Update app.py

app.py

@@ -1,57 +1,77 @@
 
+model_name = 'redael/model_udc'
+import os
+import gradio as gr
 from transformers import AutoModelForCausalLM, AutoTokenizer
 import torch
-import gradio as gr
 
-model_name = 'redael/model_udc'
-
-# Load the tokenizer and model from Hugging Face
+# Load your model and tokenizer from Hugging Face
+print("l.......")
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForCausalLM.from_pretrained(model_name)
+print("done")
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 model.to(device)
 
-# Define the generate_response function
-def generate_response(prompt, model=model, tokenizer=tokenizer, max_length=100, num_beams=5, temperature=0.5, top_p=0.9, repetition_penalty=4.0):
-    # Add context to the prompt
-    prompt = f"User: {prompt}\nAssistant:"
-
+# Function to generate response
+def generate_response(message, history, system_message, max_tokens, temperature, top_p):
+    # Prepare the conversation history
+    messages = [{"role": "system", "content": system_message}]
+    
+    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})
+    
+    # Tokenize and prepare the input
+    prompt = "\n".join([f"{msg['role'].capitalize()}: {msg['content']}" for msg in messages])
     inputs = tokenizer(prompt, return_tensors='pt', padding=True, truncation=True, max_length=512).to(device)
+    
+    # Generate the response
     outputs = model.generate(
         inputs['input_ids'],
-        max_length=max_length,
+        max_length=max_tokens,
         num_return_sequences=1,
         pad_token_id=tokenizer.eos_token_id,
-        num_beams=num_beams,
         temperature=temperature,
         top_p=top_p,
-        repetition_penalty=repetition_penalty,
         early_stopping=True
     )
     response = tokenizer.decode(outputs[0], skip_special_tokens=True)
     
-    # Clean up the response to make it more natural
-    response = response.replace('User:', '').replace('Assistant:', '').strip()
-    return response
-
-# Define the Gradio interface
-def chatbot_interface(user_input):
-    return generate_response(user_input)
+    # Clean up the response
+    response = response.split("Assistant:")[-1].strip()
+    response_lines = response.split('\n')
+    clean_response = []
+    for line in response_lines:
+        if "User:" not in line and "Assistant:" not in line:
+            clean_response.append(line)
+    response = ' '.join(clean_response)
+    
+    return [(message, response)]
 
-iface = gr.Interface(
-    fn=chatbot_interface,
-    inputs="text",
-    outputs="text",
-    title="Chatbot",
-    description="Ask anything to the chatbot.",
-    examples=[
-        ["What is the command to get a list of all files in /var/cache/apt/archives?"],
-        ["How do I change to root in Ubuntu?"],
-        ["What are the best practices for securing a web server?"]
+# Create the Gradio chat interface
+demo = gr.ChatInterface(
+    fn=generate_response,
+    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)",
+        ),
     ],
-    live=True
+    title="Chatbot",
+    description="Ask anything to the chatbot."
 )
 
-# Launch the Gradio interface
-iface.launch()
+if __name__ == "__main__":
+    demo.launch()