Update app.py

app.py

@@ -615,10 +615,11 @@ With every Rowad program, participants will be mentored by seasoned bankers and
 
 
 
-
 import gradio as gr
 import os
 import csv
+from io import StringIO
+from huggingface_hub import hf_hub_download, HfApi, HfFolder
 
 #State of Union Text
 
@@ -634,6 +635,16 @@ from langchain.vectorstores.faiss import FAISS
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain import OpenAI, VectorDBQA
 
+# Authenticate with your Hugging Face API key
+hf_api = HfApi()
+hf_folder = HfFolder()
+hf_api.login(hf_folder.get_token('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'))
+
+# Hugging Face space variables
+namespace = "AhmedEwis"
+repo_name = "Shagardi_chatbot"
+csv_file_path = "questions.csv"
+
 text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
 texts = text_splitter.split_text(state_of_the_union)
 
@@ -648,21 +659,37 @@ chain = load_chain("lc://chains/vector-db-qa/stuff/chain.json", vectorstore=vect
 def run_chain(query):
     return chain.run(query)
 
-def save_question_to_csv(file_path, question):
-    with open(file_path, 'a', newline='', encoding='utf-8') as csvfile:
-        writer = csv.writer(csvfile)
+def download_csv_from_space():
+    file_url = hf_hub_download(repo_id=f"{namespace}/{repo_name}", filename=csv_file_path)
+    with open(file_url, "r") as f:
+        return f.read()
+
+def save_question_to_csv(question):
+    csv_data = download_csv_from_space()
+    csvfile = StringIO(csv_data)
+    csvfile.seek(0)
+
+    reader = csv.reader(csvfile)
+    rows = list(reader)
+
+    with open(csv_file_path, 'w', newline='', encoding='utf-8') as f:
+        writer = csv.writer(f)
+        for row in rows:
+            writer.writerow(row)
         writer.writerow([question])
 
+    hf_api.upload_file(
+        path_or_fileobj=csv_file_path,
+        repo_id=f"{namespace}/{repo_name}",
+        file_name=csv_file_path,
+    )
+
 inputs = gr.inputs.Textbox(lines=1, label="Enter your Question:")
 outputs = gr.outputs.Textbox(label="Answer:")
 
-
-# Provide an absolute path to the CSV file
-csv_file_path = os.path.abspath('questions.csv')
-
 def run_app(input):
     answer = run_chain(input)
-    save_question_to_csv(csv_file_path, input)
+    save_question_to_csv(input)
     return answer
 
 gr.Interface(fn=run_app, inputs=inputs, outputs=outputs, title="The following is a conversation with a human called Shegardi. Shegardi is helpful, precise, truthful, and very friendly. Also, Shegardi is an employee of Warba Bank, located in Kuwait. Shegardi will only use the information provided to him.").launch()