изменено: app.py

app.py

@@ -1,287 +1,83 @@
 import gradio as gr
-import os
 import time
+from utils import Bot
+from utils.functions import make_documents, make_descriptions
 
-from langchain.document_loaders import OnlinePDFLoader
-from langchain.text_splitter import CharacterTextSplitter
-from langchain.llms import OpenAI
-from langchain.embeddings import OpenAIEmbeddings
-from langchain.vectorstores import Chroma
-from langchain.chains import ConversationalRetrievalChain
-from langchain import PromptTemplate
-from transformers import Pix2StructForConditionalGeneration, Pix2StructProcessor
-import requests
-from PIL import Image
-import torch
-
-
-
-# _template = """Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question.
-# Chat History:
-# {chat_history}
-# Follow Up Input: {question}
-# Standalone question:"""
-
-# CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template(_template)
-
-# template = """
-# You are given the following extracted parts of a long document and a question. Provide a short structured answer.
-# If you don't know the answer, look on the web. Don't try to make up an answer.
-# Question: {question}
-# =========
-# {context}
-# =========
-# Answer in Markdown:"""
-
-torch.hub.download_url_to_file('https://raw.githubusercontent.com/vis-nlp/ChartQA/main/ChartQA%20Dataset/val/png/20294671002019.png', 'chart_example.png')
-torch.hub.download_url_to_file('https://raw.githubusercontent.com/vis-nlp/ChartQA/main/ChartQA%20Dataset/test/png/multi_col_1081.png', 'chart_example_2.png')
-torch.hub.download_url_to_file('https://raw.githubusercontent.com/vis-nlp/ChartQA/main/ChartQA%20Dataset/test/png/18143564004789.png', 'chart_example_3.png')
-torch.hub.download_url_to_file('https://sharkcoder.com/files/article/matplotlib-bar-plot.png', 'chart_example_4.png')
-
-
-model_name = "google/matcha-chartqa"
-model = Pix2StructForConditionalGeneration.from_pretrained(model_name)
-processor = Pix2StructProcessor.from_pretrained(model_name)
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-model.to(device)
-
-def filter_output(output):
-    return output.replace("<0x0A>", "")
-
-def chart_qa(image, question):
-    inputs = processor(images=image, text=question, return_tensors="pt").to(device)
-    predictions = model.generate(**inputs, max_new_tokens=512)
-    return filter_output(processor.decode(predictions[0], skip_special_tokens=True))
-
-def loading_pdf():
-    return "Loading..."
-
-
-def pdf_changes(pdf_doc, open_ai_key):
-    if open_ai_key is not None:
-        os.environ['OPENAI_API_KEY'] = open_ai_key
-        loader = OnlinePDFLoader(pdf_doc.name)
-        documents = loader.load()
-        text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
-        texts = text_splitter.split_documents(documents)
-        embeddings = OpenAIEmbeddings()
-        db = Chroma.from_documents(texts, embeddings)
-        retriever = db.as_retriever()
-        global qa
-        qa = ConversationalRetrievalChain.from_llm(
-            llm=OpenAI(temperature=0.5), 
-            retriever=retriever, 
-            return_source_documents=True)
-        return "Ready"
-    else:
-        return "You forgot OpenAI API key"
-
-def add_text(history, text):
-    history = history + [(text, None)]
-    return history, ""
-
-def bot(history):
-    response = infer(history[-1][0], history)
-    history[-1][1] = ""
-    
-    for character in response:     
-        history[-1][1] += character
-        time.sleep(0.05)
-        yield history
+def init_bot(file=None,title=None,pdf=None,key=None):
     
-
-def infer(question, history):  
-    res = []
-    for human, ai in history[:-1]:
-        pair = (human, ai)
-        res.append(pair)
+    if key is None:
+        return 'You must submit OpenAI key'
     
