Update app.py

app.py

@@ -1,70 +1,81 @@
-import os
-import logging
-import streamlit as st
-from langchain.embeddings import HuggingFaceEmbeddings
-from langchain.vectorstores import Chroma
-from langchain.chains import RetrievalQA
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
-
-# Configure logging
-logging.basicConfig(level=logging.DEBUG)
-
-def load_vector_store():
-    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
-    vector_store = Chroma(persist_directory="./chroma_db", embedding_function=embeddings)
-    return vector_store
-
-def load_llm():
-    checkpoint = "LaMini-T5-738M"
-    tokenizer = AutoTokenizer.from_pretrained(checkpoint)
-    model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint)
-    pipe = pipeline(
-        'text2text-generation',
-        model=model,
-        tokenizer=tokenizer,
-        max_length=256,
-        do_sample=True,
-        temperature=0.3,
-        top_p=0.95
-    )
-    return pipe
-
-def process_answer(question):
-    try:
-        vector_store = load_vector_store()
-        llm = load_llm()
-        qa = RetrievalQA.from_chain_type(
-            llm=llm,
-            chain_type="stuff",
-            retriever=vector_store.as_retriever(),
-            return_source_documents=True
-        )
-        result = qa.invoke(question)
-        answer = result['result']
-        return answer, result
-    except Exception as e:
-        logging.error(f"An error occurred while processing the answer: {e}")
-        st.error(f"An error occurred while processing the answer: {e}")
-        return "An error occurred while processing your request.", {}
-
-def main():
-    st.title("Search Your PDF 📚📝")
-    with st.expander("About the App"):
-        st.markdown(
-            """
-            This is a Generative AI powered Question and Answering app that responds to questions about your PDF File.
-            """
-        )
-    question = st.text_area("Enter your Question")
-    if st.button("Ask"):
-        st.info("Your Question: " + question)
-        st.info("Your Answer")
-        try:
-            answer, metadata = process_answer(question)
-            st.write(answer)
-            st.write(metadata)
-        except Exception as e:
-            st.error(f"An unexpected error occurred: {e}")
-
-if __name__ == '__main__':
-    main()
+import os
+import logging
+import streamlit as st
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.vectorstores import Chroma
+from langchain.chains import RetrievalQA
+from langchain_huggingface import HuggingFacePipeline
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
+
+# Configure logging
+logging.basicConfig(level=logging.DEBUG)
+
+def load_vector_store():
+    # Ensure the directory exists
+    persist_directory = "./chroma_db"
+    if not os.path.exists(persist_directory):
+        logging.error(f"The directory '{persist_directory}' does not exist. Please run the ingestion script.")
+        st.error(f"The directory '{persist_directory}' does not exist. Please run the ingestion script.")
+        return None
+
+    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+    vector_store = Chroma(persist_directory=persist_directory, embedding_function=embeddings)
+    return vector_store
+
+def load_llm():
+    checkpoint = "LaMini-T5-738M"
+    tokenizer = AutoTokenizer.from_pretrained(checkpoint)
+    model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint)
+    pipe = pipeline(
+        'text2text-generation',
+        model=model,
+        tokenizer=tokenizer,
+        max_length=256,
+        do_sample=True,
+        temperature=0.3,
+        top_p=0.95
+    )
+    return HuggingFacePipeline(pipeline=pipe)
+
+def process_answer(question):
+    try:
+        vector_store = load_vector_store()
+        if vector_store is None:
+            return "Vector store not found. Please run the ingestion script.", {}
+
+        llm = load_llm()
+        qa = RetrievalQA.from_chain_type(
+            llm=llm,
+            chain_type="stuff",
+            retriever=vector_store.as_retriever(),
+            return_source_documents=True
+        )
+        result = qa.invoke(question)
+        answer = result['result']
+        return answer, result
+    except Exception as e:
+        logging.error(f"An error occurred while processing the answer: {e}")
+        st.error(f"An error occurred while processing the answer: {e}")
+        return "An error occurred while processing your request.", {}
+
+def main():
+    st.title("Search Your PDF 📚📝")
+    with st.expander("About the App"):
+        st.markdown(
+            """
+            This is a Generative AI powered Question and Answering app that responds to questions about your PDF File.
+            """
+        )
+    question = st.text_area("Enter your Question")
+    if st.button("Ask"):
+        st.info("Your Question: " + question)
+        st.info("Your Answer")
+        try:
+            answer, metadata = process_answer(question)
+            st.write(answer)
+            st.write(metadata)
+        except Exception as e:
+            st.error(f"An unexpected error occurred: {e}")
+
+if __name__ == '__main__':
+    main()