Uploaded files

app.py

@@ -14,40 +14,49 @@ def read_pdf(uploaded_file):
         text += pdf_reader.pages[page_num].extract_text()
     return text
 
-
-
-st.title("PdfQuerier using LLAMA by Rahul Bhoyar")
-hf_token = st.text_input("Enter your Hugging Face token:")
-llm = HuggingFaceInferenceAPI(model_name="HuggingFaceH4/zephyr-7b-alpha", token=hf_token)
-st.markdown("Query your pdf file data with using this chatbot.")
-uploaded_file = st.file_uploader("Choose a PDF file", type=["pdf"])
-
-# Creation of Embedding model
-embed_model_uae = HuggingFaceEmbedding(model_name="WhereIsAI/UAE-Large-V1")
-service_context = ServiceContext.from_defaults(llm=llm, chunk_size=800, chunk_overlap=20, embed_model=embed_model_uae)
-
-if uploaded_file is not None:
-    file_contents = read_pdf(uploaded_file)
-    documents = Document(text=file_contents)
-    documents = [documents]
-    st.success("Documents loaded successfully!")
-
-# Indexing the documents
-progress_container = st.empty()
-progress_container.text("Creating VectorStoreIndex...")
-    # Code to create VectorStoreIndex
-index = VectorStoreIndex.from_documents(documents, service_context=service_context, show_progress=True)
-# Persist Storage Context
-index.storage_context.persist()
-st.success("VectorStoreIndex created successfully!")
-# Create Query Engine
-query = st.text_input("Ask a question:")
-query_engine = index.as_query_engine()
-
-if query:
-    # Run Query
-    progress_container.text("Fetching the response...")
-    response = query_engine.query(query)
-    st.markdown(f"**Response:** {response}")
-
-
+def querying(query_engine):
+    progress_container = st.empty()
+    query = st.text_input("Enter the Query for PDF:")
+    submit = st.button("Generate The response for the query")
+
+    if submit:
+         progress_container.text("Fetching the response...")
+         response = query_engine.query(query)
+         st.write(f"**Response:** {response}")
+        
+        
+        # docs = document_search.similarity_search(query_text)
+        # output = chain.run(input_documents=docs, question=query_text)
+        # st.write(output)
+            
+def main():
+    st.title("PdfQuerier using LLAMA by Rahul Bhoyar")
+    hf_token = st.text_input("Enter your Hugging Face token:")
+    llm = HuggingFaceInferenceAPI(model_name="HuggingFaceH4/zephyr-7b-alpha", token=hf_token)
+    uploaded_file = st.file_uploader("Choose a PDF file", type=["pdf"])
+
+    if uploaded_file is not None:
+        file_contents = read_pdf(uploaded_file)
+        documents = Document(text=file_contents)
+        documents = [documents]
+        st.success("Documents loaded successfully!")
+         
+        embed_model_uae = HuggingFaceEmbedding(model_name="WhereIsAI/UAE-Large-V1")
+        service_context = ServiceContext.from_defaults(llm=llm, chunk_size=800, chunk_overlap=20, embed_model=embed_model_uae)
+         
+         # Indexing the documents
+        progress_container = st.empty()
+        progress_container.text("Creating VectorStoreIndex...")
+        # Download embeddings from OpenAI
+        
+        index = VectorStoreIndex.from_documents(documents, service_context=service_context, show_progress=True)
+        index.storage_context.persist()
+        query_engine = index.as_query_engine()
+        st.success("VectorStoreIndex created successfully!")
+        
+        querying(query_engine)
+        
+                
+if __name__ == "__main__":
+    main()
+    

app_archive.py

@@ -0,0 +1,53 @@
+import streamlit as st
+from PyPDF2 import PdfReader
+from llama_index.llms import HuggingFaceInferenceAPI
+from llama_index import VectorStoreIndex
+from llama_index.embeddings import HuggingFaceEmbedding
+from llama_index import ServiceContext
+from llama_index.schema import Document
+
+
+def read_pdf(uploaded_file):
+    pdf_reader = PdfReader(uploaded_file)
+    text = ""
+    for page_num in range(len(pdf_reader.pages)):
+        text += pdf_reader.pages[page_num].extract_text()
+    return text
+
+
+
+st.title("PdfQuerier using LLAMA by Rahul Bhoyar")
+hf_token = st.text_input("Enter your Hugging Face token:")
+llm = HuggingFaceInferenceAPI(model_name="HuggingFaceH4/zephyr-7b-alpha", token=hf_token)
+st.markdown("Query your pdf file data with using this chatbot.")
+uploaded_file = st.file_uploader("Choose a PDF file", type=["pdf"])
+
+# Creation of Embedding model
+embed_model_uae = HuggingFaceEmbedding(model_name="WhereIsAI/UAE-Large-V1")
+service_context = ServiceContext.from_defaults(llm=llm, chunk_size=800, chunk_overlap=20, embed_model=embed_model_uae)
+
+if uploaded_file is not None:
+    file_contents = read_pdf(uploaded_file)
+    documents = Document(text=file_contents)
+    documents = [documents]
+    st.success("Documents loaded successfully!")
+
+# Indexing the documents
+progress_container = st.empty()
+progress_container.text("Creating VectorStoreIndex...")
+    # Code to create VectorStoreIndex
+index = VectorStoreIndex.from_documents(documents, service_context=service_context, show_progress=True)
+# Persist Storage Context
+index.storage_context.persist()
+st.success("VectorStoreIndex created successfully!")
+# Create Query Engine
+query = st.text_input("Ask a question:")
+query_engine = index.as_query_engine()
+
+if query:
+    # Run Query
+    progress_container.text("Fetching the response...")
+    response = query_engine.query(query)
+    st.markdown(f"**Response:** {response}")
+
+