Create app.py

app.py

@@ -0,0 +1,156 @@
+import os
+import gradio as gr
+from langchain_huggingface import HuggingFaceEndpoint,HuggingFaceEmbeddings,ChatHuggingFace
+from langchain_core.load import dumpd, dumps, load, loads
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.callbacks import StreamingStdOutCallbackHandler
+
+from langchain_chroma import Chroma
+from langchain_core.documents import Document  
+from langchain_text_splitters import CharacterTextSplitter
+from pypdf import PdfReader
+import random
+
+token=""
+#repo_id = "mistralai/Mistral-7B-Instruct-v0.3"
+repo_id = "mistralai/Mixtral-8x7B-Instruct-v0.1"
+emb = "sentence-transformers/all-mpnet-base-v2"
+hf = HuggingFaceEmbeddings(model_name=emb)
+db = Chroma(persist_directory="./chroma_langchain_db")
+db.persist()
+# Load the document, split it into chunks, embed each chunk and load it into the vector store.
+#raw_documents = TextLoader('state_of_the_union.txt').load()
+def embed_fn(inp):
+    print("Try Embeddings")
+    print(inp)
+    print("End Embeddings")
+    #for eaa in inp:
+    text_splitter = CharacterTextSplitter(chunk_size=200, chunk_overlap=10)
+    #documents = text_splitter.split_documents([eaa])
+    documents = text_splitter.split_text(inp)
+    print("documents")
+    print(documents)
+    print("end documents")
+    out_emb= hf.embed_documents(documents)
+    #chain = history[:-1]
+    string_representation = dumps(out_emb, pretty=True)
+    print(string_representation)
+    #db = Chroma(collection_name="test1", embedding_function=HuggingFaceEmbeddings())
+    db.from_texts(documents,HuggingFaceEmbeddings(model_name=emb))
+    #from_documents(documents, HuggingFaceEmbeddings)
+    print("DB")
+    print(db)
+    print("end DB")
+    #return db
+def proc_doc(doc_in):
+    for doc in doc_in:
+        if doc.endswith(".txt"):
+            yield [["",f"Loading Document: {doc}"]]
+            outp = read_txt(doc)
+            embed_fn(outp)
+            yield [["","Loaded"]]
+        elif doc.endswith(".pdf"):
+            yield [["",f"Loading Document: {doc}"]]
+            outp = read_pdf(doc)
+            embed_fn(outp)
+            yield [["","Loaded"]]
+
+
+def read_txt(txt_path):
+    text=""
+    with open(txt_path,"r") as f:
+        text = f.read()
+    f.close()
+    return text
+
+def read_pdf(pdf_path):
+    text=""
+    reader = PdfReader(f'{pdf_path}')
+    number_of_pages = len(reader.pages)
+    for i in range(number_of_pages):
+        page = reader.pages[i]
+        text = f'{text}\n{page.extract_text()}'
+    return text
+def run_llm(input_text,history):
+    MAX_TOKENS=20000
+    qur= hf.embed_query(input_text)
+    docs = db.similarity_search_by_vector(qur, k=3)
+    
+    '''if len(docs) >2:
+
+    doc_list = str(docs).split(" ")
+    if len(doc_list) > MAX_TOKENS:
+        doc_cnt = int(len(doc_list) / MAX_TOKENS)
+        print(doc_cnt)
+        for ea in doc_cnt:'''
+
+
+    print(docs)
+
+    callbacks = [StreamingStdOutCallbackHandler()]  
+    llm = HuggingFaceEndpoint(  
+    endpoint_url=repo_id,  
+    max_new_tokens=2056,  
+    seed=random.randint(1,99999999999),
+    top_k=10,  
+    top_p=0.95,  
+    typical_p=0.95,  
+    temperature=0.01,  
+    repetition_penalty=1.03,  
+    #callbacks=callbacks,  
+    streaming=True,  
+    huggingfacehub_api_token=token,
+    )  
+
+
+    '''llm=HuggingFaceEndpoint(
+        endpoint_url=repo_id,
+        streaming=True,
+        max_new_tokens=2400,
+        huggingfacehub_api_token=token)'''
+    print(input_text)
+    print(history)
+    out=""
+    #prompt = ChatPromptTemplate.from_messages(
+    sys_prompt = f"Use this data to help answer users questions: {str(docs)}"
+    user_prompt = f"{input_text}"
+    prompt=[
+        {"role": "system", "content": f"[INST] Use this data to help answer users questions: {str(docs)} [/INST]"},
+        {"role": "user", "content": f"[INST]{input_text}[/INST]"},
+    ]
+    #chat = ChatHuggingFace(llm=llm, verbose=True)
+    messages = [  
+    ("system", f"[INST] Use this data to help answer users questions: {str(docs)} [/INST]"),  
+    ("user", f"[INST]{input_text}[/INST]"),  
+    ]  
+    
+    #yield(llm.invoke(prompt))
+    
+    t=llm.invoke(prompt)
+    for chunk in t:
+        out+=chunk
+        yield out
+      
+    
+css="""
+#component-0 {
+    height:400px;
+}
+"""
+
+with gr.Blocks(css=css) as app:
+    data=gr.State()
+    with gr.Column():
+        #input_text = gr.Textbox(label="You: ")
+        chat = gr.ChatInterface(
+            fn=run_llm,
+            type="tuples",
+            concurrency_limit=20,
+            
+        )
+    with gr.Row():
+        msg=gr.HTML()
+        file_in=gr.Files(file_count="multiple")
+    file_in.change(proc_doc, file_in, msg)
+        #btn = gr.Button("Generate")
+app.queue().launch()