-    chat_history = res
-    #print(chat_history)
-    query = question
-    result = qa({"question": query, "chat_history": chat_history})
-    #print(result)
-    return result["answer"]
-
-css="""
-#col-container {max-width: 700px; margin-left: auto; margin-right: auto;}
-"""
-
-title = """
-<div style="text-align: center;">
-    <h1>YnP LangChain Test </h1>
-    <p style="text-align: center;">Please specify OpenAI Key before use</p>
-</div>
-"""
-
-
-# with gr.Blocks(css=css) as demo:
-#     with gr.Column(elem_id="col-container"):
-#         gr.HTML(title)
-        
-#         with gr.Column():
-#             openai_key = gr.Textbox(label="You OpenAI API key", type="password")
-#             pdf_doc = gr.File(label="Load a pdf", file_types=['.pdf'], type="file")
-#             with gr.Row():
-#                 langchain_status = gr.Textbox(label="Status", placeholder="", interactive=False)
-#                 load_pdf = gr.Button("Load pdf to langchain")
-        
-#         chatbot = gr.Chatbot([], elem_id="chatbot").style(height=350)
-#         question = gr.Textbox(label="Question", placeholder="Type your question and hit Enter ")
-#         submit_btn = gr.Button("Send Message")
-        
-#     load_pdf.click(loading_pdf, None, langchain_status, queue=False)    
-#     load_pdf.click(pdf_changes, inputs=[pdf_doc, openai_key], outputs=[langchain_status], queue=False)
-#     question.submit(add_text, [chatbot, question], [chatbot, question]).then(
-#         bot, chatbot, chatbot
-#     )
-#     submit_btn.click(add_text, [chatbot, question], [chatbot, question]).then(
-#         bot, chatbot, chatbot)
-
-# demo.launch()
-
-
-"""functions"""
-
-def load_file():
-    return "Loading..."
-
-def load_xlsx(name):
-    import pandas as pd
+    if pdf is None:
+        return 'You must submit pdf file'
     
-    xls_file = rf'{name}'
-    data = pd.read_excel(xls_file)  
-    return data
-
-def table_loader(table_file, open_ai_key):
-    import os
-    from langchain.llms import OpenAI
-    from langchain.agents import create_pandas_dataframe_agent    
-    from pandas import read_csv
+    if file is None:
+        return 'You must submit media file'
     
-    global agent
-    if open_ai_key is not None:
-        os.environ['OPENAI_API_KEY'] = open_ai_key
-    else:
-        return "Enter API"
+    if title is None:
+        return 'You must submit the description of the media'
     
-    if table_file.name.endswith('.xlsx') or table_file.name.endswith('.xls'):
-        data = load_xlsx(table_file.name)
-        agent = create_pandas_dataframe_agent(OpenAI(temperature=0), data)
-        return "Ready!"
-    elif table_file.name.endswith('.csv'):
-        data = read_csv(table_file.name)
-        agent = create_pandas_dataframe_agent(OpenAI(temperature=0), data)
-        return "Ready!"
-    else:
-        return "Wrong file format! Upload excel file or csv!"
+    file = file.name
+    print(file)
+    pdf = pdf.name
+    file_description = make_descriptions(file, title)
+    # print(file_description)
+    documents = make_documents(pdf)
     
-def run(query):
-    from langchain.callbacks import get_openai_callback
+    # print(documents[0])
+    global bot
     
-    with get_openai_callback() as cb:
-        response = (agent.run(query))
-        costs = (f"Total Cost (USD): ${cb.total_cost}")
-        output = f'{response} \n {costs}'
-        return output
+    bot = Bot(
+        openai_api_key=key,
+        file_descriptions=file_description,
+        text_documents=documents,
+        verbose=False
+    )
     
-def respond(message, chat_history):
-    import time
+    return 'Chat bot successfully initialized'
+
+def msg_bot(history):
+    message = history[-1][0]
     
-    bot_message = run(message)
-    chat_history.append((message, bot_message))
-    time.sleep(0.5)
-    return "", chat_history
+    bot_message = bot(message)['output']
+    history[-1][1] = ""
+    for character in bot_message:
+        history[-1][1] += character
+        time.sleep(0.05)
+        yield history
 
