Create app.py

app.py

@@ -0,0 +1,95 @@
+import gradio as gr
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+from peft import PeftModel, PeftConfig
+
+# Load the fine-tuned model and tokenizer
+model_name = "your-username/phi-1_5-finetuned-oasst1"  # Replace with your actual model path
+config = PeftConfig.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, torch_dtype=torch.float16, device_map="auto")
+model = PeftModel.from_pretrained(model, model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# Create a text generation pipeline
+generator = pipeline("text-generation", model=model, tokenizer=tokenizer, device_map="auto")
+
+def generate_text(prompt, max_length=100, temperature=0.7, top_p=0.9):
+    """Generate text based on the input prompt."""
+    generated = generator(prompt, max_length=max_length, do_sample=True, temperature=temperature, top_p=top_p)
+    return generated[0]['generated_text']
+
+# Custom CSS for styling
+custom_css = """
+    body {
+        font-family: 'Arial', sans-serif;
+        background-color: #1a1a1a;
+        color: #ffffff;
+    }
+    .container {
+        max-width: 800px;
+        margin: auto;
+        padding: 20px;
+        background-color: #2a2a2a;
+        border-radius: 10px;
+        box-shadow: 0 0 10px rgba(255,255,255,0.1);
+    }
+    h1 {
+        color: #4a90e2;
+        text-align: center;
+        font-size: 2.5em;
+        margin-bottom: 20px;
+    }
+    .gr-input, .gr-box {
+        background-color: #3a3a3a !important;
+        border: 1px solid #4a4a4a !important;
+        color: #ffffff !important;
+    }
+    .gr-button {
+        background-color: #4a90e2 !important;
+        border: none !important;
+        color: white !important;
+    }
+    .gr-button:hover {
+        background-color: #3a80d2 !important;
+    }
+    .gr-form {
+        border: 1px solid #4a4a4a;
+        padding: 20px;
+        border-radius: 5px;
+        background-color: #2a2a2a;
+    }
+    .footer {
+        text-align: center;
+        margin-top: 20px;
+        font-size: 0.9em;
+        color: #888;
+    }
+"""
+
+# Create the Gradio interface
+with gr.Blocks(css=custom_css) as iface:
+    gr.HTML("<div class='container'>")
+    gr.HTML("<h1>🤖 Phi-1.5 Fine-tuned Text Generator</h1>")
+    
+    with gr.Row():
+        with gr.Column():
+            input_text = gr.Textbox(lines=5, label="Enter your prompt")
+            max_length = gr.Slider(minimum=50, maximum=500, value=100, step=10, label="Max Length")
+            temperature = gr.Slider(minimum=0.1, maximum=1.0, value=0.7, step=0.1, label="Temperature")
+            top_p = gr.Slider(minimum=0.1, maximum=1.0, value=0.9, step=0.1, label="Top P")
+            generate_button = gr.Button("Generate Text", variant="primary")
+
+        with gr.Column():
+            output_text = gr.Textbox(lines=10, label="Generated Text")
+
+    generate_button.click(
+        generate_text,
+        inputs=[input_text, max_length, temperature, top_p],
+        outputs=output_text
+    )
+
+    gr.HTML("<div class='footer'>Powered by Hugging Face and Gradio</div>")
+    gr.HTML("</div>")
+
+# Launch the app
+iface.launch()