Update app.py

app.py

@@ -1,63 +1,57 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
+from transformers import AutoModel, AutoTokenizer
+import torch
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+# Load the model and tokenizer
+model_name = "wop/kosmox-gguf"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModel.from_pretrained(model_name)
 
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
+# Function to generate responses
+def respond(message, history, system_message, max_tokens, temperature, top_p):
+    # Prepare the chat history
     messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
+    
+    for user_msg, bot_msg in history:
+        if user_msg:
+            messages.append({"role": "user", "content": user_msg})
+        if bot_msg:
+            messages.append({"role": "assistant", "content": bot_msg})
+    
     messages.append({"role": "user", "content": message})
 
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
+    # Create the chat input for the model
+    chat_input = tokenizer.chat_template.format(
+        bos_token=tokenizer.bos_token,
+        messages=[{"from": "human", "value": m['content']} if m['role'] == 'user' else {"from": "gpt", "value": m['content']} for m in messages]
+    )
+    
+    inputs = tokenizer(chat_input, return_tensors="pt")
+
+    # Generate response
+    with torch.no_grad():
+        outputs = model.generate(
+            input_ids=inputs['input_ids'],
+            max_length=max_tokens,
+            temperature=temperature,
+            top_p=top_p,
+            do_sample=True
+        )
+    
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    yield response.strip()
+
+# Define the Gradio interface
 demo = gr.ChatInterface(
     respond,
     additional_inputs=[
         gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
         gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new 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 (nucleus sampling)",
-        ),
+        gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"),
     ],
 )
 
-
+# Launch the demo
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()