Update app.py

app.py

@@ -19,47 +19,8 @@ model.eval()    # Set model to evaluation mode
 
 
 
-def verify(psudo_id, username, display_name, tweet_content, is_verified, likes):
-    '''
-    Main Endpoint for URaBOT, a POST request that takes in a tweet's data and returns a "bot" score
-
-    Returns: JSON object {"percent": double} 
+def verify(display_name, tweet_content, is_verified, likes):
     
-    payload:
-        "psudo_id": the temporary id of the tweet (as assigned in local HTML from Twitter)
-        "username": the profile's username (@tag)
-        "display_name": the profiles display name
-        "tweet_content": the text content of the tweet
-    '''
-
-    # #========== Error codes ==========#
-
-    # # Confirm that full payload was sent
-    # if 'username' not in request.form:
-    #     return make_response(jsonify({"error": "Invalid request parameters.", "message" : "No username provided"}), 400)
-        
-    # if 'display_name' not in request.form:
-    #     return make_response(jsonify({"error": "Invalid request parameters.", "message" : "No display_name provided"}), 400)
-        
-    # if 'tweet_content' not in request.form:
-    #     return make_response(jsonify({"error": "Invalid request parameters.", "message" : "No tweet_content provided"}), 400)
-        
-    # # Prevent multiple requests for the same tweet
-    # if request.form["psudo_id"] in processed_tweets:
-    #     return make_response(jsonify({"error": "Conflict, tweet is already being/has been processed"}), 409)
-
-
-    # #========== Resolve Multiple Requests ==========#
-
-    # # Add tweet to internal (backend) process list
-    # processed_tweets.append(request.form["psudo_id"])
-
-
-    #========== Return Classification ==========#
-
-    # Process the tweet through the model
-    # input = request.form["tweet_content"] + tokenizer.sep_token + request.form["display_name"] + tokenizer.sep_token +  request.form["is_verified"] + tokenizer.sep_token +  request.form["likes"]
-   
     input = tweet_content + tokenizer.sep_token + display_name + tokenizer.sep_token + is_verified + tokenizer.sep_token + likes
     tokenized_input = tokenizer(input, return_tensors='pt', padding=True, truncation=True).to(device)
     with torch.no_grad():
@@ -90,7 +51,7 @@ For information on how to customize the ChatInterface, peruse the gradio docs: h
 # Set up the Gradio Interface
 iface = gr.Interface(
     fn=verify,              # Function to process input
-    inputs=[gr.Textbox(label= "Text 1"), gr.Textbox(label= "Text 2"), gr.Textbox(label= "Text"), gr.Textbox(label= "Text 4"),gr.Textbox(label= "Text 5"),gr.Textbox(label= "Text 6")],    # Input type (Textbox for text)
+    inputs=[gr.Textbox(label= "Display Name"), gr.Textbox(label= "Tweet Content"), gr.Textbox(label= "IsVerified"), gr.Textbox(label= "Number of Likes")],    # Input type (Textbox for text)
     outputs=gr.Textbox(),   # Output type (Textbox for generated text)
     live=True                        # Optional: To update the result as you type
 )