update app

app.py

@@ -1,9 +1,10 @@
 import streamlit as st
-from transformers import AutoTokenizer, AutoModelForCausalLM, set_seed
+from transformers import AutoTokenizer, AutoModelForCausalLM
 from transformers import pipeline
 import torch
 import json
 import pandas as pd
+import requests
 
 @st.cache(allow_output_mutation=True)
 def load_tokenizer(model_ckpt):
@@ -24,45 +25,20 @@ st.set_page_config(page_icon=':laptop:', layout="wide")
            
 
 st.sidebar.header("Models")
-models = ["CodeParrot", "OPT", "InCoder"]
-selected_models = st.sidebar.multiselect('Select code generation models to compare:', 
-                         models,
-                         default=["CodeParrot"])
+models = ["CodeParrot", "InCoder"]
+selected_models = st.sidebar.multiselect('Select code generation models to compare:', models, default=["CodeParrot"])
+
 st.sidebar.header("Tasks")
 tasks = [" ", "Model evaluation", "Pretraining datasets", "Model architecture", "Code generation"]
 selected_task = st.sidebar.selectbox("Select a task:", tasks)
 
 
-tokenizer1 = load_tokenizer("lvwerra/codeparrot")
-model1 = load_model("lvwerra/codeparrot")
-tokenizer2 = load_tokenizer("facebook/incoder-1B")
-model2 = load_model("facebook/incoder-1B")
-#tokenizer3 = load_tokenizer("facebook/opt-1.3b")
-#model3 = load_model("facebook/opt-1.3b")
-pipelines = {}
-for element in models:
-    if element == "CodeParrot":
-        pipelines[element] = pipeline("text-generation", model=model1, tokenizer=tokenizer1)
-    elif element == "InCoder":
-        tokenizer = load_tokenizer("facebook/incoder-1B")
-        model = load_model("facebook/incoder-1B")
-        pipelines[element] = pipeline("text-generation", model=model2, tokenizer=tokenizer2)
-    #else:
-    #    tokenizer = load_tokenizer("facebook/opt-1.3b")
-    #    model = load_model("facebook/opt-1.3b")
-    #    pipelines[element] = pipeline("text-generation", model=model3, tokenizer=tokenizer3)
-        
-examples = load_examples()
-example_names = [example["name"] for example in examples]
-name2id = dict([(name, i) for i, name in enumerate(example_names)])
-set_seed(42)
-gen_kwargs = {}
-
 if selected_task == " ":
     st.title("Code Generation Models comparison")
     with open("intro.txt", "r") as f:
         intro = f.read()
     st.markdown(intro)
+    
 elif selected_task == "Pretraining datasets":
     st.title("Pretraining datasets 📚")
     st.markdown("Preview of some code files from Github repositories")   
@@ -72,7 +48,8 @@ elif selected_task == "Pretraining datasets":
         with open(f"datasets/{model.lower()}.txt", "r") as f:
             text = f.read()
         st.markdown(f"### {model}:")
-        st.markdown(text)   
+        st.markdown(text)  
+        
 elif selected_task == "Model architecture":
     st.title("Model architecture 🔨")
     for model in selected_models:
@@ -80,30 +57,32 @@ elif selected_task == "Model architecture":
             text = f.read()
         st.markdown(f"## {model}:")
         st.markdown(text) 
+        
 elif selected_task == "Model evaluation":
     st.title("Code models evaluation 📊")
     with open("evaluation/intro.txt", "r") as f:
         intro = f.read()
     st.markdown(intro)
+    
 elif selected_task == "Code generation":
     st.title("Code generation 💻")
     st.sidebar.header("Examples")
+    examples = load_examples()
+    example_names = [example["name"] for example in examples]
+    name2id = dict([(name, i) for i, name in enumerate(example_names)])
     selected_example = st.sidebar.selectbox("Select one of the following examples:", example_names)
     example_text = examples[name2id[selected_example]]["value"]
     default_length = examples[name2id[selected_example]]["length"]
     st.sidebar.header("Generation settings")
-    gen_kwargs["do_sample"] = st.sidebar.radio("Decoding strategy:",  ["Greedy", "Sample"]) == "Sample"
-    gen_kwargs["max_new_tokens"] = st.sidebar.slider("Number of tokens to generate:", value=default_length, min_value=8, step=8, max_value=256)
-    if gen_kwargs["do_sample"]:
-        gen_kwargs["temperature"] = 0.2
-        gen_kwargs["top_k"] = 0
-        gen_kwargs["top_p"] = 0.95
+    temperature = st.sidebar.slider("Temperature:", value=0.2, min_value=0, step=0.1, max_value=2)
+    max_new_tokens = st.sidebar.slider("Number of tokens to generate:", value=default_length, min_value=8, step=8, max_value=256)
+    seed = st.sidebar.slider("Random seed:", value=42, min_value=0, step=1, max_value=1000)
     gen_prompt = st.text_area("Generate code with prompt:", value=example_text, height=220,).strip()
     if st.button("Generate code!"):
         with st.spinner("Generating code..."):
             for model in selected_models:
-                if model != "OPT":
-                    pipe = pipelines[model]
-                    generated_text = pipe(gen_prompt, **gen_kwargs)[0]['generated_text']
-                    st.markdown(f"{model}:")
-                    st.code(generated_text)
+                url = f'https://hf.space/embed/loubnabnl/{model.lower()}-subspace/+/api/predict/'
+                r = requests.post(url=url, json={"data": [gen_prompt, max_new_tokens, temperature, seed]})
+                generated_text = r.json()['data'][0]
+                st.markdown(f"{model}:")
+                st.code(generated_text)