+def user(user_message, history):
+    return "", history + [[user_message, None]]
 
 with gr.Blocks() as demo:
-    with gr.Column(elem_id="col-container"):
-        gr.HTML(title)
-        key = gr.Textbox(
-                show_label=False,
-                placeholder="Your OpenAI key",
-                type = 'password',
-                ).style(container=False)
-
-    # PDF processing tab
-    with gr.Tab("PDFs"):
-        
-        with gr.Row(): 
-            
-            with gr.Column(scale=0.5):
-                langchain_status = gr.Textbox(label="Status", placeholder="", interactive=False)
-                load_pdf = gr.Button("Load pdf to langchain")
-                
-            with gr.Column(scale=0.5):
-                pdf_doc = gr.File(label="Load a pdf", file_types=['.pdf'], type="file")
-                
-                
-        with gr.Row():
-            
-            with gr.Column(scale=1): 
-                chatbot = gr.Chatbot([], elem_id="chatbot").style(height=350)
-                
-        with gr.Row():
-            
-            with gr.Column(scale=0.85):
-                question = gr.Textbox(
-                show_label=False,
-                placeholder="Enter text and press enter, or upload an image",
-                ).style(container=False)
-                
-            with gr.Column(scale=0.15, min_width=0):
-                clr_btn = gr.Button("Clear!")
-                
-    load_pdf.click(loading_pdf, None, langchain_status, queue=False)    
-    load_pdf.click(pdf_changes, inputs=[pdf_doc, key], outputs=[langchain_status], queue=True)
-    question.submit(add_text, [chatbot, question], [chatbot, question]).then(
-        bot, chatbot, chatbot
-    )
-                
-    # XLSX and CSV processing tab
-    with gr.Tab("Spreadsheets"):
-        with gr.Row(): 
-            
-            with gr.Column(scale=0.5):
-                status_sh = gr.Textbox(label="Status", placeholder="", interactive=False)
-                load_table = gr.Button("Load csv|xlsx to langchain")
-                
-            with gr.Column(scale=0.5):
-                raw_table = gr.File(label="Load a table file (xls or csv)", file_types=['.csv, xlsx, xls'], type="file")
-                
-                
-        with gr.Row():
+    
+    key = gr.Textbox(label='OpenAI key')
+    with gr.Tab("Chat bot initialization"):
             
-            with gr.Column(scale=1): 
-                chatbot_sh = gr.Chatbot([], elem_id="chatbot").style(height=350)
-                
-                
-        with gr.Row():
+        with gr.Row(variant='panel'):
+            with gr.Column():
+                with gr.Row():
+                    title = gr.Textbox(label='File short description')
+                with gr.Row():
+                    file = gr.File(label='CSV or image', file_types=['.csv', 'image'])
+
+            pdf = gr.File(label='pdf')
             
-            with gr.Column(scale=0.85):
-                question_sh = gr.Textbox(
-                show_label=False,
-                placeholder="Enter text and press enter, or upload an image",
-                ).style(container=False)
-                
-            with gr.Column(scale=0.15, min_width=0):
-                clr_btn = gr.Button("Clear!")
+        with gr.Row(variant='panel'):
+            init_button = gr.Button('submit')
+            init_output = gr.Textbox(label="Initialization status")
+            init_button.click(fn=init_bot,inputs=[file,title,pdf,key],outputs=init_output,api_name='init')
             
-    load_table.click(load_file, None, status_sh, queue=False)    
-    load_table.click(table_loader, inputs=[raw_table, key], outputs=[status_sh], queue=False)
-   
-    question_sh.submit(respond, [question_sh, chatbot_sh], [question_sh, chatbot_sh])
-    clr_btn.click(lambda: None, None, chatbot_sh, queue=False)
-    
+        chatbot = gr.Chatbot()
+        msg = gr.Textbox(label='Ask the bot')
+        clear = gr.Button('Clear')
+        msg.submit(user,[msg,chatbot],[msg,chatbot],queue=False).then(
+            msg_bot, chatbot, chatbot
+        )
+        clear.click(lambda: None, None, chatbot, queue=False)
+
+demo.queue()
+demo.launch()
+
 
-    with gr.Tab("Charts"):
-            image = gr.Image(type="pil", label="Chart")
-            question = gr.Textbox(label="Question")
-            load_chart = gr.Button("Load chart and question!")
-            answer = gr.Textbox(label="Model Output")
-            
-    load_chart.click(chart_qa, [image, question], answer)
 
-        
-demo.queue(concurrency_count=3)
-demo.launch()