Update app.py

app.py

@@ -1,13 +1,9 @@
 # app.py
 import streamlit as st
 from transformers import pipeline
-from googletrans import Translator
 from rouge_score import rouge_scorer
 import torch
 
-# -------------------------------
-# Page Config
-# -------------------------------
 st.set_page_config(page_title="Multilingual Summarization Dashboard", layout="wide")
 
 # -------------------------------
@@ -17,47 +13,74 @@ with open("style.css") as f:
     st.markdown(f"<style>{f.read()}</style>", unsafe_allow_html=True)
 
 # -------------------------------
-# Model Loading
+# Hugging Face API Token
+# -------------------------------
+st.sidebar.title("Hugging Face API Token")
+api_token = st.sidebar.text_input(
+    "Enter your Hugging Face API token:",
+    type="password",
+    help="Get your token from https://huggingface.co/settings/tokens"
+)
+
+if not api_token:
+    st.warning("Please enter your Hugging Face API token to enable model inference.")
+
+# -------------------------------
+# Model Initialization
 # -------------------------------
 @st.cache_resource(show_spinner=True)
-def load_models():
+def load_models(token):
     models = {}
     models['urT5-base'] = pipeline(
         "summarization",
         model="mbshr/urt5-base-finetuned",
-        device=0 if torch.cuda.is_available() else -1
+        device=0 if torch.cuda.is_available() else -1,
+        use_auth_token=token
     )
     models['mT5-small'] = pipeline(
         "summarization",
         model="google/mt5-small",
-        device=0 if torch.cuda.is_available() else -1
+        device=0 if torch.cuda.is_available() else -1,
+        use_auth_token=token
     )
     models['mT5-base'] = pipeline(
         "summarization",
         model="google/mt5-base",
-        device=0 if torch.cuda.is_available() else -1
+        device=0 if torch.cuda.is_available() else -1,
+        use_auth_token=token
+    )
+    # Translation models
+    models['en→ur'] = pipeline(
+        "translation",
+        model="Helsinki-NLP/opus-mt-en-ur",
+        device=0 if torch.cuda.is_available() else -1,
+        use_auth_token=token
+    )
+    models['ur→en'] = pipeline(
+        "translation",
+        model="Helsinki-NLP/opus-mt-ur-en",
+        device=0 if torch.cuda.is_available() else -1,
+        use_auth_token=token
     )
     return models
 
-models = load_models()
-translator = Translator()
-scorer = rouge_scorer.RougeScorer(['rouge1', 'rouge2', 'rougeL'], use_stemmer=True)
+models = load_models(api_token) if api_token else {}
 
 # -------------------------------
-# Sidebar
+# Sidebar Settings
 # -------------------------------
 st.sidebar.title("Settings")
-selected_model = st.sidebar.selectbox("Choose Summarization Model", list(models.keys()))
+selected_model = st.sidebar.selectbox("Choose Summarization Model", ["urT5-base", "mT5-small", "mT5-base"])
 max_length = st.sidebar.slider("Max summary length", 50, 500, 150)
 min_length = st.sidebar.slider("Min summary length", 10, 300, 40)
-target_lang = st.sidebar.selectbox("Translate summary to", ["None", "English", "Urdu", "French", "Spanish"])
+target_lang = st.sidebar.selectbox("Translate summary to", ["None", "English", "Urdu"])
 show_comparison = st.sidebar.checkbox("Compare models")
 show_rouge = st.sidebar.checkbox("Show ROUGE Score (requires reference)")
 
 # -------------------------------
 # Main Interface
 # -------------------------------
-st.title("🌐 Multilingual Summarization Dashboard")
+st.title("🌐 Multilingual Summarization Dashboard (API Version)")
 st.write("Enter text to summarize, optionally translate, compare models, and evaluate with ROUGE.")
 
 text = st.text_area("Enter text to summarize:", height=200)
@@ -69,11 +92,13 @@ if show_rouge:
 # Generate Summary
 # -------------------------------
 if st.button("Generate Summary"):
-    if not text.strip():
+    if not api_token:
+        st.error("Please provide Hugging Face API token.")
+    elif not text.strip():
         st.error("Please enter some text!")
     else:
-        # Handle long texts with chunking
-        chunk_size = 500  # characters
+        # Chunking
+        chunk_size = 500
         chunks = [text[i:i+chunk_size] for i in range(0, len(text), chunk_size)]
         full_summary = ""
         for chunk in chunks:
@@ -85,26 +110,29 @@ if st.button("Generate Summary"):
 
         # Translation
         if target_lang != "None":
-            lang_code = target_lang[:2].lower()
             try:
-                translation = translator.translate(full_summary, dest=lang_code).text
+                if target_lang == "English":
+                    translated = models['ur→en'](full_summary)[0]['translation_text']
+                else:  # Urdu
+                    translated = models['en→ur'](full_summary)[0]['translation_text']
                 st.subheader(f"Summary in {target_lang}:")
-                st.write(translation)
+                st.write(translated)
             except Exception as e:
                 st.warning(f"Translation failed: {str(e)}")
 
         # Model comparison
         if show_comparison:
             st.subheader("Comparison with other models:")
-            for model_name, model_pipeline in models.items():
+            for model_name in ["urT5-base", "mT5-small", "mT5-base"]:
                 if model_name != selected_model:
                     comp_summary = ""
                     for chunk in chunks:
-                        comp_summary += model_pipeline(chunk, max_length=max_length, min_length=min_length, do_sample=False)[0]['summary_text'] + " "
+                        comp_summary += models[model_name](chunk, max_length=max_length, min_length=min_length, do_sample=False)[0]['summary_text'] + " "
                     st.markdown(f"**{model_name} Summary:** {comp_summary}")
 
         # ROUGE Evaluation
         if show_rouge and reference_text.strip():
+            scorer = rouge_scorer.RougeScorer(['rouge1', 'rouge2', 'rougeL'], use_stemmer=True)
             scores = scorer.score(reference_text, full_summary)
             st.subheader("ROUGE Scores:")
             for k, v in scores.items():