Update app.py

app.py

@@ -1,63 +1,199 @@
+from __future__ import annotations
+from typing import Iterable
 import gradio as gr
-from transformers import pipeline
+from gradio.themes.base import Base
+from gradio.themes.utils import colors, fonts, sizes
+import time
 import torch
-
-theme = gr.themes.Monochrome(
-    primary_hue="indigo",
-    secondary_hue="blue",
-    neutral_hue="slate",
-    radius_size=gr.themes.sizes.radius_sm,
-    font=[gr.themes.GoogleFont("Open Sans"), "ui-sans-serif", "system-ui", "sans-serif"],
-)
+from transformers import pipeline
+import pandas as pd
 
 instruct_pipeline = pipeline(model="databricks/dolly-v2-12b", torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto")
-def generate(instruction): 
-    return instruct_pipeline(instruction)
 
 
-examples = [
-    "Instead of making a peanut butter and jelly sandwich, what else could I combine peanut butter with in a sandwich? Give five ideas",
-    "How do I make a campfire?",
-    "Write me a tweet about the launch of Dolly 2.0, a new LLM"
-   
-]
+def run_pipeline(prompt):
+    response = instruct_pipeline(prompt)
+    return response
+
+def get_user_input(input_question, history):
+    return "", history + [[input_question, None]]
+
+def get_qa_user_input(input_question, history):
+    return "", history + [[input_question, None]]
+
+def dolly_chat(history):
+    prompt = history[-1][0]
+    bot_message = run_pipeline(prompt)
+    history[-1][1] = bot_message
+    return history
+
+def qa_bot(context, history):
+    query = history[-1][0]
+    prompt = f'instruction: {query} \ncontext: {context}'
+    bot_message = run_pipeline(prompt)
+    history[-1][1] = bot_message
+    return history
+
+def reset_chatbot():
+    return gr.update(value="")
+
+def load_customer_support_example():
+    df = pd.read_csv("examples.csv")
+    return df['doc'].iloc[0], df['question'].iloc[0]
 
+def load_databricks_doc_example():
+    df = pd.read_csv("examples.csv")
+    return df['doc'].iloc[1], df['question'].iloc[1]
 
-def process_example(args):
-    for x in generate(args):
-        pass
-    return x
+# Referred & modified from https://gradio.app/theming-guide/
+class SeafoamCustom(Base):
+    def __init__(
+        self,
+        *,
+        primary_hue: colors.Color | str = colors.emerald,
+        secondary_hue: colors.Color | str = colors.blue,
+        neutral_hue: colors.Color | str = colors.blue,
+        spacing_size: sizes.Size | str = sizes.spacing_md,
+        radius_size: sizes.Size | str = sizes.radius_md,
+        font: fonts.Font
+        | str
+        | Iterable[fonts.Font | str] = (
+            fonts.GoogleFont("Quicksand"),
+            "ui-sans-serif",
+            "sans-serif",
+        ),
+        font_mono: fonts.Font
+        | str
+        | Iterable[fonts.Font | str] = (
+            fonts.GoogleFont("IBM Plex Mono"),
+            "ui-monospace",
+            "monospace",
+        ),
+    ):
+        super().__init__(
+            primary_hue=primary_hue,
+            secondary_hue=secondary_hue,
+            neutral_hue=neutral_hue,
+            spacing_size=spacing_size,
+            radius_size=radius_size,
+            font=font,
+            font_mono=font_mono,
+        )
+        super().set(
+            button_primary_background_fill="linear-gradient(90deg, *primary_300, *secondary_400)",
+            button_primary_background_fill_hover="linear-gradient(90deg, *primary_200, *secondary_300)",
+            button_primary_text_color="white",
+            button_primary_background_fill_dark="linear-gradient(90deg, *primary_600, *secondary_800)",
+            block_shadow="*shadow_drop_lg",
+            button_shadow="*shadow_drop_lg",
+            input_background_fill="zinc",
+            input_border_color="*secondary_300",
+            input_shadow="*shadow_drop",
+            input_shadow_focus="*shadow_drop_lg",
+        )
+
+
+seafoam = SeafoamCustom()
+
+with gr.Blocks(theme=seafoam) as demo:
 
-css = ".generating {visibility: hidden}"
+    with gr.Row(variant='panel'):
+      with gr.Column():
+          gr.HTML(
+              """<html><img src='file/dolly.jpg', alt='dolly logo', width=150, height=150 /><br></html>"""
+          )
+      with gr.Column():
+          gr.Markdown("# **<p align='center'>Dolly 2.0: World's First Truly Open Instruction-Tuned LLM</p>**")
+          gr.Markdown("Dolly 2.0, the first open source, instruction-following LLM, fine-tuned on a human-generated instruction dataset licensed for research and commercial use. It's a 12B parameter language model based on the EleutherAI pythia model family and fine-tuned exclusively on a new, high-quality human generated instruction following dataset, crowdsourced among Databricks employees.")
 
