Create app.py

app.py

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+import gradio as gr
+from zipfile import ZipFile
+import os
+
+# Importing the sentiment_analysis function
+def sentiment_analysis(dated_input):
+    tokenizer = AutoTokenizer.from_pretrained("aliciiavs/sentiment-analysis-whatsapp2")
+    model = AutoModelForSequenceClassification.from_pretrained("aliciiavs/sentiment-analysis-whatsapp2")
+
+    # Tokenize input
+    inputs = tokenizer(dated_input, padding=True, return_tensors="pt")
+
+    # Forward pass through the model
+    with torch.no_grad():
+        outputs = model(**inputs)
+
+    # Get predicted probabilities and predicted label
+    probabilities = torch.softmax(outputs.logits, dim=1)
+    predicted_label = torch.argmax(probabilities, dim=1)
+
+    # Convert the predicted label tensor to a Python integer
+    predicted_label = predicted_label.item()
+
+    # Map predicted label index to sentiment label
+    label_dic = {0: 'sadness', 1: 'joy', 2: 'love', 3: 'anger', 4: 'fear', 5: 'surprise'}
+
+    # Return the predicted sentiment label instead of printing it
+    return label_dic[predicted_label]
+
+# Define a Gradio interface
+def sentiment_analysis_interface(zip_file):
+    # Extract text from zip file
+    with ZipFile(zip_file) as archive:
+        # Assuming each file in the zip contains text
+        text = ""
+        for filename in archive.namelist():
+            with archive.open(filename) as file:
+                text += file.read().decode("utf-8")
+    
+    # Perform sentiment analysis
+    predicted_sentiment = sentiment_analysis(text)
+    
+    # Return the predicted sentiment label
+    return f"Predicted sentiment label: {predicted_sentiment}"
+
+# Create a Gradio interface
+gr.Interface(
+    fn=sentiment_analysis_interface,
+    inputs=gr.inputs.File(type="zip", label="Upload a zip file"),
+    outputs="text"
+).launch()