Create app.py

app.py

@@ -0,0 +1,80 @@
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import random
+from datasets import load_dataset
+
+title = "Ask Rick a Question"
+description = """
+The bot was trained to answer questions based on Rick and Morty dialogues. Ask Rick anything!
+<img src="https://huggingface.co/spaces/course-demos/Rick_and_Morty_QA/resolve/main/rick.png" width=200px>
+"""
+
+article = "Check out [the original Rick and Morty Bot](https://huggingface.co/spaces/kingabzpro/Rick_and_Morty_Bot) that this demo is based off of."
+
+
+tokenizer = AutoTokenizer.from_pretrained("./gemma-2b-sciq-checkpoint")
+model = AutoModelForCausalLM.from_pretrained("./gemma-2b-sciq-checkpoint")
+
+dataset = load_dataset("allenai/sciq")
+random_test_samples = dataset["test"].select(random.sample(range(0, len(dataset["test"])), 10))
+
+examples = []
+for row in random_test_samples:
+    examples.append([row['support'].replace('\n', ' ')])
+    examples.append([row['support'].replace('\n', ' '), row['correct_answer'].replace('\n', ' ')])
+
+
+def predict(context, answer):
+    formatted = f"{context.replace('\n', ' ')}\n"
+    
+    if answer is not None:
+        formatted = f"{context.replace('\n', ' ')}\n{answer.replace('\n', ' ')}\n"
+
+        
+    inputs = tokenizer(formatted, return_tensors="pt")
+    outputs = model.generate(**inputs, max_new_tokens=100)
+    decoded_outputs = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    split_outputs = decoded_outputs.split("\n")
+    
+    if len(split_outputs) == 6:
+        return {
+            "context": split_outputs[0],
+            "answer": split_outputs[1],
+            "question": split_outputs[2],
+            "distractor1": split_outputs[3],
+            "distractor2": split_outputs[4],
+            "distractor3": split_outputs[5],
+        }
+        
+    return None
+    
+
+support_gr = gr.TextArea(
+    label="Context",
+    info="Make sure you use proper punctuation.",
+    value="Bananas are yellow and curved."
+)
+
+answer_gr = gr.TextArea(
+    label="Answer optional",
+    info="Make sure you use proper punctuation.",
+    value="yellow"
+)
+
+button = gr.Button("Generate", elem_id="send-btn", visible=True)
+
+output_gr = gr.TextArea(
+    label="Output",
+    info="Make sure you use proper punctuation.",
+    value=""
+)
+
+gr.Interface(
+    fn=predict,
+    inputs=[support_gr, answer_gr],
+    outputs=[output_gr],
+    title=title,
+    description=description,
+    article=article,
+    examples=examples,
+).launch()