-with gr.Blocks(theme=theme, analytics_enabled=False, css=css) as demo:
-    with gr.Column():
-        gr.Markdown(
-            """ ## Dolly 2.0
-            Dolly 2.0 is a 12B parameter language model based on the EleutherAI pythia model family and fine-tuned exclusively on a new, high-quality human generated instruction following dataset, crowdsourced among Databricks employees. For more details, please refer to the [model card](https://huggingface.co/databricks/dolly-v2-12b)
+      
+
+    qa_bot_state = gr.State(value=[])
+
+    with gr.Tabs():
+        with gr.TabItem("Dolly Chat"):
             
-            Type in the box below and click the button to generate answers to your most pressing questions!
+            with gr.Row():
+
+                with gr.Column():
+                    chatbot = gr.Chatbot(label="Chat History")
+                    input_question = gr.Text(
+                        label="Instruction",
+                        placeholder="Type prompt and hit enter.",
+                    )
+                    clear = gr.Button("Clear", variant="primary")
+
+            with gr.Row():
+                with gr.Accordion("Show example inputs I can load:", open=False):
+                    gr.Examples(
+                        [
+                            ["Explain to me the difference between nuclear fission and fusion."],
+                            ["Give me a list of 5 science fiction books I should read next."],
+                            ["I'm selling my Nikon D-750, write a short blurb for my ad."],
+                            ["Write a song about sour donuts"],
+                            ["Write a tweet about a new book launch by J.K. Rowling."],
+                          
+                        ],
+                        [input_question],
+                        [],
+                        None,
+                        cache_examples=False,
+                    )
+
+        with gr.TabItem("Q&A with Context"):
             
-      """
-        )
-        with gr.Row():
-            with gr.Column(scale=3):
-                instruction = gr.Textbox(placeholder="Enter your question here", label="Question", elem_id="q-input")
-
-                with gr.Box():
-                    gr.Markdown("**Answer**")
-                    output = gr.Markdown(elem_id="q-output")
-                submit = gr.Button("Generate", variant="primary")
-                gr.Examples(
-                    examples=examples,
-                    inputs=[instruction],
-                    cache_examples=False,
-                    fn=process_example,
-                    outputs=[output],
-                )
-
-
-    submit.click(generate, inputs=[instruction], outputs=[output])
-    instruction.submit(generate, inputs=[instruction], outputs=[output])
-
-demo.queue(concurrency_count=16).launch(debug=True)
+            with gr.Row():
+
+                with gr.Column():
+                    input_context = gr.Text(label="Add context here", lines=10)
+
+                with gr.Column():
+                    qa_chatbot = gr.Chatbot(label="Q&A History")
+                    qa_input_question = gr.Text(
+                        label="Input Question",
+                        placeholder="Type question here and hit enter.",
+                    )
+                    qa_clear = gr.Button("Clear", variant="primary")
+
+            with gr.Row():
+                with gr.Accordion("Show example inputs I can load:", open=False):
+                    example_1 = gr.Button("Load Customer support example")
+                    example_2 = gr.Button("Load Databricks documentation example")
+
+
+    input_question.submit(
+        get_user_input,
+        [input_question, chatbot],
+        [input_question, chatbot],
+    ).then(dolly_chat, [chatbot], chatbot)
+
+
+    clear.click(lambda: None, None, chatbot)
+
+
+    qa_input_question.submit(
+        get_qa_user_input,
+        [qa_input_question, qa_chatbot],
+        [qa_input_question, qa_chatbot],
+    ).then(qa_bot, [input_context, qa_chatbot], qa_chatbot)
+
+    qa_clear.click(lambda: None, None, qa_chatbot)
+
+    # reset the chatbot Q&A history when input context changes
+    input_context.change(fn=reset_chatbot, inputs=[], outputs=qa_chatbot)
+
+    example_1.click(
+        load_customer_support_example,
+        [],
+        [input_context, qa_input_question],
+    )
+
+    example_2.click(
+        load_databricks_doc_example,
+        [],
+        [input_context, qa_input_question],
+    )
+
+if __name__ == "__main__":
+
+    demo.queue(concurrency_count=1,max_size=100).launch(max_threads=5,debug=True)