Update space

app.py

@@ -4,59 +4,88 @@ 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("karanzrk/bert-Causal-QA")
+# client = InferenceClient("karanzrk/bert-Causal-QA")
 
+from transformers import pipeline
+generator = pipeline('text-generation', model = 'karanzrk/bert-Causal-QA')
+# generator("Hello, I'm a language model", max_length = 30, num_return_sequences=3)
 
-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})
+# def respond(
+#     message,
+#     max_tokens,
 
-    response = ""
+# ):
+#     messages = [{"role": "system", "content": system_message}]
 
-    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
+#     for val in history:
+#         if val[0]:
+#             messages.append({"role": "user", "content": val[0]})
+#         if val[1]:
+#             messages.append({"role": "assistant", "content": val[1]})
 
-        response += token
-        yield response
+#     messages.append({"role": "user", "content": message})
 
-"""
-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="Question: ", label="System message"),
-        gr.Slider(minimum=1, maximum=128, 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)",
-        ),
-    ],
-)
+#     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="Question: ", label="System message"),
+#         gr.Slider(minimum=1, maximum=128, 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)",
+#         ),
+#     ],
+# )
+
+def inference(text):
+    # classifier = pipeline("text-classification", model="karanzrk/essayl0")
+    text = "Question: " + text
+    output = generator(text)
+    return output
+
+# launcher = gr.Interface(
+#     fn=inference,
+#     inputs=gr.Textbox(lines=5, placeholder="Essay here...."),
+#     outputs="text"
+# )
+
+with gr.Blocks() as demo:
+    gr.Markdown(
+        """
+        # Welcome to bert-demo
+        Ask your question
+        """
+    )
+    inputs = gr.Textbox(label="Input Box",lines = 5, placeholder="Question: ")
+    button = gr.Button("Grade!")
+    output = gr.Textbox(label="Output Box")
+    button.click(fn=inference, inputs=inputs, outputs = output, api_name="Autograde")
+    
+
+demo.launch()
 
 
 if __name__ == "__main__":