Update app.py

app.py

@@ -1,83 +1,87 @@
-import streamlit as st
-import PyPDF2
-from langchain.llms import HuggingFaceHub
-import pptx
-import os
-from langchain.vectorstores.cassandra import Cassandra
-from langchain.indexes.vectorstore import VectorStoreIndexWrapper
-from langchain.embeddings import OpenAIEmbeddings
-import cassio
-from langchain.text_splitter import CharacterTextSplitter
-
-
-
-
-# Secure API keys (replace with environment variables in deployment)
-ASTRA_DB_APPLICATION_TOKEN = os.getenv("ASTRA_DB_APPLICATION_TOKEN")
-ASTRA_DB_ID = os.getenv("ASTRA_DB_ID")
-HUGGINGFACE_API_KEY = os.getenv("HUGGINGFACE_API_KEY")
-OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
-
-
-# Initialize Astra DB connection
-cassio.init(token=ASTRA_DB_APPLICATION_TOKEN, database_id=ASTRA_DB_ID)
-
-# Initialize LLM & Embeddings
-hf_llm = HuggingFaceHub(repo_id="google/flan-t5-large", model_kwargs={"temperature": 0, "max_length": 64})
-embedding =OpenAIEmbeddings(openai_api_key=OPENAI_API_KEY)
-
-# Initialize vector store
-astra_vector_store = Cassandra(embedding=embedding, table_name="qa_mini_demo")
-
-def extract_text_from_pdf(uploaded_file):
-    """Extract text from a PDF file."""
-    text = ""
-    pdf_reader = PyPDF2.PdfReader(uploaded_file)
-    for page in pdf_reader.pages:
-        page_text = page.extract_text()
-        if page_text:  # Avoid NoneType error
-            text += page_text + "\n"
-    return text
-
-def extract_text_from_ppt(uploaded_file):
-    """Extract text from a PowerPoint file."""
-    text = ""
-    presentation = pptx.Presentation(uploaded_file)
-    for slide in presentation.slides:
-        for shape in slide.shapes:
-            if hasattr(shape, "text"):
-                text += shape.text + "\n"
-    return text
-
-def main():
-    st.title("Chat with Documents")
-
-    uploaded_file = st.file_uploader("Upload a PDF or PPT file", type=["pdf", "pptx"])
-    extract_button = st.button("Extract Text")
-
-    extracted_text = ""
-    if extract_button and uploaded_file is not None:
-        if uploaded_file.name.endswith(".pdf"):
-            extracted_text = extract_text_from_pdf(uploaded_file)
-        elif uploaded_file.name.endswith(".pptx"):
-            extracted_text = extract_text_from_ppt(uploaded_file)
-
-        if extracted_text:
-            text_splitter = CharacterTextSplitter(separator="\n", chunk_size=800, chunk_overlap=200, length_function=len)
-            texts = text_splitter.split_text(extracted_text)
-            astra_vector_store.add_texts(texts)
-
-    # Ensure the vector store index is initialized properly
-    astra_vector_index = VectorStoreIndexWrapper(vectorstore=astra_vector_store)
-
-    query = st.text_input("Enter your query")
-    submit_query = st.button("Submit Query")
-    if submit_query:
-
-   
-        value = astra_vector_index.query(query, llm=hf_llm)
-
-        st.write(f"Response: {value}")
-
-if __name__ == "__main__":
-    main()
+import streamlit as st
+import PyPDF2
+from langchain.llms import HuggingFaceHub
+import pptx
+import os
+from langchain.vectorstores.cassandra import Cassandra
+from langchain.indexes.vectorstore import VectorStoreIndexWrapper
+from langchain.embeddings import OpenAIEmbeddings
+import cassio
+from langchain.text_splitter import CharacterTextSplitter
+from huggingface_hub import login
+
+
+
+
+
+
+
+# Secure API keys (replace with environment variables in deployment)
+ASTRA_DB_APPLICATION_TOKEN = os.getenv("ASTRA_DB_APPLICATION_TOKEN")
+ASTRA_DB_ID = os.getenv("ASTRA_DB_ID")
+HUGGINGFACE_API_KEY = os.getenv("HUGGINGFACE_API_KEY")
+OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
+login(token=HUGGINGFACE_API_KEY)
+
+# Initialize Astra DB connection
+cassio.init(token=ASTRA_DB_APPLICATION_TOKEN, database_id=ASTRA_DB_ID)
+
+# Initialize LLM & Embeddings
+hf_llm = HuggingFaceHub(repo_id="google/flan-t5-large", model_kwargs={"temperature": 0, "max_length": 64})
+embedding =OpenAIEmbeddings(openai_api_key=OPENAI_API_KEY)
+
+# Initialize vector store
+astra_vector_store = Cassandra(embedding=embedding, table_name="qa_mini_demo")
+
+def extract_text_from_pdf(uploaded_file):
+    """Extract text from a PDF file."""
+    text = ""
+    pdf_reader = PyPDF2.PdfReader(uploaded_file)
+    for page in pdf_reader.pages:
+        page_text = page.extract_text()
+        if page_text:  # Avoid NoneType error
+            text += page_text + "\n"
+    return text
+
+def extract_text_from_ppt(uploaded_file):
+    """Extract text from a PowerPoint file."""
+    text = ""
+    presentation = pptx.Presentation(uploaded_file)
+    for slide in presentation.slides:
+        for shape in slide.shapes:
+            if hasattr(shape, "text"):
+                text += shape.text + "\n"
+    return text
+
+def main():
+    st.title("Chat with Documents")
+
+    uploaded_file = st.file_uploader("Upload a PDF or PPT file", type=["pdf", "pptx"])
+    extract_button = st.button("Extract Text")
+
+    extracted_text = ""
+    if extract_button and uploaded_file is not None:
+        if uploaded_file.name.endswith(".pdf"):
+            extracted_text = extract_text_from_pdf(uploaded_file)
+        elif uploaded_file.name.endswith(".pptx"):
+            extracted_text = extract_text_from_ppt(uploaded_file)
+
+        if extracted_text:
+            text_splitter = CharacterTextSplitter(separator="\n", chunk_size=800, chunk_overlap=200, length_function=len)
+            texts = text_splitter.split_text(extracted_text)
+            astra_vector_store.add_texts(texts)
+
+    # Ensure the vector store index is initialized properly
+    astra_vector_index = VectorStoreIndexWrapper(vectorstore=astra_vector_store)
+
+    query = st.text_input("Enter your query")
+    submit_query = st.button("Submit Query")
+    if submit_query:
+
+   
+        value = astra_vector_index.query(query, llm=hf_llm)
+
+        st.write(f"Response: {value}")
+
+if __name__ == "__main__":
+    main()