Update app.py

app.py

@@ -24,6 +24,47 @@ with gr.Blocks() as demo:
 
 demo.launch()
 
+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+# Model और Tokenizer लोड करें (आप चाहें तो कोई और चैट मॉडल भी ले सकते हैं)
+model_name = "microsoft/DialoGPT-medium"   # या "mistralai/Mistral-7B-Instruct-v0.2" (अगर Spaces पर चलता है)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+def respond_to_message(message, chat_history):
+    # Chat history को एक स्ट्रिंग में जोड़ें
+    chat_input = ""
+    for user, bot in chat_history:
+        chat_input += f"User: {user}\nBot: {bot}\n"
+    chat_input += f"User: {message}\nBot:"
+
+    # Encode input
+    input_ids = tokenizer.encode(chat_input, return_tensors="pt")
+    # Generate response
+    output = model.generate(
+        input_ids,
+        max_length=input_ids.shape[1] + 64,
+        pad_token_id=tokenizer.eos_token_id,
+        do_sample=True,
+        top_k=50,
+        top_p=0.95
+    )
+    response = tokenizer.decode(output[0][input_ids.shape[1]:], skip_special_tokens=True)
+    chat_history.append((message, response.strip()))
+    return "", chat_history
+
+with gr.Blocks() as demo:
+    chatbot = gr.Chatbot(label="AI चैट बोर्ड")
+    msg = gr.Textbox(label="आपका मैसेज")
+    clear = gr.ClearButton([msg, chatbot])
+
+    msg.submit(respond_to_message, [msg, chatbot], [msg, chatbot])
+
+demo.launch()
+
+
 from datasets import load_dataset
 
 # Login using e.g. `huggingface-cli login` to access this dataset