Update app.py

app.py

@@ -1,36 +1,20 @@
-
-
 import gradio as gr
-from transformers import AutoModelForCausalLM, AutoTokenizer
-import torch
+import openai
 
-# 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)
+# OpenAI API Key (यहाँ अपनी API Key डालें)
+openai.api_key = "YOUR_API_KEY"
 
+# Backend Function: यूजर का मैसेज लेकर OpenAI से रिस्पॉन्स लाता है
 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 = openai.ChatCompletion.create(
+        model="gpt-3.5-turbo",
+        messages=[{"role": "user", "content": message}]
     )
-    response = tokenizer.decode(output[0][input_ids.shape[1]:], skip_special_tokens=True)
-    chat_history.append((message, response.strip()))
+    bot_message = response.choices[0].message['content']
+    chat_history.append((message, bot_message))
     return "", chat_history
 
+# Frontend: Gradio UI
 with gr.Blocks() as demo:
     chatbot = gr.Chatbot(label="AI चैट बोर्ड")
     msg = gr.Textbox(label="आपका मैसेज")
@@ -40,6 +24,7 @@ with gr.Blocks() as demo:
 
 demo.launch()
 
+
 from transformers import GPT2Tokenizer, GPT2LMHeadModel
 
 tokenizer = GPT2Tokenizer.from_pretrained("gpt2")