Update app.py

app.py

@@ -1,108 +1,54 @@
-# import gradio as gr
-# from huggingface_hub import InferenceClient
-# import os
+import gradio as gr
+from huggingface_hub import InferenceClient
+import os
 
-# client = InferenceClient(
-#     model="mistralai/Mistral-Small-24B-Instruct-2501",
-#     token=os.getenv('HF_TOKEN')
-# )
+client = InferenceClient(
+    model="mistralai/Mistral-7B-Instruct-v0.3",
+    token=os.getenv('HF_TOKEN')
+)
 
-# def chat_fn(message, system_message, history_str, max_tokens, temperature, top_p):
-#     # Convert history string (optional) to message list
-#     messages = [{"role": "system", "content": system_message}]
+def chat_fn(message, system_message, history_str, max_tokens, temperature, top_p):
+    # Convert history string (optional) to message list
+    messages = [{"role": "system", "content": system_message}]
     
-#     if history_str:
-#         # Format: user1||assistant1\nuser2||assistant2
-#         for pair in history_str.split("\n"):
-#             if "||" in pair:
-#                 user_msg, assistant_msg = pair.split("||", 1)
-#                 messages.append({"role": "user", "content": user_msg})
-#                 messages.append({"role": "assistant", "content": assistant_msg})
+    if history_str:
+        # Format: user1||assistant1\nuser2||assistant2
+        for pair in history_str.split("\n"):
+            if "||" in pair:
+                user_msg, assistant_msg = pair.split("||", 1)
+                messages.append({"role": "user", "content": user_msg})
+                messages.append({"role": "assistant", "content": assistant_msg})
     
-#     messages.append({"role": "user", "content": message})
-
-#     # Get response from HF
-#     response = ""
-#     for chunk in client.chat_completion(
-#         messages=messages,
-#         stream=True,
-#         max_tokens=max_tokens,
-#         temperature=temperature,
-#         top_p=top_p,
-#     ):
-#         response += chunk.choices[0].delta.content or ""
+    messages.append({"role": "user", "content": message})
+
+    # Get response from HF
+    response = ""
+    for chunk in client.chat_completion(
+        messages=messages,
+        stream=True,
+        max_tokens=max_tokens,
+        temperature=temperature,
+        top_p=top_p,
+    ):
+        response += chunk.choices[0].delta.content or ""
     
-#     return response
-
-# demo = gr.Interface(
-#     fn=chat_fn,
-#     inputs=[
-#         gr.Textbox(lines=2, label="User Message"),
-#         gr.Textbox(value="You are a friendly Chatbot.", label="System Prompt"),
-#         gr.Textbox(lines=4, placeholder="user||bot\nuser2||bot2", label="Conversation History (optional)"),
-#         gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max Tokens"),
-#         gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-#         gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p"),
-#     ],
-#     outputs="text",
-#     allow_flagging="never",
-#     title="LLM Budaya",
-#     description="Chatbot menggunakan model HuggingFace Zephyr-7B"
-# )
-
-# if __name__ == "__main__":
-#     demo.launch()
-
-import gradio as gr
-import torch
-from transformers import AutoTokenizer, AutoModelForCausalLM
-
-# Load model & tokenizer
-model_id = "mistralai/Mistral-Small-24B-Instruct-2501"
-tokenizer = AutoTokenizer.from_pretrained(model_id)
+    return response
 
-# Load model di CPU
-model = AutoModelForCausalLM.from_pretrained(
-    model_id,
-    torch_dtype=torch.float32,
-    device_map={"": "cpu"}
-)
-
-# Inference function
-def chat_fn(message, system_prompt, max_tokens, temperature, top_p):
-    prompt = f"<s>[INST] {system_prompt.strip()}\n{message.strip()} [/INST]"
-
-    inputs = tokenizer(prompt, return_tensors="pt").to("cpu")
-
-    with torch.no_grad():
-        output = model.generate(
-            **inputs,
-            max_new_tokens=max_tokens,
-            temperature=temperature,
-            top_p=top_p,
-            do_sample=True,
-            pad_token_id=tokenizer.eos_token_id
-        )
-
-    decoded = tokenizer.decode(output[0], skip_special_tokens=True)
-    return decoded.split("[/INST]")[-1].strip()
-
-# Gradio UI
 demo = gr.Interface(
     fn=chat_fn,
     inputs=[
         gr.Textbox(lines=2, label="User Message"),
-        gr.Textbox(value="You are a helpful and concise assistant.", label="System Prompt"),
-        gr.Slider(minimum=1, maximum=1024, value=256, step=1, label="Max Tokens"),
+        gr.Textbox(value="You are a friendly Chatbot.", label="System Prompt"),
+        gr.Textbox(lines=4, placeholder="user||bot\nuser2||bot2", label="Conversation History (optional)"),
+        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max Tokens"),
         gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
         gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p"),
     ],
     outputs="text",
-    title="Mistral-Small-24B CPU Chat",
-    description="Chatbot menggunakan model Mistral-Small-24B-Instruct-2501 dijalankan lokal via CPU. Ini akan berjalan lambat.",
-    flagging_mode="never",
+    allow_flagging="never",
+    title="LLM Budaya",
+    description="Chatbot menggunakan model HuggingFace Zephyr-7B"
 )
 
 if __name__ == "__main__":
-    demo.launch()
-
+    demo.launch()