Update src/app.py

src/app.py

@@ -1,8 +1,11 @@
+# app.py (Full Corrected Code for Hugging Face Spaces)
+
 import streamlit as st
 import httpx
 from bs4 import BeautifulSoup
 import random
 from transformers import pipeline
+import os  # Import 'os' to read environment variables
 
 # --- Page Configuration ---
 st.set_page_config(page_title="VirasaaT", layout="centered")
@@ -10,29 +13,36 @@ st.set_page_config(page_title="VirasaaT", layout="centered")
 # --- Caching & Model Loading ---
 
 @st.cache_resource
-def load_summarizer(token): # CHANGED: Function now accepts the token
+def load_summarizer(token: str):
     """
-    Loads the Hugging Face summarization model.
-    This is cached as a resource to be loaded only once.
+    Loads the Hugging Face summarization model using a token.
+    This is cached as a resource to be loaded only once per session.
     """
     print("Loading AI summarization model...")
     if not token:
-        # This error will now appear on app startup if the secret is missing.
-        st.error("Hugging Face API token is not configured. Please set the HUGGINGFACE_TOKEN secret in your deployment settings.", icon="🔒")
+        # This error is critical for deployment. It tells the user the secret is missing.
+        st.error(
+            "Hugging Face API token not found. Please set the 'HUGGINGFACE_TOKEN' secret in your Space settings.",
+            icon="🔒"
+        )
         st.stop()
         
-    # CHANGED: 'use_auth_token' is deprecated, use 'token' instead.
+    # Use 'token' instead of the deprecated 'use_auth_token'.
     summarizer = pipeline("summarization", model="facebook/bart-large-cnn", token=token)
     print("Model loaded successfully.")
     return summarizer
 
-# MOVED: Load the model once when the app starts.
-# This is the correct way to handle heavy resources.
-HUGGINGFACE_TOKEN = st.secrets.get("HUGGINGFACE_TOKEN")
+# --- Secret and Model Initialization (Corrected for Hugging Face Spaces) ---
+
+# 1. Read the secret from environment variables provided by Hugging Face Spaces.
+HUGGINGFACE_TOKEN = os.getenv("HUGGINGFACE_TOKEN")
+
+# 2. Load the model once using the token.
+#    This happens on app startup. The app will stop here if the token is not found.
 summarizer = load_summarizer(HUGGINGFACE_TOKEN)
 
 
-@st.cache_data(ttl=3600)  # Cache API data for 1 hour
+@st.cache_data(ttl=3600)  # Cache Wikipedia API data for 1 hour
 def get_recommendations(state: str):
     """Fetches cultural topics from Wikipedia's category system."""
     print(f"Fetching recommendations for {state}...")
@@ -53,9 +63,11 @@ def get_recommendations(state: str):
         return []
 
 @st.cache_data(ttl=3600)
-# CHANGED: The function now accepts the summarizer object as an argument.
 def get_wiki_summary_and_image(_summarizer, query: str):
-    """Fetches, summarizes, and extracts an image for a Wikipedia article."""
+    """
+    Fetches, summarizes, and extracts an image for a Wikipedia article.
+    This function now ACCEPTS the summarizer object instead of loading it.
+    """
     print(f"Fetching and processing article for '{query}'...")
     params = {"action": "parse", "page": query, "format": "json", "prop": "text|images", "redirects": True}
     try:
@@ -64,7 +76,7 @@ def get_wiki_summary_and_image(_summarizer, query: str):
             res.raise_for_status()
             data = res.json()
             
-        if "error" in data: 
+        if "error" in data:
             print(f"Wikipedia API error for query '{query}': {data['error']}")
             return None
 
@@ -73,18 +85,13 @@ def get_wiki_summary_and_image(_summarizer, query: str):
         html_content = parse_data["text"]["*"]
         soup = BeautifulSoup(html_content, "html.parser")
         
-        # Find image more robustly (preferring one from an infobox)
-        infobox_img = soup.select_one(".infobox .image img")
-        if infobox_img:
-             img_tag = infobox_img
-        else:
-             img_tag = soup.find("img") # Fallback to first image
-             
+        # Find image more robustly
+        img_tag = soup.select_one(".infobox .image img") or soup.find("img")
         image_url = f"https:{img_tag['src']}" if img_tag and 'src' in img_tag.attrs else None
         
         full_text = " ".join([p.get_text() for p in soup.find_all("p") if p.get_text()])
         
-        if not full_text.strip(): 
+        if not full_text.strip():
             print(f"No text content found for '{query}'")
             return None
         
@@ -107,6 +114,7 @@ def get_wiki_summary_and_image(_summarizer, query: str):
 # --- Streamlit User Interface ---
 st.title("🇮🇳 VirasaaT – AI-Powered Culture Explorer")
 st.markdown("Discover the cultural richness of India with dynamic recommendations and AI-generated summaries.")
+
 states_list = ["Select a state", "Andhra Pradesh", "Arunachal Pradesh", "Assam", "Bihar", "Chhattisgarh", "Goa", "Gujarat", "Haryana", "Himachal Pradesh", "Jharkhand", "Karnataka", "Kerala", "Madhya Pradesh", "Maharashtra", "Manipur", "Meghalaya", "Mizoram", "Nagaland", "Odisha", "Punjab", "Rajasthan", "Sikkim", "Tamil Nadu", "Telangana", "Tripura", "Uttar Pradesh", "Uttarakhand", "West Bengal"]
 
 # Initialize session state for the search query
@@ -119,27 +127,25 @@ if selected_state != "Select a state":
     recommendations = get_recommendations(selected_state)
     if recommendations:
         display_recommendations = ["Select a topic"] + recommendations
-        selected_topic = st.selectbox("Step 2: Select a Recommended Topic", display_recommendations, key="topic_select")
+        selected_topic = st.selectbox("Step 2: Select a Recommended Topic", display_recommendations)
         if selected_topic != "Select a topic":
             st.session_state.query = selected_topic
     else:
         st.warning(f"Could not find dynamic recommendations for {selected_state}. Please search manually below.")
 
-manual_query = st.text_input("Or Enter Any Cultural Topic Manually", placeholder="e.g., Diwali, Yoga, Taj Mahal", key="manual_input")
+manual_query = st.text_input("Or Enter Any Cultural Topic Manually", placeholder="e.g., Diwali, Yoga, Taj Mahal")
 
 if manual_query:
     st.session_state.query = manual_query
 
 if st.button("✨ Explore Culture", type="primary"):
-    # Use the query from session state, which is set by either the dropdown or the text box
     query_to_explore = st.session_state.query
     
     if not query_to_explore.strip():
         st.warning("Please select a state and a topic, or enter a topic manually.")
     else:
         with st.spinner(f"AI is exploring '{query_to_explore}'..."):
-            # CHANGED: Pass the globally loaded summarizer into the function.
-            # Note the leading underscore in _summarizer to avoid name clashes with the global variable.
+            # Pass the globally loaded summarizer object into the function.
             article_data = get_wiki_summary_and_image(summarizer, query_to_explore)
             
         if article_data:
@@ -150,4 +156,4 @@ if st.button("✨ Explore Culture", type="primary"):
             st.write(article_data["summary"])
             st.markdown(f"**[🔗 Read Full Article on Wikipedia]({article_data['url']})**")
         else:
-            st.error(f"Could not retrieve or summarize the article for '{query_to_explore}'. It might be a protected page, a redirect, or not exist. Please try another topic.")
+            st.error(f"Could not retrieve or summarize the article for '{query_to_explore}'. Please try another topic.")