tabs, xlsx analyzer

README.md

@@ -5,9 +5,9 @@ colorFrom: purple
 colorTo: pink
 sdk: gradio
 sdk_version: 3.27.0
-python_version: 3.10.9
 app_file: app.py
 pinned: false
 duplicated_from: fffiloni/langchain-chat-with-pdf-openai
 ---
+
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -9,11 +9,6 @@ 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.
@@ -33,32 +28,12 @@ import torch
 # =========
 # 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:
+    if openai_key is not None:
         os.environ['OPENAI_API_KEY'] = open_ai_key
         loader = OnlinePDFLoader(pdf_doc.name)
         documents = loader.load()
@@ -108,180 +83,34 @@ css="""
 """
 
 title = """
-<div style="text-align: center;">
+<div style="text-align: center;max-width: 700px;">
     <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
-    
-    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
-    
-    global agent
-    if open_ai_key is not None:
-        os.environ['OPENAI_API_KEY'] = open_ai_key
-    else:
-        return "Enter API"
-    
-    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!"
-    
-def run(query):
-    from langchain.callbacks import get_openai_callback
-    
-    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
-    
-def respond(message, chat_history):
-    import time
-    
-    bot_message = run(message)
-    chat_history.append((message, bot_message))
-    time.sleep(0.5)
-    return "", chat_history
-
-
-with gr.Blocks() as demo:
+with gr.Blocks(css=css) 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):
+        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")
-                
-            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!")
-                
+        
+        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, key], outputs=[langchain_status], queue=True)
+    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
     )
-                
-    # 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():
-            
-            with gr.Column(scale=1): 
-                chatbot_sh = gr.Chatbot([], elem_id="chatbot").style(height=350)
-                
-                
-        with gr.Row():
-            
-            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!")
-            
-    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)
-    
+    submit_btn.click(add_text, [chatbot, question], [chatbot, question]).then(
+        bot, chatbot, chatbot)
 
-    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()

requirements.txt

@@ -3,6 +3,4 @@ tiktoken
 chromadb
 langchain
 unstructured
-unstructured[local-inference]
-pandas
-tabulate
+unstructured[local-inference]