Update app.py

app.py

@@ -1,56 +1,68 @@
-import streamlit as st
-from transformers import DistilBertTokenizerFast, DistilBertForSequenceClassification
-import torch
-import torch.nn.functional as F
-
-import zipfile
-import shutil
-import os
-
-def unzip_and_save(zip_file_path, extraction_path):
-            shutil.move(source_path, destination_path)
-
-# Example usage:
-# Path to your ZIP file which is your sentimetn analysis model zip
-zip_file_path = 'bert_model_dir.zip'  
-# Destination folder for extraction
-extraction_path = 'bert_model_sentiment_v1'  
-
-unzip_and_save(zip_file_path, extraction_path)
-
-# Load the fine-tuned model and tokenizer
-model_path = "bert_model_sentiment_v1/bert_model_dir"
-tokenizer_path = "bert_model_sentiment_v1/bert_model_dir"
-
-@st.cache_resource
-def load_model():
-    model = DistilBertForSequenceClassification.from_pretrained(model_path)
-    tokenizer = DistilBertTokenizerFast.from_pretrained(tokenizer_path)
-    return model, tokenizer
-
-model, tokenizer = load_model()
-
-def predict_sentiment(text):
-    device = 'cuda' if torch.cuda.is_available() else 'cpu'
-    model.to(device)
-
-    tokenized = tokenizer(text, truncation=True, padding=True, return_tensors='pt').to(device)
-    outputs = model(**tokenized)
-
-    probs = F.softmax(outputs.logits, dim=-1)
-    preds = torch.argmax(outputs.logits, dim=-1).item()
-    probs_max = probs.max().detach().cpu().numpy()
-
-    prediction = "Positive" if preds == 1 else "Negative"
-    return prediction, probs_max * 100
-
-st.title("Sentiment Analysis App")
-text = st.text_area("Enter your text:")
-
-if st.button("Predict Sentiment"):
-    if text:
-        sentiment, confidence = predict_sentiment(text)
-        st.write(f"Sentiment: {sentiment}")
-        st.write(f"Confidence: {confidence:.2f}%")
-    else:
-        st.write("Please enter some text.")
+
+import streamlit as st
+from transformers import DistilBertTokenizerFast, DistilBertForSequenceClassification
+import torch
+import torch.nn.functional as F
+
+import zipfile
+import shutil
+import os
+
+def unzip_and_save(zip_file_path, extraction_path):
+    # Create the extraction directory if it doesn't exist
+    os.makedirs(extraction_path, exist_ok=True)
+
+    with zipfile.ZipFile(zip_file_path, 'r') as zip_ref:
+        folder_name = os.path.basename(zip_file_path).split('.')[0]
+        zip_ref.extractall(extraction_path)
+        source_path = os.path.join(extraction_path, folder_name)
+        destination_path = os.path.join(extraction_path, folder_name)
+        if os.path.exists(destination_path):
+            print(f"Error: Destination path '{destination_path}' already exists")
+        else:
+            shutil.move(source_path, destination_path)
+
+# Example usage:
+# Path to your ZIP file which is your sentimetn analysis model zip
+zip_file_path = 'bert_model_dir.zip'  
+# Destination folder for extraction
+extraction_path = 'bert_model_sentiment_v1'  
+
+unzip_and_save(zip_file_path, extraction_path)
+
+# Load the fine-tuned model and tokenizer
+model_path = "bert_model_sentiment_v1/bert_model_dir"
+tokenizer_path = "bert_model_sentiment_v1/bert_model_dir"
+
+@st.cache_resource
+def load_model():
+    model = DistilBertForSequenceClassification.from_pretrained(model_path)
+    tokenizer = DistilBertTokenizerFast.from_pretrained(tokenizer_path)
+    return model, tokenizer
+
+model, tokenizer = load_model()
+
+def predict_sentiment(text):
+    device = 'cuda' if torch.cuda.is_available() else 'cpu'
+    model.to(device)
+
+    tokenized = tokenizer(text, truncation=True, padding=True, return_tensors='pt').to(device)
+    outputs = model(**tokenized)
+
+    probs = F.softmax(outputs.logits, dim=-1)
+    preds = torch.argmax(outputs.logits, dim=-1).item()
+    probs_max = probs.max().detach().cpu().numpy()
+
+    prediction = "Positive" if preds == 1 else "Negative"
+    return prediction, probs_max * 100
+
+st.title("Sentiment Analysis App")
+text = st.text_area("Enter your text:")
+
+if st.button("Predict Sentiment"):
+    if text:
+        sentiment, confidence = predict_sentiment(text)
+        st.write(f"Sentiment: {sentiment}")
+        st.write(f"Confidence: {confidence:.2f}%")
+    else:
+        st.write("Please enter some text.")