Added app

app.py

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+# we will take last 8 messages as input and calculate the sentiment of each message
+NUM_MESSAGES = 8
+
+from transformers import pipeline
+import gradio as gr
+
+
+pipe = pipeline("text-classification", model="distilbert/distilbert-base-uncased-finetuned-sst-2-english")
+
+
+def sentiment_analysis(*messages):
+    """
+    Input will be a list of messages.
+    The function calculates the sentiment of each message, and then returns the average sentiment of the messages.
+    while calculating the sentiment, also take positive and negative labels into account.
+    scores are normalized to 0-100 range.
+    """
+    # return 0 if no messages are provided
+    if len(messages) == 0:
+        return 0
+
+    if len(messages) > NUM_MESSAGES:
+        messages = messages[-NUM_MESSAGES:]
+
+    # each message should be of same length, so we will pad the messages
+    # find longest message
+    max_len = max([len(m) for m in messages])
+    # pad each message to the length of the longest message
+    messages = [m.ljust(max_len) for m in messages]
+
+    output = pipe(messages)
+    score = 0
+    for i in range(len(output)):
+        if output[i]['label'] == 'POSITIVE':
+            score += output[i]['score']
+        else:
+            score -= output[i]['score']
+
+    # shift score to 0-100 range
+    score = (score + NUM_MESSAGES) * 50 / NUM_MESSAGES
+    return round(score, 2)
+
+
+demo = gr.Interface(
+    fn=sentiment_analysis,
+    inputs=["text"] * NUM_MESSAGES,
+    outputs=["number"],
+    title="Sentiment Analysis",
+    description=f"Analyze the sentiment of the last {NUM_MESSAGES} messages"
+)
+
+demo.launch()

requirements.txt

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+transformers~=4.38.0