Update app.py

app.py

@@ -1,60 +1,22 @@
 import gradio as gr
-from unsloth import FastLanguageModel
 import torch
 
-# Load your model
-model, tokenizer = FastLanguageModel.from_pretrained(
-    model_name="alaamostafa/Mistral-7B-Unsloth",
-    max_seq_length=2048,
-    load_in_4bit=True,
-)
+# This is a simplified version showing just the UI structure
+# You'll need to integrate your actual model loading and generation code
 
-# Enable faster inference
-FastLanguageModel.for_inference(model)
+def generate_text(prompt, max_length, temperature, top_p, top_k, repetition_penalty):
+    # Your text generation function would go here
+    # This is just a placeholder that returns the prompt as a demonstration
+    return f"Generated text based on: {prompt}"
 
-# Set up chat template
-from unsloth.chat_templates import get_chat_template
-tokenizer = get_chat_template(
-    tokenizer,
-    chat_template="chatml",
-    mapping={"role": "from", "content": "value", "user": "human", "assistant": "gpt"},
-    map_eos_token=True,
-)
-
-# Neuroscience example prompts
-example_prompts = [
-    "Recent advances in neuroimaging suggest that",
-    "The role of dopamine in learning and memory involves",
-    "Explain the concept of neuroplasticity in simple terms",
-    "What are the key differences between neurons and glial cells?"
-]
-
-# Text generation function with parameters
-def generate_text(prompt, max_new_tokens, temperature, top_p, top_k, repetition_penalty):
-    # Prepare input
-    inputs = tokenizer(prompt, return_tensors="pt").to("cuda" if torch.cuda.is_available() else "cpu")
-    
-    # Generate response
-    outputs = model.generate(
-        input_ids=inputs["input_ids"],
-        max_new_tokens=int(max_new_tokens),
-        temperature=float(temperature),
-        top_p=float(top_p),
-        top_k=int(top_k),
-        repetition_penalty=float(repetition_penalty),
-        use_cache=True
-    )
-    
-    response = tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)
-    return response
-
-# Create Gradio interface with layout matching your second image
+# Create the interface with a layout matching image 2
 with gr.Blocks() as demo:
     with gr.Row():
+        # Left column - Input
         with gr.Column():
             prompt = gr.Textbox(
                 label="Enter your prompt",
-                value="",
+                placeholder="Type your prompt here...",
                 lines=5
             )
             
@@ -88,12 +50,26 @@ with gr.Blocks() as demo:
                     minimum=1.0, maximum=2.0, value=1.1, step=0.1
                 )
         
+        # Right column - Output
         with gr.Column():
             output = gr.Textbox(
                 label="Generated Text",
                 lines=20
             )
     
+    # Example prompts
+    example_prompts = [
+        "Recent advances in neuroimaging suggest that",
+        "The role of dopamine in learning and memory involves",
+        "Explain the concept of neuroplasticity in simple terms",
+        "What are the key differences between neurons and glial cells?"
+    ]
+    
+    gr.Examples(
+        examples=example_prompts,
+        inputs=prompt
+    )
+    
     # Set up event handlers
     generate_btn.click(
         fn=generate_text,
@@ -106,12 +82,6 @@ with gr.Blocks() as demo:
         inputs=None,
         outputs=[prompt, output]
     )
-    
-    # Example prompts
-    gr.Examples(
-        examples=example_prompts,
-        inputs=prompt
-    )
 
 # Launch the app
 demo.launch()