Update app.py

app.py

@@ -1,7 +1,113 @@
 import gradio as gr
+import pandas as pd
+from groq import Groq
 
-def greet(name):
-    return "Hello " + name + "!!"
+def excel_to_csv(fi):
+    df = pd.read_excel(fi)
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
+    columns = ';'.join(df.columns)
+    
+    df = df.to_csv(path_or_buf = "here2.csv")
+
+    return gr.Dropdown(choices=columns, label="Columns of the file", multiselect=True, allow_custom_value=True), "here2.csv"
+
+def text_to_neoj(rm, cm):
+    prompt = """
+    
+    Here is a random Neo4J database with additionnal informations in '#' :  
+    
+    (d:Document {URL: string (#contains the url of the document), type: string (#contains the type of the document), Description: string (#contains the description of the document)} -[:TALKS_ABOUT]-> (t: Topic {title: string (#contains the title of the topic), description: string (#contains the description of the topic)}) <-[:INTERESTED_BY]- (e: Expert {name: string (#contains the name of the expert)};
+    
+      
+    (s:Solution {url: string (#contains the url of the document the solution refers to), description: string (#contains the description of the solution})-[:SOLUTION_OF]->(d)
+    (p:Problem {url: string (#contains the url of the document the problem refers to), description: string (#contains the description of the problem})-[:PROBLEM_OF]->(d);
+    
+    ---
+    Here is a exemple of a Cypher script for this data batase: 
+    
+    //KeyIssues
+    LOAD CSV WITH HEADERS FROM 'file:///dataKeyIssues.csv' AS row
+    FIELDTERMINATOR ';'
+    WITH row WHERE row.Expert IS NOT NULL AND row.Problems IS NULL  
+    WITH row, split(replace(row.Description,", This topic","This topic"), 'This topic') as Descriptions, split(row.Topic, ',') as Topics, split(row.Expert, ',') AS Experts, split(row.Score, ',') as Score 
+    
+    MERGE (dd:Document {uri: row.Document, description: row.`Key Issue`, type: "TR"})
+    
+    FOREACH (i IN RANGE(0, size(Descriptions) - 2) |
+        MERGE (t:Topic {name: trim(Topics[i]), description: trim(Descriptions[i+1])})
+        MERGE (dd)-[r:TALKS_ABOUT{}]->(t)
+             ON CREATE set r.score = Score[i]
+             ON MATCH SET
+             r.score = CASE WHEN Score[i] > r.score 
+             THEN Score[i]
+             ELSE r.score END
+        MERGE (e:Expert {name: trim(Experts[i])})
+        MERGE (e)-[:INTERESTED_BY]->(t)
+    );
+    
+    LOAD CSV WITH HEADERS FROM 'file:///dataKeyIssues.csv' AS row
+    FIELDTERMINATOR ';'
+    WITH row WHERE  row.Problems IS NOT NULL  
+    WITH row, split(replace(row.Description,", This topic","This topic"), 'This topic') as Descriptions, split(row.Topic, ',') as Topics
+    
+    MERGE (dd:Document {uri: row.Document, keyIssues: row.`Key Issue`, type: "TR"})
+    MERGE (p:Problem {description: row.Problems})
+    MERGE (p)-[:PROBLEM_OF]->(dd)
+    ---
+    With the first line of a CSV file, provide me a Neo4J Cypher script which implement the csv file in the database. Here are some remarks about this file : %s .Only provide a script based on the data given in the CSV file: 
+    
+    First line = %s
+    """ % (rm,cm)
+
+    messages = [
+        {
+            "role": "system",
+            "content": f"You are a helpful assistant. Only show your final response to the **User Query**! Do not provide any explanations or details."
+        },
+        {
+            "role": "user",
+            "content": prompt,
+        }
+    ]
+    
+    client = Groq(api_key= os.environ["GROQ_API_KEY1"])
+    chat_completion = client.chat.completions.create(
+        messages=messages,
+        model="llama3-70b-8192",
+    )
+    
+    response = chat_completion.choices[0].message.content
+    print(response)
+    
+    text_file = open("Output.txt", "w")
+    
+    text_file.write(response)
+    
+    text_file.close()
+    
+    return "Output.txt"
+
+
+
+with gr.Blocks() as demo:
+    
+    with gr.Tab("Excel to Neo4J"):
+        gr.Markdown("### Transfer your excel data in a Neo4J database using this tool !")
+
+        ex_fi = gr.File(file_count='single')
+        csv_fi = gr.File(file_count='single')
+
+        columns = gr.Dropdown(label="Columns of the file", multiselect=True, allow_custom_value=True)
+
+        remarks = gr.Dropdown(["This file do not contain information about Topic or Expert Nodes", "This file do not contain information about Document Nodes", "This file do not contain information about Solution Nodes", "This file do not contain information about Problem nodes"], label="Remarks", multiselect=True, allow_custom_value=True)
+
+        btn_submit = gr.Button("Submit")
+
+        result_fi = gr.File(file_count='single')
+        
+        ex_fi.upload(excel_to_csv, inputs=ex_fi, outputs=[ex_fi, csv_fi])
+
+        btn_submit.click(text_to_neo4j, inputs=[remarks, columns], outputs=result_fi)
+        
+    
 demo.launch()