Create app.py

app.py

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+import gradio as gr
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+import os
+
+# Set up model parameters
+MODEL_ID = "alaamostafa/Microsoft-Phi-2"
+BASE_MODEL_ID = "microsoft/phi-2"
+
+# Check if CUDA is available and set device accordingly
+device = "cuda" if torch.cuda.is_available() else "cpu"
+print(f"Using device: {device}")
+
+# Load the tokenizer
+tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL_ID)
+
+# Load base model with appropriate dtype based on available hardware
+print("Loading base model...")
+base_model = AutoModelForCausalLM.from_pretrained(
+    BASE_MODEL_ID,
+    torch_dtype=torch.float16 if device == "cuda" else torch.float32,
+    trust_remote_code=True,
+    device_map="auto"
+)
+
+# Load the fine-tuned adapter
+print(f"Loading adapter from {MODEL_ID}...")
+model = PeftModel.from_pretrained(
+    base_model,
+    MODEL_ID,
+    device_map="auto"
+)
+
+print("Model loaded successfully!")
+
+def generate_text(
+    prompt, 
+    max_length=512, 
+    temperature=0.7, 
+    top_p=0.9, 
+    top_k=40, 
+    repetition_penalty=1.1
+):
+    """Generate text based on prompt with the fine-tuned model"""
+    # Prepare input
+    inputs = tokenizer(prompt, return_tensors="pt").to(device)
+    
+    # Generate text
+    with torch.no_grad():
+        outputs = model.generate(
+            **inputs,
+            max_length=max_length,
+            temperature=temperature,
+            top_p=top_p,
+            top_k=top_k,
+            repetition_penalty=repetition_penalty,
+            do_sample=temperature > 0,
+            pad_token_id=tokenizer.eos_token_id
+        )
+    
+    # Decode and return the generated text
+    generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return generated_text
+
+# Create the Gradio interface
+css = """
+.gradio-container {max-width: 800px !important}
+.gr-prose code {white-space: pre-wrap !important}
+"""
+
+title = "Neuroscience Fine-tuned Phi-2 Model"
+description = """
+This is a fine-tuned version of Microsoft's Phi-2 model, adapted specifically for neuroscience domain content.
+Use this interface to interact with the model and see how it handles neuroscience-related queries.
+
+**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?
+"""
+
+with gr.Blocks(css=css) as demo:
+    gr.Markdown(f"# {title}")
+    gr.Markdown(description)
+    
+    with gr.Row():
+        with gr.Column():
+            prompt = gr.Textbox(
+                label="Enter your prompt",
+                placeholder="Recent advances in neuroscience suggest that",
+                lines=5
+            )
+            
+            with gr.Row():
+                submit_btn = gr.Button("Generate", variant="primary")
+                clear_btn = gr.Button("Clear")
+            
+            with gr.Accordion("Advanced Options", open=False):
+                max_length = gr.Slider(
+                    minimum=64, maximum=1024, value=512, step=64,
+                    label="Maximum Length"
+                )
+                temperature = gr.Slider(
+                    minimum=0.0, maximum=1.5, value=0.7, step=0.1,
+                    label="Temperature (0 = deterministic, 0.7 = creative, 1.5 = random)"
+                )
+                top_p = gr.Slider(
+                    minimum=0.1, maximum=1.0, value=0.9, step=0.1,
+                    label="Top-p (nucleus sampling)"
+                )
+                top_k = gr.Slider(
+                    minimum=1, maximum=100, value=40, step=1,
+                    label="Top-k"
+                )
+                repetition_penalty = gr.Slider(
+                    minimum=1.0, maximum=2.0, value=1.1, step=0.1,
+                    label="Repetition Penalty"
+                )
+            
+        with gr.Column():
+            output = gr.Textbox(
+                label="Generated Text",
+                lines=20
+            )
+    
+    # Set up event handlers
+    submit_btn.click(
+        fn=generate_text,
+        inputs=[prompt, max_length, temperature, top_p, top_k, repetition_penalty],
+        outputs=output
+    )
+    clear_btn.click(
+        fn=lambda: ("", None),
+        inputs=None,
+        outputs=[prompt, output]
+    )
+    
+    # Example prompts
+    examples = [
+        ["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=examples,
+        inputs=prompt
+    )
+
+# Launch the app
+demo.launch()