trying redis

app.py

@@ -7,8 +7,12 @@
 import os
 import requests
 from flask_cors import CORS
+import redis
+from _md5 import md5
 
 app = Flask(__name__)
+db=redis.from_url(os.environ['REDISCLOUD_URL'])
+# db = redis.StrictRedis(host='localhost', port=6379, db=0)
 CORS(app)
 
 
@@ -137,27 +141,27 @@ def gpt(self, prompt):
         response = {'answer': answer, 'sources': sources}
         return response
 
-    def reply(self, prompt):
-        print(prompt)
-        prompt = self.create_prompt(df, prompt)
-        return self.gpt(prompt)
-
 @app.route("/", methods=["GET", "POST"])
 def index():
     return render_template("index.html")
 
 @app.route("/process_pdf", methods=['POST'])
 def process_pdf():
     print("Processing pdf")
+    key = md5(request.data).hexdigest()
+    print(key)
+    x = pd.DataFrame(columns=['keys'])
+    x = x.append({'keys': key}, ignore_index=True)
+    x.to_csv('static/keys.csv', mode='a', index=False, header=False)
     file = request.data
     pdf = PdfReader(BytesIO(file))
     chatbot = Chatbot()
     paper_text = chatbot.parse_paper(pdf)
     df = chatbot.paper_df(paper_text)
     df = chatbot.calculate_embeddings(df)
     print(df.head(5))
-    # save dataframe to csv at static/df.csv
-    df.to_csv('static/df.csv')
+    if db.get(key) is None:
+        db.set(key, df.to_json())
     print("Done processing pdf")
     return "PDF file processed and embeddings calculated"
 
@@ -166,13 +170,17 @@ def download_pdf():
     chatbot = Chatbot()
     url = request.json['url']
     r = requests.get(str(url))
-    print(r.headers)
+    key = md5(r.content).hexdigest()
+    x = pd.DataFrame(columns=['keys'])
+    x = x.append({'keys': key}, ignore_index=True)
+    x.to_csv('static/keys.csv', mode='a', index=False, header=False)
     pdf = PdfReader(BytesIO(r.content))
     paper_text = chatbot.parse_paper(pdf)
     df = chatbot.paper_df(paper_text)
     df = chatbot.calculate_embeddings(df)
     print(df.head(5))
-    df.to_csv('static/df.csv')
+    if db.get(key) is None:
+        db.set(key, df.to_json())
     print("Done processing pdf")
     return "PDF file processed and embeddings calculated"
 
@@ -181,7 +189,11 @@ def reply():
     chatbot = Chatbot()
     query = request.json['query']
     query = str(query)
-    df = pd.read_csv('static/df.csv')
+    x = pd.read_csv('static/keys.csv')
+    key = x['keys'].iloc[-1]
+    print(key)
+    print(db.get(key))
+    df = pd.read_json(BytesIO(db.get(key)))
     print(df.head(5))
     prompt = chatbot.create_prompt(df, query)
     response = chatbot.gpt(prompt)

requirements.txt

@@ -7,5 +7,7 @@ flask-cors
 matplotlib
 scipy
 plotly
+redis
+zlib
 gunicorn==20.1.0
 scikit-learn==0.24.1

static/js/script.js

@@ -118,7 +118,7 @@ input.addEventListener("change", async function() {
   const file = this.files[0];
   const fileArrayBuffer = await file.arrayBuffer();
   console.log(fileArrayBuffer);
-  // Make a post request to "http://127.0.0.1:5000/process_pdf" with the file
+  // Make a post request to /process_pdf with the file
   fetch('/process_pdf', {
       method: 'POST',
       body: fileArrayBuffer,

static/keys.csv

@@ -0,0 +1,2 @@
+keys
+d56d71ecadf2137be09d8b1d35c6c042

templates/index.html

@@ -12,7 +12,7 @@
 
     <p style="color: white; margin: 10px; /* padding: 10px; */ /* border-radius: 10px; */ /* background-color: grey; */ width: fit-content; /* max-width: 80%; */ text-align: start; overflow-wrap: break-word; /* font-style: normal; */ font-variant-caps: normal; /* font-weight: normal; */ /* font-stretch: normal; */ /* font-size: 1.2em; */ line-height: normal; font-family: Roboto;">or</p>
 
-    <form id="up" action="http://127.0.0.1:5000/process_pdf" method="post" style="margin-top: 50px; margin-left: 15px;" enctype="multipart/form-data">
+    <form id="up" style="margin-top: 50px; margin-left: 15px;" enctype="multipart/form-data">
       <label for="file-input" class="upload-btn">Upload PDF</label>
       <input type="file" accept=".pdf" id="file-input"/>
     </form>