Update app.py

app.py

@@ -1,64 +1,36 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
+from transformers import pipeline
+
+def load_question_generator():
+    question_generator = pipeline('text2text-generation', 
+                                model='your-username/your-model-name')
+    return question_generator
+
+def generate_questions(context, num_questions=3):
+    try:
+        generator = load_question_generator()
+        questions = []
+        
+        for _ in range(num_questions):
+            result = generator(context, max_length=64, num_return_sequences=1)
+            questions.append(result[0]['generated_text'])
+            
+        return "\n\n".join(questions)
+    except Exception as e:
+        return f"Error generating questions: {str(e)}"
+
+iface = gr.Interface(
+    fn=generate_questions,
+    inputs=[
+        gr.Textbox(lines=5, label="Enter your text context"),
+        gr.Slider(minimum=1, maximum=5, value=3, step=1, label="Number of questions")
     ],
+    outputs=gr.Textbox(label="Generated Questions"),
+    title="Question Generator",
+    description="Generate questions from your text using AI",
+    examples=[
+        ["The Sun is the star at the center of the Solar System. It is a nearly perfect sphere of hot plasma, heated to incandescence by nuclear fusion reactions in its core.", 2],
+    ]
 )
 
-
-if __name__ == "__main__":
-    demo.launch()
+iface.launch()