Upload 3 files

TriageScript.txt

@@ -0,0 +1,6 @@
+How do you treat a serious allergic reaction?
+I take an allergy medication, but it doesn't work as well as I'd like.
+How do you treat serious shock?
+I have to take an epi-pen every time I go to the doctor.
+What do you do if you are confused?
+I just have to keep an eye on my throat and try not to sneeze.

app.py

@@ -0,0 +1,137 @@
+from transformers import BlenderbotTokenizer, BlenderbotForConditionalGeneration
+import torch
+import gradio as gr
+
+
+# PersistDataset -----
+import os
+import csv
+import gradio as gr
+from gradio import inputs, outputs
+import huggingface_hub
+from huggingface_hub import Repository, hf_hub_download, upload_file
+from datetime import datetime
+DATASET_REPO_URL = "https://huggingface.co/datasets/awacke1/Carddata.csv"
+DATASET_REPO_ID = "awacke1/Carddata.csv"
+DATA_FILENAME = "Carddata.csv"
+DATA_FILE = os.path.join("data", DATA_FILENAME)
+HF_TOKEN = os.environ.get("HF_TOKEN")
+
+SCRIPT = """
+<script>
+if (!window.hasBeenRun) {
+    window.hasBeenRun = true;
+    console.log("should only happen once");
+    document.querySelector("button.submit").click();
+}
+</script>
+"""
+
+try:
+    hf_hub_download(
+        repo_id=DATASET_REPO_ID,
+        filename=DATA_FILENAME,
+        cache_dir=DATA_DIRNAME,
+        force_filename=DATA_FILENAME
+    )
+except:
+    print("file not found")
+repo = Repository(
+    local_dir="data", clone_from=DATASET_REPO_URL, use_auth_token=HF_TOKEN
+)
+
+def generate_html() -> str:
+    with open(DATA_FILE) as csvfile:
+        reader = csv.DictReader(csvfile)
+        rows = []
+        for row in reader:
+            rows.append(row)
+        rows.reverse()
+        if len(rows) == 0:
+            return "no messages yet"
+        else:
+            html = "<div class='chatbot'>"
+            for row in rows:
+                html += "<div>"
+                html += f"<span>{row['inputs']}</span>"
+                html += f"<span class='outputs'>{row['outputs']}</span>"
+                html += "</div>"
+            html += "</div>"
+            return html
+            
+def store_message(name: str, message: str):
+    if name and message:
+        with open(DATA_FILE, "a") as csvfile:
+            writer = csv.DictWriter(csvfile, fieldnames=["name", "message", "time"])
+            writer.writerow(
+                {"name": name.strip(), "message": message.strip(), "time": str(datetime.now())}
+            )
+        commit_url = repo.push_to_hub()
+    return ""
+    
+iface = gr.Interface(
+    store_message,
+    [
+        inputs.Textbox(placeholder="Your name"),
+        inputs.Textbox(placeholder="Your message", lines=2),
+    ],
+    "html",
+    css="""
+    .message {background-color:cornflowerblue;color:white; padding:4px;margin:4px;border-radius:4px; }
+    """,
+    title="Reading/writing to a HuggingFace dataset repo from Spaces",
+    description=f"This is a demo of how to do simple *shared data persistence* in a Gradio Space, backed by a dataset repo.",
+    article=f"The dataset repo is [{DATASET_REPO_URL}]({DATASET_REPO_URL})",
+)
+
+
+mname = "facebook/blenderbot-400M-distill"
+model = BlenderbotForConditionalGeneration.from_pretrained(mname)
+tokenizer = BlenderbotTokenizer.from_pretrained(mname)
+
+def take_last_tokens(inputs, note_history, history):
+    """Filter the last 128 tokens"""
+    if inputs['input_ids'].shape[1] > 128:
+        inputs['input_ids'] = torch.tensor([inputs['input_ids'][0][-128:].tolist()])
+        inputs['attention_mask'] = torch.tensor([inputs['attention_mask'][0][-128:].tolist()])
+        note_history = ['</s> <s>'.join(note_history[0].split('</s> <s>')[2:])]
+        history = history[1:]
+    return inputs, note_history, history
+
+def add_note_to_history(note, note_history):
+    """Add a note to the historical information"""
+    note_history.append(note)
+    note_history = '</s> <s>'.join(note_history)
+    return [note_history]
+
+title = "Chatbot State of the Art now with Memory Saved to Dataset"
+description = """Chatbot With Memory"""
+
+def chat(message, history):
+    history = history or []
+    if history: 
+        history_useful = ['</s> <s>'.join([str(a[0])+'</s> <s>'+str(a[1]) for a in history])]
+    else:
+        history_useful = []
+    history_useful = add_note_to_history(message, history_useful)
+    inputs = tokenizer(history_useful, return_tensors="pt")
+    inputs, history_useful, history = take_last_tokens(inputs, history_useful, history)
+    reply_ids = model.generate(**inputs)
+    response = tokenizer.batch_decode(reply_ids, skip_special_tokens=True)[0]
+    history_useful = add_note_to_history(response, history_useful)
+    list_history = history_useful[0].split('</s> <s>')
+    history.append((list_history[-2], list_history[-1]))
+    store_message(message, response) # Save to dataset
+    return history, history
+
+gr.Interface(
+    fn=chat,
+    theme="huggingface",
+    css=".footer {display:none !important}",
+    inputs=["text", "state"],
+    outputs=["chatbot", "state"],
+    title=title,
+    allow_flagging="never",
+    description=f"Gradio chatbot backed by memory in a dataset repository.",
+    article=f"The dataset repo is [{DATASET_REPO_URL}]({DATASET_REPO_URL})"
+    ).launch()

requirements.txt

@@ -0,0 +1,6 @@
+transformers
+torch
+gradio
+Werkzeug
+huggingface_hub
+Pillow