendly chatbot that understands and replies in the same language used by the user. Reply in English or Tagalog depending on the input.
 
-    return reply
+User: {message}
+AI:"""
+
+    response = requests.post(API_URL, headers=headers, json={"inputs": prompt})
+    
+    if response.status_code == 200:
+        result = response.json()
+        reply = result[0]["generated_text"].split("AI:")[-1].strip()
+        return reply
+    else:
+        return "❌ Error: " + response.text
 
-# UI with Gradio
 with gr.Blocks(theme=gr.themes.Soft()) as app:
-    gr.Markdown("<h1 style='text-align: center;'>🤖 Tagalog-English ChatBot</h1>")
-    gr.Markdown("<marquee style='color:black; font-weight:bold'>Created by Niko Jay Sedan</marquee>")
-    gr.Markdown("💬 Magtanong sa Tagalog o English. Sasagot sa parehong wika.")
+    gr.Markdown("<h1 style='text-align: center;'>🤖 Tagalog-English Smart ChatBot</h1>")
+    gr.Markdown("<marquee style='color: black; font-weight: bold;'>Created by Niko Sedan using Hugging Face LLaMA-2</marquee>")
 
     with gr.Row():
         with gr.Column():
-            user_input = gr.Textbox(label="📝 Your Message")
-            submit_btn = gr.Button("Send")
-        bot_output = gr.Textbox(label="🤖 Bot Reply")
+            user_input = gr.Textbox(label="💬 Enter your question here")
+            send_btn = gr.Button("Send")
+        bot_reply = gr.Textbox(label="🤖 Bot's Reply")
 
-    submit_btn.click(fn=chat_bot, inputs=user_input, outputs=bot_output)
+    send_btn.click(fn=smart_bot, inputs=user_input, outputs=bot_reply)
 
 app.launch()