Upload folder using huggingface_hub

.gitignore

@@ -0,0 +1 @@
+.env

README.md

@@ -1,12 +1,6 @@
 ---
-title: WEF Hackathon Group2
-emoji: 📉
-colorFrom: pink
-colorTo: yellow
-sdk: gradio
-sdk_version: 5.13.0
+title: WEF_Hackathon_Group2
 app_file: app.py
-pinned: false
+sdk: gradio
+sdk_version: 4.37.2
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,164 @@
+import gradio as gr
+import os
+import pandas as pd
+from openai import OpenAI
+import logging
+
+# Set up logging
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
+    handlers=[
+        logging.FileHandler('app.log'),
+        logging.StreamHandler()
+    ]
+)
+
+logger = logging.getLogger(__name__)
+
+
+def upload_file(xsl_file):
+    if xsl_file is None:
+        return "Please upload a file first"
+    try:
+        df = pd.read_excel(xsl_file.name)
+        return df
+    except Exception as e:
+        return f"Error reading file: {str(e)}"
+       
+def process_query(xsl_file, query):
+    # Process the file and generate responses
+    logger.info("Starting query processing")
+    api_key = os.getenv('OPENAI_API_KEY')
+    client = OpenAI()
+    model = "gpt-4o"
+    responses = {}
+
+    # Convert DataFrame to JSON
+    if xsl_file is not None:
+        logger.info(f"Reading Excel file: {xsl_file.name}")
+        try:
+            df = pd.read_excel(xsl_file.name)
+            json_data = df.to_json(orient='records')
+            responses['data'] = json_data
+            logger.info("Successfully converted Excel to JSON")
+        except Exception as e:
+            logger.error(f"Error converting Excel to JSON: {str(e)}")
+            raise
+    
+    try:
+        logger.info("Sending request to OpenAI API")
+        response = client.chat.completions.create(
+            model=model,
+            messages=[
+                {"role": "system", "content": os.getenv('system_prompt')},
+                {"role": "user", "content": f"Examples: {os.getenv('context_prompt')}\n\n Data: {json_data}\n\n Query: {query} \n\n INSTRUCTIONS: You must absolutely return the sql between <SQL></SQL> and the XML between <XML></XML>. If the user query is incorrect, add the disclaimer between the SQL/XML tags as well. \n\n Output:"}
+            ]
+        )
+            
+        responses['query'] = query
+        responses['response'] = response.choices[0].message.content
+        logger.info(f"Raw response content: {response.choices[0].message.content}")
+        
+        # Add validation before splitting
+        if '<SQL>' not in response.choices[0].message.content or '<XML>' not in response.choices[0].message.content:
+            logger.error("Response missing SQL or XML tags")
+            raise ValueError("Response format invalid - missing SQL or XML tags")
+            
+        try:
+            responses['SQL'] = response.choices[0].message.content.split('<SQL>')[1].split('</SQL>')[0]
+            logger.info("Successfully extracted SQL query")
+        except IndexError as e:
+            logger.error(f"Error extracting SQL query: {str(e)}")
+            responses['SQL'] = "Error extracting SQL query"
+            
+        try:
+            responses['XML'] = response.choices[0].message.content.split('<XML>')[1].split('</XML>')[0]
+            logger.info("Successfully extracted XML query") 
+        except IndexError as e:
+            logger.error(f"Error extracting XML query: {str(e)}")
+            responses['XML'] = "Error extracting XML query"
+            
+        logger.info("Successfully processed OpenAI response")
+
+    except Exception as e:
+        print(f"Error occurred: {e}")
+        responses['error'] = str(e)
+
+    try:
+        df = pd.read_excel(xsl_file.name)
+        return responses['SQL'], responses['XML'], responses
+    except Exception as e:
+        return f"Error reading file: {str(e)}", None, None
+
+# Create the Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("# WEF Hackathon Demo")
+
+    
+    # Top section - XLS file upload
+    with gr.Row():
+        xsl_upload = gr.File(
+            label="Upload XLS File",
+            file_types=[".xls"]
+        )
+    
+    # Display section for uploaded data
+    data_display = gr.Dataframe(
+        label="Uploaded Data Preview",
+        interactive=False,
+        wrap=True
+    )
+    
+    # Bottom section - 3 columns
+    with gr.Row():
+        # First column - Natural language input
+        with gr.Column():
+            query_input = gr.Textbox(
+                label="Natural Language Query",
+                placeholder="Enter your query here...",
+                lines=5
+            )
+        
+        # Second column - SQL output
+        with gr.Column():
+            sql_output = gr.Textbox(
+                label="LLM SQL Response",
+                interactive=False
+            )
+        
+        # Third column - XML output
+        with gr.Column():
+            xml_output = gr.Textbox(
+                label="LLM XML Response",
+                interactive=False
+            )
+    
+    # Submit button
+    submit_btn = gr.Button("Process Query")
+    cached_examples = os.getenv('examples')
+    examples = gr.Examples(
+        examples=[
+    '''Join LFA1 (general vendor data) with LFB1 (company code data) by matching LIFNR. Filter on a specific vendor (e.g., LIFNR = "100000"), then retrieve the vendor’s number, name, company code, payment block, and payment terms.''',
+    '''Match LFA1’s address number (ADRNR) to ADR6’s address reference (ADDRNUMBER) to get e-mail data. For a given vendor (e.g., LIFNR = "100000"), select the vendor’s number, name, e-mail address, and validity dates.''',
+    '''Join LFM1 and LFM2 on both vendor number (LIFNR) and purchasing organization (EKORG). Retrieve data like the vendor’s credit group and blocking status for a given vendor (LIFNR) and purchasing org (EKORG).'''
+    '''Link LFBK (vendor’s bank details) to BNKA (bank master) by matching bank key and account (e.g., LFBK.BANKL = BNKA.BANKL and LFBK.BANKN = BNKA.BANKN). For a vendor (LIFNR = "100000"), return their bank account plus the bank’s name and country.''',
+    '''Join BSIK (open vendor items) with LFA1 (vendor data) using LIFNR. Filter on a specific vendor and list open items (document number, amount) alongside the vendor’s name.''',
+    '''Combine CDHDR/CDPOS (change documents) with LFA1 (vendors). Match CDHDR.OBJECTID = LFA1.LIFNR (and ensure CDHDR.OBJECTCLAS = "LFA1"), then join CDHDR.CHANGENR = CDPOS.CHANGENR to display what fields changed, along with old/new values, for a specific vendor.'''
+        ],
+        inputs=query_input
+    )
+
+    xsl_upload.change(fn=upload_file,
+                      inputs=[xsl_upload],
+                      outputs=data_display)
+    
+    # Handle submission
+    submit_btn.click(
+        fn=process_query,
+        inputs=[xsl_upload, query_input],
+        outputs=[sql_output, xml_output]
+    )
+
+if __name__ == "__main__":
+    demo.launch()