Update app.py

app.py

@@ -1,94 +1,12 @@
-from transformers import AutoTokenizer, AutoModelForCausalLM
-import gradio as gr
-import torch
-
-MODEL_NAME = "docto/Docto-Bot"
-
-# Load tokenizer and model
-tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
-model = AutoModelForCausalLM.from_pretrained(MODEL_NAME)
-
-# Use GPU if available
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model.to(device)
-
-# Set pad token if missing
-if tokenizer.pad_token is None:
-    tokenizer.pad_token = tokenizer.eos_token
-
-
-def get_reply(user_input):
-
-    if not user_input.strip():
-        return "Please enter a question."
-
-    try:
-        # Build prompt
-        prompt = f"Question: {user_input}\nAnswer:"
-
-        # Tokenize
-        inputs = tokenizer(
-            prompt,
-            return_tensors="pt"
-        ).to(device)
-
-        # Generate response
-        outputs = model.generate(
-            **inputs,
-
-            max_new_tokens=150,
-
-            do_sample=True,
-
-            temperature=0.7,
-
-            top_k=50,
-
-            top_p=0.9,
-
-            repetition_penalty=1.15,
-
-            no_repeat_ngram_size=3,
-
-            eos_token_id=tokenizer.eos_token_id,
-
-            pad_token_id=tokenizer.eos_token_id
-        )
-
-        # Decode
-        response = tokenizer.decode(
-            outputs[0],
-            skip_special_tokens=True
-        )
-
-        # Extract answer only
-        if "Answer:" in response:
-            response = response.split("Answer:", 1)[1]
-
-        return response.strip()
-
-    except Exception as e:
-        return f"Error: {e}"
-
-
-# Gradio UI
 iface = gr.Interface(
     fn=get_reply,
-
     inputs=gr.Textbox(
         lines=2,
         placeholder="Ask a question..."
     ),
-
-    outputs=gr.Textbox(
-        label="Bot Response"
-    ),
-
+    outputs=gr.Textbox(label="Bot Response"),
     title="Docto-Bot",
-
-    description="Medical Question Answering Bot",
-
-    allow_flagging="never"
+    description="Medical Question Answering Bot"
 )
 
 iface.launch()