Update app.py

app.py

@@ -1,50 +1,50 @@
-import streamlit as st
-import os
-from dotenv import load_dotenv
-from llama_index.readers.web import SimpleWebPageReader
-from llama_index.core import VectorStoreIndex
-from llama_index.embeddings.gemini import GeminiEmbedding
-import google.generativeai as genai
-from llama_index.llms.gemini import Gemini
-from llama_index.core.node_parser import SentenceSplitter
-from llama_index.core import Settings
-
-st.title("LLM-Powered QA System")
-load_dotenv()
-api_key = os.getenv('GOOGLE_API_KEY')
-model = Gemini(model_name="models/gemini-pro")
-
-def validate_api_key():
-    if not api_key:
-        st.error("GOOGLE_API_KEY environment variable not found!")
-        st.stop()
-    genai.configure(api_key=api_key)
-
-validate_api_key()
-url = st.text_input("Enter Webpage URL to Process:")
-question = st.text_input("Enter your question from the webpage:")
-
-if st.button("Process Webpage"):
-    if url:
-        try:
-            st.info("Fetching webpage content...")
-            reader = SimpleWebPageReader(html_to_text=True)
-            documents = reader.load_data(urls=[url])
-            embed_model = GeminiEmbedding(model_name="models/embedding-001")
-            Settings.llm = model
-            Settings.embed_model = embed_model
-            Settings.node_parser = SentenceSplitter(chunk_size=512, chunk_overlap=20)
-            Settings.num_output = 512
-            Settings.context_window = 3900
-            index = VectorStoreIndex.from_documents(documents,settings = Settings)
-            query_engine = index.as_query_engine()
-            response = query_engine.query(question)
-            st.write("Response:" + response.response)
-        except Exception as e:
-            st.error(f"Error occurred: {str(e)}")
-
-
-
-            
-
-
+import streamlit as st
+import os
+from dotenv import load_dotenv
+from llama_index.readers.web import SimpleWebPageReader
+from llama_index.core import VectorStoreIndex
+from llama_index.embeddings.gemini import GeminiEmbedding
+import google.generativeai as genai
+from llama_index.llms.gemini import Gemini
+from llama_index.core.node_parser import SentenceSplitter
+from llama_index.core import Settings
+
+st.title("LLM-Powered QA System")
+load_dotenv()
+api_key = os.getenv('GOOGLE_API_KEY')
+model = Gemini(model_name="models/gemini-1.5-flash")
+
+def validate_api_key():
+    if not api_key:
+        st.error("GOOGLE_API_KEY environment variable not found!")
+        st.stop()
+    genai.configure(api_key=api_key)
+
+validate_api_key()
+url = st.text_input("Enter Webpage URL to Process:")
+question = st.text_input("Enter your question from the webpage:")
+
+if st.button("Process Webpage"):
+    if url:
+        try:
+            st.info("Fetching webpage content...")
+            reader = SimpleWebPageReader(html_to_text=True)
+            documents = reader.load_data(urls=[url])
+            embed_model = GeminiEmbedding(model_name="models/embedding-001")
+            Settings.llm = model
+            Settings.embed_model = embed_model
+            Settings.node_parser = SentenceSplitter(chunk_size=512, chunk_overlap=20)
+            Settings.num_output = 512
+            Settings.context_window = 3900
+            index = VectorStoreIndex.from_documents(documents,settings = Settings)
+            query_engine = index.as_query_engine()
+            response = query_engine.query(question)
+            st.write("Response:" + response.response)
+        except Exception as e:
+            st.error(f"Error occurred: {str(e)}")
+
+
+
+            
+
+