Add application file

app.py

@@ -0,0 +1,72 @@
+import requests
+import time
+import gradio as gr
+import os
+
+########################
+## Loading the model: ##
+#######################
+api_key = os.environ.get("HF_API_KEY_INFERENCE")
+API_URL = "https://api-inference.huggingface.co/models/chernandezc/distilbert-base-uncased-finetuned-items-multi-label-21122023-AUGMENTED" #Api endpoint.
+headers = {"Authorization": f"Bearer {api_key}"} #This is a read only API key, do not share please :D.
+                                                                            #This is visible just to experiment, I cannot share environment variables.
+
+def query(payload): #Function to use the API.
+	response = requests.post(API_URL, headers=headers, json=payload)
+	return response.json() #Return Json.
+
+# ##########################################################
+# Function to process the output and print classifications #
+############################################################
+def classify_output(item):
+    label_dict = {
+        'LABEL_0': 'Cognition',
+        'LABEL_1': 'Affect',
+        'LABEL_2': 'Self',
+        'LABEL_3': 'Motivation',
+        'LABEL_4': 'Attention',
+        'LABEL_5': 'Overt_Behavior',
+        'LABEL_6': 'Context'
+    }
+
+    output = query({ #Try to query the endpoint.
+      "inputs": item,
+      })
+
+    # If the model is loading, wait and try again.
+    while 'error' in output:
+        time.sleep(output["estimated_time"]) #Sleep the estimated time and try again.
+        output = query({
+          "inputs": item,
+          })
+
+    # Store classifications in a list
+    classifications = []
+
+    # Find the item with the highest score
+    highest_score_item = max(output[0], key=lambda x: x['score'])
+
+    for item in output[0]:
+        # Check if the score is greater than or equal to 0.5
+        if item['score'] >= 0.5:
+            # Append the category and score to the classifications list
+            classifications.append((label_dict[item['label']], item['score']))
+
+    # Construct and print the classification message
+    if classifications:
+        classification_str = ', '.join([f"'{label}' ({score:.2f})" for label, score in classifications])
+
+        output1 = f"The item you provided is classified as: {classification_str}"
+        return output1
+    else:
+        output2 = f"No classifications with a score of 0.5 or higher were found. \n     However, the highest probability was for: '{label_dict[highest_score_item['label']]}' ({round(item['score'],2)}) \n     Use this classification with caution due to uncertainty"
+        return output2
+    
+    #########################################
+    ######## RUN GRADIO APP #################
+    #########################################
+
+demo = gr.Interface(fn=classify_output, inputs="text", outputs="text")
+demo.launch()
+
+