Update app.py

app.py

@@ -1,63 +1,74 @@
+from language_model import TransformersAVG
+
+import os
+
+import pandas as pd
 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)",
+import torch
+
+
+
+def predict(title, type='End2End', num_beams=4):
+    if type == "End2End":
+        model = TransformersAVG('av-generation/t5-small-end2end-ae-110k', use_auth_token=auth_token)
+        predictions = model.generate_av_end2end(title, num_beams=num_beams)   
+    elif type == "Pipeline":
+        model = TransformersAVG('av-generation/t5-small-ag-ae-110k', model_ve='ksabeh/t5-small-ve-ae-110k', use_auth_token=auth_token)
+        predictions = model.generate_av_pipeline(title, num_beams=num_beams)
+    elif type == 'Multitask':
+        model = TransformersAVG('av-generation/t5-small-mlt-ae-110k', use_auth_token=auth_token)
+        predictions = model.generate_av_mul(title, num_beams=num_beams)
+    else:
+        pass
+    df = pd.DataFrame(predictions, columns=['Attribute', 'Value'])
+    return gr.Dataframe(df)
+
+# with gr.Blocks() as demo:
+#     gr.Markdown("""
+#     # Attribute Value Generation
+#     Select Model and AVG Type,  Type into the text box, and click RUN to get Attributes and Values generated by AI.
+#     """)
+
+#     title = gr.Textbox(
+#             label = "Title",
+#             info = "Title of product",
+#             lines = 2,
+#         )
+#     type = gr.Dropdown(
+#             ["End2End", "Pipeline", "Multitask"], value=["End2End"], multiselect=False, label="AVG Type", info="Select type of AVG approach.")
+    
+#     num_beams = gr.Slider(1, 10, value=4, step=1, label="Number of Beams", info="Degree of exploration at inference.")
+#     run_btn = gr.Button("Run")
+#     output = gr.Dataframe(label="Output Attribute Values")
+#     run_btn.click(fn=predict, inputs=[title, type, num_beams], outputs=output, api_name='predict')
+
+# demo.launch()
+
+
+demo = gr.Interface(
+    predict,
+    [
+        gr.Textbox(
+            label = "Title",
+            info = "Title of product",
+            lines = 2,
         ),
+        gr.Dropdown(
+            ["End2End", "Pipeline", "Multitask"], value=["End2End"], multiselect=False, label="AVG Type", info="Select type of AVG approach."),
+
+        gr.Slider(1, 10, value=4, step=1, label="Number of Beams", info="Degree of exploration at inference.")
     ],
+    "dataframe",
+    title="Attribute Value Generation",
+    examples=[["Women/Girls Tap Dance Shoes Patent Leather Shiny Red /Black/White Tap Shoes for Kids Teacher Practice Performance Shoes T30"], 
+              ["2018 Nike Dunk High Premium SB Men's Breathable Hard-wearing Skateboarding Shoes NIKE Sports Sneakers 313171-674"],
+              ["New style of high-quality custom LP electric guitar, Abalone Flower inlaid fingerboard electric guitar, maple top, free shipping"],
+              ["2Pcs Glasses Side Protection Optical Aye Mate Universal Sideshield Side Shields  Cycling Eyewear"],
+              ["Li-Ning 2018 Men Wade Series Jersey Regular Fit 81% Polyester 19% Spandex Breathable Tops Li Ning Sports T-Shirts Tee ATSN149"],
+              ["LASPERAL Autumn Winter Fitness Men Running Jackets Coat Sports PU Leather Patchwork Long Sleeve Slim Gym Soccer Baseball Jackets"],
+              ["Original New Arrival Authentic NIKE AIR ZOOM VOMERO V12 Men's Breathable Running Shoes Sports Comfortable Sneakers 863762-008"]
+              ],
+    cache_examples = True
 )
 
-
-if __name__ == "__main__":
-    demo.launch()
+demo.launch()