Create app.py

app.py

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+import streamlit as st
+from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
+import time
+import requests
+import json
+
+repo_name = "BeardedMonster/SabiYarn-125M"
+
+tokenizer = AutoTokenizer.from_pretrained(repo_name, trust_remote_code=True)
+
+# Add sidebar with instructions
+st.sidebar.title("Instructions: How to use")
+st.sidebar.write("""
+1.These are the tags for languages: **<yor>, <ibo>, <hau>, <efi>, <urh>, <fuv>, <eng>, <pcm>**\n
+2. You can either input any string in the specified language:\n
+- how are you?, Abin mamaki ne aikin da\n
+Or you can prompt the model with questions or instructions using defined tags:\n
+- **Instruction following:**
+<prompt> who are you? <response>:, <prompt> speak yoruba <response>:\n
+- **sentiment classification:**
+<classify> I don't like apples <sentiment>\n
+- **Topic classification:**
+<classify> TEXT HERE <topic>\n
+- **Title Generation:**
+<title> TEXT HERE <headline>\n
+- **Diacritize tex:t**
+<diacritize> TEXT HERE <yor>, <diacritize> TEXT HERE <hau>\n
+- **Clean text:**
+<clean> TEXT HERE <hau>\n
+**Note: Model's performance vary with prompts due to model size and training data distribution.**\n
+3. Lastly, you can play with some of the generation parameters below to improve performance.
+""")
+
+# Define generation configuration
+max_length = st.sidebar.slider("Max Length", min_value=10, max_value=500, value=100)
+num_beams = st.sidebar.slider("Number of Beams", min_value=1, max_value=10, value=5)
+temperature = st.sidebar.slider("Temperature", min_value=0.1, max_value=2.0, value=0.9)
+top_k = st.sidebar.slider("Top-K", min_value=1, max_value=100, value=50)
+top_p = st.sidebar.slider("Top-P", min_value=0.1, max_value=1.0, value=0.95)
+repetition_penalty = st.sidebar.slider("Repetition Penalty", min_value=1.0, max_value=10.0, value=2.0)
+length_penalty = st.sidebar.slider("Length Penalty", min_value=0.1, max_value=10.0, value=1.7)
+# early_stopping = st.sidebar.selectbox("Early Stopping", [True, False], index=0)
+
+generation_config = {
+    "max_length": max_length,
+    "num_beams": num_beams,
+    "do_sample": True,
+    "temperature": temperature,
+    "top_k": top_k,
+    "top_p": top_p,
+    "repetition_penalty": repetition_penalty,
+    "length_penalty": length_penalty,
+    "early_stopping": True
+}
+
+# Streamlit app
+st.title("SabiYarn-125M: Generates text in multiple Nigerian languages.")
+
+st.write("**Supported Languages: English, Yoruba, Igbo, Hausa, Pidgin, Efik, Urhobo, Fulfulde, Fulah. \nResults might not be coherent for less represented languages (i.e Efik, \
+Urhobo, Fulfulde, Fulah).**")
+st.write("**Model is running on CPU RAM. So, token generation might be slower (streaming not enabled).**")
+st.write("**Avg Response time: 15 secs/ 50 tokens. Response time increase with input length**")
+st.write("-" * 50)
+
+def generate_from_api(user_input, generation_config):
+    url = " https://pauljeffrey--sabiyarn-fastapi-app.modal.run/predict"
+
+    payload = json.dumps({
+      "prompt": user_input,
+      "config": generation_config
+    })
+    headers = {
+      'Content-Type': 'application/json'
+    }
+    
+    response = requests.request("POST", url, headers=headers, data=payload)
+    return response.json()["generated_text"]
+
+# Sample texts
+sample_texts = {
+    "Tell me a story in pidgin": "<prompt> Tell me a story in pidgin <response>:",
+    "Translate 'how are you?' to Yoruba": "<prompt> Translate 'how are you?' to Yoruba <response>:",
+    "Classify the sentiment of 'I don't like apples'": "<classify> I don't like apples <sentiment>",
+    "Generate a title for 'This is a sample text'": "<title> This is a sample text <headline>"
+}
+
+# Task options
+task_options = {
+    "Instruction following": "<prompt> {} <response>:",
+    "Sentiment classification": "<classify> {} <sentiment>",
+    "Topic classification": "<classify> {} <topic>",
+    "Title Generation": "<title> {} <headline>",
+    "Diacritize text": "<diacritize> {} <yor>",
+    "Clean text": "<clean> {} <hau>"
+}
+
+# Dropdown for sample text
+sample_text = st.selectbox("Select a sample text to test the model:", list(sample_texts.keys()))
+
+# Dropdown for tasks
+task = st.selectbox("Select a task for the model:", list(task_options.keys()))
+
+# Text input
+user_input = st.text_area("Enter text below **(please, first read the instructions on how to use in the side bar)**: ", sample_texts[sample_text])
+
+if st.button("Generate"):
+    if user_input:
+        try:
+            st.write("**Generated Text Below:**")
+            wrapped_input = task_options[task].format(user_input)
+            generated_text = generate_from_api(wrapped_input, generation_config)
+            full_output = st.empty()
+           
+            start_time = time.time()
+            output = ""
+            for next_token in tokenizer.tokenize(generated_text):
+                output += tokenizer.convert_tokens_to_string([next_token])
+                full_output.text(output)
+                time.sleep(0.1)
+            end_time = time.time()
+            time_diff = end_time - start_time
+            st.write("Time taken: ", time_diff , "seconds.")
+        except Exception as e:
+            st.error(f"Error during text generation: {e}")
+    else:
+        st.write("Please enter some text to generate.")