add create_embeddings functionality

app.py

@@ -138,6 +138,19 @@ for i in st.session_state["chat_history"]:
 # pinecone.init(api_key=PINECONE_API_KEY, environment=PINECONE_ENV)
 # st.text(pinecone)
 
+def create_embeddings():
+    # Get the uploaded file
+    uploaded_file = st.session_state["uploaded_file"]
+
+    # Read the contents of the file
+    file_contents = uploaded_file.read()
+
+    st.write("created_embeddings")
+
+    # Display the contents of the file
+    st.write(file_contents)
+
+
 def print_out(pages):
     for i in range(len(pages)):
         text = pages[i].extract_text().strip()
@@ -153,6 +166,17 @@ def combine_text(pages):
     mbsize = round(len(bytesize) / p, 2)
     st.write(f"There are {len(concatenates_text)} characters in the pdf with {mbsize}MB size")
 
+# def promt_engineer(text):
+#     promt_template = """
+#     write a concise summary of the following text delimited by triple backquotes.
+#     return your response in bullet points which convers the key points of the text.
+
+#     ```{text}```
+
+#     BULLET POINT SUMMARY:
+#     """
+
+
 with st.sidebar:
     st.markdown("""
     ***Follow this steps***
@@ -161,7 +185,7 @@ with st.sidebar:
     - Takes couple of mins after upload the pdf
     - Now Chat with model to get the summarized info or Generative reponse
     """)
-    uploaded_files = st.file_uploader('Choose your .pdf file', type="pdf", accept_multiple_files=True)
+    uploaded_files = st.file_uploader('Choose your .pdf file', type="pdf", accept_multiple_files=True, key="uploaded_file", on_change=create_embeddings)
     for uploaded_file in uploaded_files:
         # To read file as bytes:
         # bytes_data = uploaded_file.getvalue()
@@ -182,3 +206,4 @@ with st.sidebar:
         pages = reader.pages
         print_out(pages)
         combine_text(pages)
+        # promt_engineer(text)