Create app.py

app.py

@@ -0,0 +1,31 @@
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+
+# Load the finetuned model and tokenizer from Hugging Face Model Hub
+model_path = "sagar007/phi3.5_finetune"
+tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True, device_map="auto")
+
+# Create a text-generation pipeline
+generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
+
+def generate_text(prompt, max_length=100, temperature=0.7):
+    """Generate text based on the input prompt."""
+    generated = generator(prompt, max_length=max_length, temperature=temperature, num_return_sequences=1)
+    return generated[0]['generated_text']
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=generate_text,
+    inputs=[
+        gr.Textbox(lines=5, label="Enter your prompt"),
+        gr.Slider(minimum=50, maximum=500, value=100, step=10, label="Max Length"),
+        gr.Slider(minimum=0.1, maximum=1.0, value=0.7, step=0.1, label="Temperature"),
+    ],
+    outputs=gr.Textbox(lines=10, label="Generated Text"),
+    title="Finetuned Phi-3.5 Text Generation",
+    description="Enter a prompt and generate text using the finetuned Phi-3.5 model.",
+)
+
+# Launch the app
+iface.launch()