Update app.py

app.py

@@ -1,161 +1,22 @@
-# from huggingface_hub import InferenceClient
-# import gradio as gr
-
-# client = InferenceClient(
-#     "mistralai/Mixtral-8x7B-Instruct-v0.1"
-# )
-
-
-# def format_prompt(message, history):
-#   prompt = "<s>"
-#   for user_prompt, bot_response in history:
-#     prompt += f"[INST] {user_prompt} [/INST]"
-#     prompt += f" {bot_response}</s> "
-#   prompt += f"[INST] {message} [/INST]"
-#   return prompt
-
-# def generate(
-#     prompt, history, system_prompt, temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0,
-# ):
-#     temperature = float(temperature)
-#     if temperature < 1e-2:
-#         temperature = 1e-2
-#     top_p = float(top_p)
-
-#     generate_kwargs = dict(
-#         temperature=temperature,
-#         max_new_tokens=max_new_tokens,
-#         top_p=top_p,
-#         repetition_penalty=repetition_penalty,
-#         do_sample=True,
-#         seed=42,
-#     )
-
-#     formatted_prompt = format_prompt(f"{system_prompt}, {prompt}", history)
-#     stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
-#     output = ""
-
-#     for response in stream:
-#         output += response.token.text
-#         yield output
-#     return output
-
-
-# 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)
-
-
-import os
-import streamlit as st
-import gradio as gr
-from PyPDF2 import PdfReader
-from langchain.text_splitter import CharacterTextSplitter
-from langchain.embeddings import HuggingFaceBgeEmbeddings
-from langchain.vectorstores import FAISS
-from langchain.chat_models import ChatOpenAI
-from langchain.memory import ConversationBufferMemory
-from langchain.chains import ConversationalRetrievalChain
 from huggingface_hub import InferenceClient
+import gradio as gr
 
-# Set the Hugging Face Hub API token
-hf_token = st.secrets['MixtralPDFChat']
-
-os.environ["HUGGINGFACEHUB_API_TOKEN"] = hf_token
-
-# Initialize the InferenceClient
-client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")
-
-def get_pdf_text(pdf_docs):
-    text = ""
-    for pdf in pdf_docs:
-        pdf_reader = PdfReader(pdf)
-        for page in pdf_reader.pages:
-            text += page.extract_text()
-    return text
-
-def get_text_chunks(text):
-    text_splitter = CharacterTextSplitter(
-        separator="\n", chunk_size=1500, chunk_overlap=300, length_function=len
-    )
-    chunks = text_splitter.split_text(text)
-    return chunks
+client = InferenceClient(
+    "mistralai/Mixtral-8x7B-Instruct-v0.1"
+)
 
-def get_vectorstore(text_chunks):
-    model = "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2"
-    encode_kwargs = {"normalize_embeddings": True}
-    embeddings = HuggingFaceBgeEmbeddings(
-        model_name=model, encode_kwargs=encode_kwargs, model_kwargs={"device": "cpu"}
-    )
-    vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
-    return vectorstore
 
 def format_prompt(message, history):
-    prompt = "<s>"
-    for user_prompt, bot_response in history:
-        prompt += f"[INST] {user_prompt} [/INST]"
-        prompt += f" {bot_response}</s> "
-    prompt += f"[INST] {message} [/INST]"
-    return prompt
-
-def generate(prompt, history, system_prompt, temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0):
+  prompt = "<s>"
+  for user_prompt, bot_response in history:
+    prompt += f"[INST] {user_prompt} [/INST]"
+    prompt += f" {bot_response}</s> "
+  prompt += f"[INST] {message} [/INST]"
+  return prompt
+
+def generate(
+    prompt, history, system_prompt, temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0,
+):
     temperature = float(temperature)
     if temperature < 1e-2:
         temperature = 1e-2
@@ -179,35 +40,64 @@ def generate(prompt, history, system_prompt, temperature=0.9, max_new_tokens=256
         yield output
     return output
 
-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()
+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)