Re-added home page

app.py

@@ -3,6 +3,7 @@ import streamlit as st
 from transformers import pipeline
 
 import apps.summarization
+import apps.home
 
 st.set_page_config(
         page_title="Turna",
@@ -10,9 +11,8 @@ st.set_page_config(
         layout='wide'
     )
 
-API_URL = "https://api-inference.huggingface.co/models/boun-tabi-LMG/TURNA"
-
 PAGES = {
+    "Turna": apps.home
     "Text Summarization": apps.summarization
 }
 
@@ -24,65 +24,6 @@ page = PAGES[selection]
 # with st.spinner(f"Loading {selection} ..."):
 ast.shared.components.write_page(page)
 
-st.markdown(
-        """
-        <h1 style="text-align:left;">TURNA</h1>
-        """,
-        unsafe_allow_html=True,
-    )
-
-st.write("#")
-
-col = st.columns(2)
-
-col[0].image("images/turna-logo.png", width=100)
-
-st.markdown(
-        """
-
-        <h3 style="text-align:right;">TURNA is a Turkish encoder-decoder language model.</h3>
-        
-        <p style="text-align:right;"><p>
-        <p style="text-align:right;">Use the generation paramters on the sidebar to adjust generation quality.</p>
-        <p style="text-align:right;"><p>
-        """,
-        unsafe_allow_html=True,
-    )
-
-#st.title('Turkish Language Generation')
-#st.write('...with Turna')
-input_text = st.text_area(label='Enter a text: ', height=100, 
-    value="Türkiye'nin başkeni neresidir?")
-if st.button("Generate"):
-    with st.spinner('Generating...'):
-        output = query(input_text)
-        st.success(output)
-
-def query(payload):
-    #{"inputs": payload, ""}
-	while True:
-		response = requests.post(API_URL, json=payload)
-		if 'error' not in response.json():
-			output = response.json()[0]["generated_text"]
-			return output
-		else:
-			time.sleep(15)
-			print('Sending request again', flush=True)
-
-def pipe():
-    pipe = pipeline("text2text-generation", model="boun-tabi-LMG/TURNA", tokenizer="boun-tabi-LMG/TURNA", temperature=0.7, repetition_penalty=0.5, top_p=0.9)
-
-"""PAGES = {
-    "Turkish Language Generation": pages.turna,
-}
-
-st.sidebar.title("Navigation")
-selection = st.sidebar.radio("Pages", list(PAGES.keys()))
-
-page = PAGES[selection]
-# with st.spinner(f"Loading {selection} ..."):
-ast.shared.components.write_page(page)"""
-
 st.sidebar.header("Info")
 
 st.sidebar.write(

apps/home.py

@@ -0,0 +1,57 @@
+import requests
+import streamlit as st
+import time 
+from transformers import pipeline
+import os
+
+st.set_page_config(page_title="TURNA")
+
+API_URL = "https://api-inference.huggingface.co/models/boun-tabi-LMG/TURNA"
+
+st.markdown(
+        """
+        <h1 style="text-align:left;">TURNA</h1>
+        """,
+        unsafe_allow_html=True,
+    )
+
+st.write("#")
+
+col = st.columns(2)
+
+col[0].image("images/turna-logo.png", width=100)
+
+st.markdown(
+        """
+
+        <h3 style="text-align:right;">TURNA is a Turkish encoder-decoder language model.</h3>
+        
+        <p style="text-align:right;"><p>
+        <p style="text-align:right;">Use the generation paramters on the sidebar to adjust generation quality.</p>
+        <p style="text-align:right;"><p>
+        """,
+        unsafe_allow_html=True,
+    )
+
+#st.title('Turkish Language Generation')
+#st.write('...with Turna')
+input_text = st.text_area(label='Enter a text: ', height=100, 
+    value="Türkiye'nin başkeni neresidir?")
+if st.button("Generate"):
+    with st.spinner('Generating...'):
+        output = query(input_text)
+        st.success(output)
+
+def query(payload):
+    #{"inputs": payload, ""}
+	while True:
+		response = requests.post(API_URL, json=payload)
+		if 'error' not in response.json():
+			output = response.json()[0]["generated_text"]
+			return output
+		else:
+			time.sleep(15)
+			print('Sending request again', flush=True)
+
+def pipe():
+    pipe = pipeline("text2text-generation", model="boun-tabi-LMG/TURNA", tokenizer="boun-tabi-LMG/TURNA", temperature=0.7, repetition_penalty=0.5, top_p=0.9)

apps/summarization.py

@@ -6,7 +6,6 @@ import os
 
 st.set_page_config(page_title="Text Summarization", page_icon="📈")
 
