Update app.py

app.py

@@ -1,64 +1,138 @@
-import gradio as gr
+import os
 from huggingface_hub import InferenceClient
+import gradio as gr
+import nltk
+import torch
+from transformers import DistilBertTokenizer, DistilBertModel
+from duckduckgo_search import ddg
+from langchain.chains import RetrievalQA
+from langchain.document_loaders import UnstructuredFileLoader
+from langchain.embeddings import HuggingFaceBgeEmbeddings
+from langchain.vectorstores import Chroma
+from transformers import DistilBertConfig, DistilBertModel
+
+# Initialize tokenizer and model for embedding
+tokenizer = DistilBertTokenizer.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")
+embedding_model_name = "distilbert/distilbert-base-uncased-finetuned-sst-2-english"
+DEVICE = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
+
+# Load Qwen 2 for text generation
+qwen_text_gen = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+# Function to search the web
+def search_web(query):
+    results = ddg(query)
+    web_content = ''
+    if results:
+        for result in results:
+            web_content += result['body']
+    return web_content
 
+# Function to initialize knowledge vector store
+def init_knowledge_vector_store(file):
+    if file is None:
+        return
+    filepath = file.name
+    distilbert_embedding = HuggingFaceBgeEmbeddings(model_name=embedding_model_name)
+    loader = UnstructuredFileLoader(filepath, mode="elements")
+    docs = loader.load()
+    Chroma.from_documents(docs, distilbert_embedding, persist_directory="./vector_store")
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
+# Function to get knowledge vector store
+def get_knowledge_vector_store():
+    distilbert_embedding = HuggingFaceBgeEmbeddings(model_name=embedding_model_name)
+    vector_store = Chroma(embedding_function=distilbert_embedding, persist_directory="./vector_store")
+    return vector_store
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+# Function to get knowledge-based answer
+def get_knowledge_based_answer(query, qwen_text_gen, vector_store, VECTOR_SEARCH_TOP_K, web_content):
+    if web_content:
+        prompt_template = f"""Answer the user's question based on the following known information.
+                            Known web search content: {web_content} """ + """
+                            Known Content:
+                            {context}
+                            question:
+                            {question}"""
+    else:
+        prompt_template = """Answer the user's question based on the known information.
+            Known Content:
+            {context}
+            question:
+            {question}"""
+    prompt = PromptTemplate(template=prompt_template, input_variables=["context", "question"])
+    
+    knowledge_chain = RetrievalQA.from_llm(
+        llm=qwen_text_gen, 
+        retriever=vector_store.as_retriever(search_kwargs={"k": VECTOR_SEARCH_TOP_K}), 
+        prompt=prompt
+    )
+    
+    knowledge_chain.combine_documents_chain.document_prompt = PromptTemplate(
+        input_variables=["page_content"], 
+        template="{page_content}"
+    )
+    
+    knowledge_chain.return_source_documents = True
+    
+    result = knowledge_chain.invoke({"query": query})
+    
+    return result['result']
 
-    messages.append({"role": "user", "content": message})
+# Function to clear session
+def clear_session():
+    return '', None
 
-    response = ""
+# Function to predict
+def predict(input, qwen_text_gen, VECTOR_SEARCH_TOP_K, use_web, key=None, history=None):
+    if history == None:
+        history = []
+    vector_store = get_knowledge_vector_store()
+    if use_web == 'True':
+        web_content = search_web(query=input)
+        if web_content is None:
+            web_content = ""
+    else:
+        web_content = ''
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
+    resp = get_knowledge_based_answer(
+        query=input,
+        qwen_text_gen=qwen_text_gen,
+        vector_store=vector_store,
+        VECTOR_SEARCH_TOP_K=VECTOR_SEARCH_TOP_K,
+        web_content=web_content,
+    )
+    history.append((input, resp))
+    return '', history, history
 
-        response += token
-        yield response
+# Gradio interface setup
+block = gr.Blocks()
+with block as demo:
+    gr.Markdown("<h1><center>Chat History </center></h1>")
+    with gr.Row():
+        with gr.Column(scale=1):
+            file = gr.File(label='Please upload txt, md, docx type files', file_types=['.txt', '.md', '.docx'])
+            get_vs = gr.Button("Generate Knowledge Base")
+            get_vs.click(init_knowledge_vector_store, inputs=[file])
+            
+            use_web = gr.Radio(["True", "False"], label="Web Search", value="False")
+            
+            VECTOR_SEARCH_TOP_K = gr.Slider(1, 10, value=5, step=1, label="vector search top k", interactive=True)
 
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-textbox_css = {"border": "2px solid blue"}  # Customize the border style here
-demo = gr.ChatInterface(
-    respond, title="MediPro",
-        additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="MediPro",
-        ),
-    ],
-)
+        with gr.Column(scale=4):
+            chatbot = gr.Chatbot(label='Ming History Knowledge Question and Answer Assistant', height=600)
+            message = gr.Textbox(label='Please enter your question')
+            state = gr.State()
 
+            with gr.Row():
+                clear_history = gr.Button("Clear history conversation")
+                send = gr.Button("Send")
+                send.click(predict,
+                           inputs=[message, qwen_text_gen, VECTOR_SEARCH_TOP_K, use_web, state],
+                           outputs=[message, chatbot, state])
+                clear_history.click(fn=clear_session, inputs=[], outputs=[chatbot, state], queue=False)
+                
+                message.submit(predict,
+                               inputs=[message, qwen_text_gen, VECTOR_SEARCH_TOP_K, use_web, state],
+                               outputs=[message, chatbot, state])
 
-if __name__ == "__main__":
-    demo.launch()
+demo.queue().launch(share=False)