update app

app.py

@@ -1,43 +1,24 @@
-from transformers import AutoTokenizer, AutoModelForCausalLM
-import torch
 import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM
 from gtts import gTTS
-from langdetect import detect
 import tempfile
+from langdetect import detect
 
-model_name = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
-
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float32)
+# Load tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
+model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")
 
 def respond(user_input):
     if not user_input:
-        return "Please ask something.", None
+        return "Please enter a question.", None
 
     # Detect language
-    detected_lang = detect(user_input)
+    try:
+        detected_lang = detect(user_input)
+    except:
+        detected_lang = "en"
 
-    # Prepare prompt
+    # Generate AI response
     prompt = f"[INST] {user_input} [/INST]"
     inputs = tokenizer(prompt, return_tensors="pt")
-    outputs = model.generate(**inputs, max_new_tokens=256, pad_token_id=tokenizer.eos_token_id)
-
-    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
-
-    # Save voice response to a temporary file (Hugging Face compatible)
-    tts = gTTS(text=response, lang='hi' if detected_lang == 'hi' else 'en')
-    temp_audio = tempfile.NamedTemporaryFile(delete=False, suffix=".mp3")
-    tts.save(temp_audio.name)
-
-    return response, temp_audio.name
-
-iface = gr.Interface(
-    fn=respond,
-    inputs=gr.Textbox(lines=2, placeholder="Ask me anything..."),
-    outputs=[gr.Textbox(label="TeachMe Says"), gr.Audio(label="Voice", autoplay=True)],
-    title="TeachMe - Your Smart Tutor",
-    description="Light AI bot with Hindi + English voice support."
-)
-
-if __name__ == "__main__":
-    iface.launch()
+    outputs = model.generate(**inputs, max_new_**