Create app.py

app.py

@@ -0,0 +1,32 @@
+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Replace with your Hugging Face model path
+MODEL_NAME = "username/model-name"
+
+# Load the model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+model = AutoModelForCausalLM.from_pretrained(MODEL_NAME)
+
+# Define the function to interact with the model
+def chat_with_model(input_text, max_length=100):
+    inputs = tokenizer(input_text, return_tensors="pt")
+    outputs = model.generate(inputs.input_ids, max_length=max_length, num_return_sequences=1, do_sample=True)
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return response
+
+# Set up the Gradio interface
+interface = gr.Interface(
+    fn=chat_with_model,
+    inputs=[
+        gr.Textbox(lines=5, placeholder="Enter your text here...", label="Input Text"),
+        gr.Slider(minimum=50, maximum=500, step=10, value=100, label="Max Length")
+    ],
+    outputs=gr.Textbox(lines=5, label="Response"),
+    title="Conversational Model",
+    description="A conversational chatbot powered by a Hugging Face model.",
+)
+
+# Launch the Gradio app
+if __name__ == "__main__":
+    interface.launch()