-API_URL = "https://api-inference.huggingface.co/models/boun-tabi-LMG/turna_summarization_mlsum"
 HF_AUTH_TOKEN = os.getenv('HF_AUTH_TOKEN')
 headers = {"Authorization": f"Bearer {HF_AUTH_TOKEN}"}
 
@@ -18,18 +17,71 @@ def write():
 		"""Here, you can summarize your text using the fine-tuned TURNA summarization models. """
 	)
 
+	# Sidebar
+
+    # Taken from https://huggingface.co/spaces/flax-community/spanish-gpt2/blob/main/app.py
+    st.sidebar.subheader("Configurable parameters")
+
+    model_name = st.sidebar.selectbox(
+        "Model Selector",
+        options=[
+            "turna_summarization_mlsum",
+            "turna_summarization_tr_news",
+        ],
+        index=0,
+    )
+    max_new_tokens = st.sidebar.number_input(
+        "Maximum length",
+        min_value=0,
+        max_value=128,
+        value=128,
+        help="The maximum length of the sequence to be generated.",
+    )
+    length_penalty = st.sidebar.number_input(
+        "Length penalty",
+        value=2.0,
+        help=" length_penalty > 0.0 promotes longer sequences, while length_penalty < 0.0 encourages shorter sequences. ",
+    )
+    """do_sample = st.sidebar.selectbox(
+        "Sampling?",
+        (True, False),
+        help="Whether or not to use sampling; use greedy decoding otherwise.",
+    )
+    num_beams = st.sidebar.number_input(
+        "Number of beams",
+        min_value=1,
+        max_value=10,
+        value=3,
+        help="The number of beams to use for beam search.",
+    )
+    repetition_penalty = st.sidebar.number_input(
+        "Repetition Penalty",
+        min_value=0.0,
+        value=3.0,
+        step=0.1,
+        help="The parameter for repetition penalty. 1.0 means no penalty",
+    )"""
+    no_repeat_ngram_size = st.sidebar.number_input(
+        "No Repeat N-Gram Size",
+        min_value=0,
+        value=3,
+        help="If set to int > 0, all ngrams of that size can only occur once.",
+    )
+
 	input_text = st.text_area(label='Enter a text: ', height=200, 
 			value="Kalp krizi geçirenlerin yaklaşık üçte birinin kısa bir süre önce grip atlattığı düşünülüyor. Peki grip virüsü ne yapıyor da kalp krizine yol açıyor? Karpuz şöyle açıkladı: Grip virüsü kanın yapışkanlığını veya pıhtılaşmasını artırıyor.")
+	url = ("https://api-inference.huggingface.co/models/boun-tabi-LMG/" + model_name.lower())
+	params = {"length_penalty": length_penalty, "no_repeat_ngram_size": no_repeat_ngram_size, "max_new_tokens": max_new_tokens }
 	if st.button("Generate"):
 		with st.spinner('Generating...'):
-			output = query(input_text)
+			output = query(input_text, url, params)
 			st.success(output)
 
 
-def query(payload):
-	data = {"inputs": payload, "parameters": {"length_penalty": 2.0, "no_repeat_ngram_size": 3, "max_length":128}}
+def query(text, url, params):
+	data = {"inputs": payload, "parameters": params}
 	while True:
-		response = requests.post(API_URL, headers=headers, json=data)
+		response = requests.post(url, headers=headers, json=data)
 		if 'error' not in response.json():
 			output = response.json()[0]["generated_text"]
 			return output