Update app.py

app.py

@@ -104,6 +104,7 @@
 
 
 import os
+import streamlit as st
 import gradio as gr
 from PyPDF2 import PdfReader
 from langchain.text_splitter import CharacterTextSplitter
@@ -115,7 +116,9 @@ from langchain.chains import ConversationalRetrievalChain
 from huggingface_hub import InferenceClient
 
 # Set the Hugging Face Hub API token
-os.environ["HUGGINGFACEHUB_API_TOKEN"] = st.secrets['huggingface_token']
+hf_token = st.secrets['MixtralPDFChat']
+
+os.environ["HUGGINGFACEHUB_API_TOKEN"] = hf_token
 
 # Initialize the InferenceClient
 client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")
@@ -176,79 +179,35 @@ def generate(prompt, history, system_prompt, temperature=0.9, max_new_tokens=256
         yield output
     return output
 
-def main(pdf_docs):
-    # get pdf text
-    raw_text = get_pdf_text(pdf_docs)
-
-    # get the text chunks
-    text_chunks = get_text_chunks(raw_text)
-
-    # create vector store
-    vectorstore = get_vectorstore(text_chunks)
-
-    # create conversation chain
-    conversation_chain = get_conversation_chain(vectorstore)
-
-    additional_inputs=[
-        gr.Textbox(
-            label="System Prompt",
-            max_lines=1,
-            interactive=True,
-        ),
-        gr.Slider(
-            label="Temperature",
-            value=0.9,
-            minimum=0.0,
-            maximum=1.0,
-            step=0.05,
-            interactive=True,
-            info="Higher values produce more diverse outputs",
-        ),
-        gr.Slider(
-            label="Max new tokens",
-            value=256,
-            minimum=0,
-            maximum=1048,
-            step=64,
-            interactive=True,
-            info="The maximum numbers of new tokens",
-        ),
-        gr.Slider(
-            label="Top-p (nucleus sampling)",
-            value=0.90,
-            minimum=0.0,
-            maximum=1,
-            step=0.05,
-            interactive=True,
-            info="Higher values sample more low-probability tokens",
-        ),
-        gr.Slider(
-            label="Repetition penalty",
-            value=1.2,
-            minimum=1.0,
-            maximum=2.0,
-            step=0.05,
-            interactive=True,
-            info="Penalize repeated tokens",
-        )
-    ]
-
-    examples=[["I'm planning a vacation to Japan. Can you suggest a one-week itinerary including must-visit places and local cuisines to try?", None, None, None, None, None, ],
-              ["Can you write a short story about a time-traveling detective who solves historical mysteries?", None, None, None, None, None,],
-              ["I'm trying to learn French. Can you provide some common phrases that would be useful for a beginner, along with their pronunciations?", None, None, None, None, None,],
-              ["I have chicken, rice, and bell peppers in my kitchen. Can you suggest an easy recipe I can make with these ingredients?", None, None, None, None, None,],
-              ["Can you explain how the QuickSort algorithm works and provide a Python implementation?", None, None, None, None, None,],
-              ["What are some unique features of Rust that make it stand out compared to other systems programming languages like C++?", None, None, None, None, None,],
-             ]
-
-    gr.ChatInterface(
-        fn=generate,
-        chatbot=gr.Chatbot(show_label=False, show_share_button=False, show_copy_button=True, likeable=True, layout="panel"),
-        additional_inputs=additional_inputs,
-        title="Mixtral 46.7B",
-        examples=examples,
-        concurrency_limit=20,
-    ).launch(show_api= True)
+def main():
+    st.title("PDF Chatbot")
+
+    # Upload PDF files
+    pdf_docs = st.file_uploader("Upload PDF files", type=['pdf'], accept_multiple_files=True)
+
+    if pdf_docs:
+        # get pdf text
+        raw_text = get_pdf_text(pdf_docs)
+
+        # get the text chunks
+        text_chunks = get_text_chunks(raw_text)
+
+        # create vector store
+        vectorstore = get_vectorstore(text_chunks)
+
+        # create conversation chain
+        conversation_chain = get_conversation_chain(vectorstore)
+
+        def chat_interface(user_input):
+            response = next(generate(user_input, [], "System Prompt"))
+            return response
+
+        st.subheader("Chat Interface")
+        user_input = st.text_input("Ask a question:")
+        if user_input:
+            response = chat_interface(user_input)
+            st.write("Bot's Response:")
+            st.write(response)
 
 if __name__ == "__main__":
-    main([])
+    main()