New interface & xlsx analyzer (#3)

- On branch pr/3 (c0fd588b06bba9fdbf04e7c1a360122b8a23caf4)

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

@@ -33,7 +33,7 @@ def loading_pdf():
 
 
 def pdf_changes(pdf_doc, open_ai_key):
-    if openai_key is not None:
+    if open_ai_key is not None:
         os.environ['OPENAI_API_KEY'] = open_ai_key
         loader = OnlinePDFLoader(pdf_doc.name)
         documents = loader.load()
@@ -83,34 +83,177 @@ css="""
 """
 
 title = """
-<div style="text-align: center;max-width: 700px;">
+<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.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.Column(elem_id="col-container"):
         gr.HTML(title)
+        openai_key = gr.Textbox(
+                show_label=False,
+                placeholder="Your OpenAI key",
+                type = 'password',
+                ).style(container=False)
+
+    # PDF processing tab
+    with gr.Tab("PDFs"):
         
-        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():
+        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")
-        
-        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")
-        
+                
+            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, openai_key], outputs=[langchain_status], queue=False)
+    load_pdf.click(pdf_changes, inputs=[pdf_doc, openai_key], outputs=[langchain_status], queue=True)
     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)
-
+                
+    # 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, openai_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)
+    
+                
+    with gr.Tab("Charts"):
+        gr.Text('Soon!')
+        
+        
+        
+demo.queue(concurrency_count=3)
 demo.launch()

requirements.txt

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