Create app.py

app.py

@@ -0,0 +1,78 @@
+import os
+import asyncio
+from concurrent.futures import ThreadPoolExecutor
+import requests
+import gradio as gr
+
+MAX_NEW_TOKENS = 256
+TOKEN = os.environ.get("HF_TOKEN", None)
+URLS = [
+    "https://api-inference.huggingface.co/models/google/flan-ul2",
+    "https://api-inference.huggingface.co/models/google/flan-t5-xxl",
+]
+
+def fetch(session, text, api_url):
+    model = api_url.split("/")[-1]
+    response = session.post(api_url, json={"inputs": text, "parameters": {"max_new_tokens": MAX_NEW_TOKENS}})
+    if response.status_code != 200:
+        return model, None
+    return model, response.json()
+
+async def inference(text):
+    with ThreadPoolExecutor(max_workers=2) as executor:
+        with requests.Session() as session:
+            session.headers = {"Authorization": f"Bearer {TOKEN}"}
+            # Initialize the event loop
+            loop = asyncio.get_event_loop()
+            tasks = [
+                loop.run_in_executor(
+                    executor, fetch, *(session, text, url)  # Allows us to pass in multiple arguments to `fetch`
+                )
+                for url in URLS
+            ]
+
+            # Initializes the tasks to run and awaits their results
+            responses = [None, None]
+            for (model, response) in await asyncio.gather(*tasks):
+                if response is not None:
+                    if model == "flan-ul2":
+                        responses[0] = response[0]["generated_text"]
+                    elif model == "flan-t5-xxl":
+                        responses[1] = response[0]["generated_text"]
+    return responses
+
+def feedback(inputs, feedback, is_positive):
+    with open('promptlog.txt', 'a') as f:
+        f.write(f"Inputs: {inputs}\nFeedback: {feedback}\nIs positive: {is_positive}\n\n")
+
+def display_history():
+    try:
+        with open('promptlog.txt', 'r') as f:
+            history = f.read()
+    except FileNotFoundError:
+        history = "No history yet."
+    print(history)
+
+def app():
+    title = "Flan UL2 vs Flan T5 XXL"
+    description = "Compare with feedback: [Flan-T5-xxl](https://huggingface.co/google/flan-t5-xxl) and [Flan-UL2](https://huggingface.co/google/flan-ul2)."
+    inputs = gr.inputs.Textbox(lines=3, label="Input Prompt")
+    outputs = [gr.outputs.Textbox(lines=3, label="Flan T5-UL2"), gr.outputs.Textbox(lines=3, label="Flan T5-XXL")]
+    feedback_box = gr.inputs.CheckboxGroup(["Positive feedback", "Negative feedback"], label="Feedback")
+    feedback_text = gr.inputs.Textbox(label="Feedback Reason")
+    feedback_button = gr.inputs.Button(label="Submit Feedback")
+    display_history_button = gr.inputs.Button(label="Display Feedback History")
+
+    def predict_text(inputs):
+        return inference(inputs)
+
+    def handle_feedback(inputs, feedback, is_positive):
+        feedback(inputs, feedback, is_positive)
+        return "Thank you for your feedback!"
+
+    def handle_display_history():
+        display_history()
+
+    gr.Interface(fn=predict_text, inputs=inputs, outputs=outputs, title=title, description=description).launch()
+
+    feedback_ui = gr.Interface(fn=handle_feedback, inputs=[inputs, feedback_box, feedback_text, feedback_button], outputs=gr.outputs