Updated file

app.py

@@ -15,40 +15,39 @@ def read_pdf(uploaded_file):
     return text
 
 
-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)
-    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
-            response = query_engine.query(query)
-            progress_container.text("Fetching the response...")
-            st.markdown(f"**Response:** {response}")
-
-
-if __name__ == "__main__":
-    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)
+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)
+
+
+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}")
+
+