Update app.py

app.py

@@ -2,49 +2,29 @@ import gradio as gr
 from transformers import pipeline
 
 # Load the text generation pipeline
-pipe = pipeline("text-generation", model="microsoft/Phi-3.5-mini-instruct", trust_remote_code=True)
+pipe = pipeline("text-generation", model="microsoft/phi-4", trust_remote_code=True)
 
 # Define the function for text generation
-def generate(user_input):
-    try:
-        # System prompt and user input
-        system_prompt = "You are a text generator. Your task is to generate text that is requested by the user. Also make sure this looks simple."
-        messages = [
-            {"role": "system", "content": system_prompt},
-            {"role": "user", "content": user_input}
-        ]
-        # Generate a response using the pipeline
-        result = pipe(messages, max_length=100, num_return_sequences=1)
-        # Format the response
-        if result and isinstance(result, list) and 'generated_text' in result[0]:
-            response_text = result[0]['generated_text']
-            return [
-                {"role": "system", "content": system_prompt},
-                {"role": "user", "content": user_input},
-                {"role": "assistant", "content": response_text}
-            ]
-        else:
-            return [{"role": "error", "content": "Error: No valid response generated."}]
-    except Exception as e:
-        return [{"role": "error", "content": f"Error: {e}"}]
+def generate(text):
+    result = generator(text, max_length=100, num_return_sequences=1)
+    return result[0]['generated_text']
 
 # Define examples
 examples = [
-    ["What is the capital of France?"],
-    ["Generate a poem about nature."],
-    ["Write a story about a brave knight."],
-    ["Explain the concept of reinforcement learning."]
+    ["What is the fundamental difference between supervised and unsupervised learning"],
+    ["What is overfitting in supervised learning"],
+    ["What is a convolutional neural network"],
+    ["Describe the concept of transfer learning and its significance in deep learning"]
+
 ]
 
 # Create the Gradio interface
 run = gr.Interface(
     fn=generate,
     inputs=gr.Textbox(lines=5, label="Input Text"),
-    outputs=gr.JSON(label="Generated Conversation"),
-    examples=examples,
-    cache_examples=True
+    outputs=gr.Textbox(label="Generated Text"),
+    examples=examples
 )
 
 # Launch the app
-if __name__ == "__main__":
-    run.launch()
+run.launch()