init

app.py

@@ -0,0 +1,62 @@
+import streamlit as st
+from transformers import (
+    AutoModelForCausalLM,
+    AutoModelForSeq2SeqLM,
+    AutoTokenizer,
+    GenerationConfig,
+)
+
+@st.cache_resource
+def load_tokenizer_model(name_or_path: str, model_type: str, model_auth_token: str):
+    model_auth_token = None if model_auth_token == "" else model_auth_token
+    tokenizer = AutoTokenizer.from_pretrained(*name_or_path.split(","), use_auth_token=model_auth_token)
+    if model_type == "seq2seq":
+        model = AutoModelForSeq2SeqLM.from_pretrained(*name_or_path.split(","), use_auth_token=model_auth_token)
+    elif model_type == "causal":
+        model = AutoModelForCausalLM.from_pretrained(*name_or_path.split(","), use_auth_token=model_auth_token)
+    else:
+        raise ValueError("model_type must be one of 'seq2seq' or 'causal'")
+
+    return tokenizer, model
+
+def main():
+    st.title("Huggingface Transformers Demo")
+    with st.form("model_form"):
+        model_type = st.selectbox("Select Model Type", ["seq2seq", "causal"])
+        model_name_or_path = st.text_input("Model Name or Path")
+        model_auth_token = st.text_input("Model Auth Token")
+        input_text = st.text_area("Input Text")
+
+        col1, col2, col3 = st.columns(3)
+        user_gen_config = {}
+        with col1:
+            user_gen_config["min_length"] = st.number_input("Min Length", value=10, min_value=1, max_value=1000, step=1)
+            user_gen_config["max_length"] = st.number_input("Max Length", value=50, min_value=1, max_value=1000, step=1)
+            user_gen_config["top_k"] = st.number_input("Top K", value=50, min_value=1, max_value=100, step=1)
+
+        with col2:
+            user_gen_config["num_beams"] = st.number_input("Num Beams", value=1, min_value=1, max_value=100, step=1)
+            user_gen_config["top_p"] = st.number_input("Top P", value=1.0, min_value=0.0, max_value=100.0, step=0.1)
+            user_gen_config["repetition_penalty"] = st.number_input("Repetition Penalty", value=1.0, min_value=0.0,
+                                                                    max_value=100.0, step=0.1)
+
+        with col3:
+            user_gen_config["temperature"] = st.number_input("Temperature", value=1.0, min_value=0.0, max_value=100.0, step=0.1)
+            user_gen_config["do_sample"] = st.checkbox("Do Sample", value=False)
+            user_gen_config["early_stopping"] = st.checkbox("Early Stopping", value=True)
+
+        submitted = st.form_submit_button("Submit")
+
+        if submitted:
+            tokenizer, model = load_tokenizer_model(model_name_or_path, model_type, model_auth_token)
+            gen_config = GenerationConfig.from_model_config(model.config)
+            for k,v in user_gen_config.items():
+                setattr(gen_config, k, v)
+            input_ids = tokenizer.encode(input_text, return_tensors="pt")
+            output_ids = model.generate(input_ids, generation_config=gen_config)
+            output_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+            st.write(output_text)
+
+
+if __name__ == "__main__":
+    main()