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app.py

@@ -1,3 +1,78 @@
-import gradio as gr
-
-gr.load("models/dejanseo/good-vibes").launch()
+import streamlit as st
+from transformers import AlbertTokenizer, AlbertForSequenceClassification
+import torch
+import trafilatura
+import nltk
+from nltk.tokenize import sent_tokenize
+
+# Download NLTK data
+nltk.download('punkt')
+
+# Load the tokenizer and model from Hugging Face
+tokenizer = AlbertTokenizer.from_pretrained("dejanseo/good-vibes")
+model = AlbertForSequenceClassification.from_pretrained("dejanseo/good-vibes")
+
+# Set Streamlit layout to wide
+st.set_page_config(layout="wide")
+
+# Function to classify text and highlight "Good Vibes" (Label_0) with dynamic opacity
+def classify_and_highlight(text, max_length=512):
+    sentences = sent_tokenize(text)
+    
+    highlighted_text = ""
+    for sentence in sentences:
+        # Tokenize and classify each sentence separately
+        inputs = tokenizer(sentence, return_tensors="pt", truncation=True, padding=True)
+        outputs = model(**inputs)
+        softmax_scores = torch.softmax(outputs.logits, dim=-1)
+        prediction = torch.argmax(softmax_scores, dim=-1).item()
+        confidence = softmax_scores[0][prediction].item() * 100
+        
+        if prediction == 0:  # Label_0 corresponds to "Good Vibes"
+            # Adjust opacity calculation: base +10%
+            opacity = ((confidence - 50) / 100) + 0.1
+            highlighted_text += f'<span style="background-color: rgba(0, 255, 0, {opacity});" title="{confidence:.2f}%">{sentence}</span> '
+        else:
+            highlighted_text += f'{sentence} '
+
+    return highlighted_text.strip()
+
+# Function to extract content from URL using Trafilatura
+def extract_content_from_url(url):
+    downloaded = trafilatura.fetch_url(url)
+    if downloaded:
+        return trafilatura.extract(downloaded)
+    else:
+        return None
+
+# Streamlit app layout
+st.title("Good Vibes Detector - SEO by DEJAN")
+st.write("This app detects good vibes on the internet.")
+
+mode = st.radio("Choose input mode", ("Paste text", "Enter URL"))
+
+if mode == "Paste text":
+    user_text = st.text_area("Paste your text here:")
+    if st.button("Classify"):
+        if user_text:
+            result = classify_and_highlight(user_text)
+            st.markdown(result, unsafe_allow_html=True)
+            st.markdown("---")
+            st.write("This is a custom sentiment classification model developed by [Dejan Marketing](https://dejanmarketing.com/). If you'd like to do a large-scale sentiment analysis on your website or discuss your needs with our team, please [book an appointment here](https://dejanmarketing.com/conference/).")
+        else:
+            st.write("Please paste some text.")
+
+elif mode == "Enter URL":
+    user_url = st.text_input("Enter the URL:")
+    if st.button("Extract and Classify"):
+        if user_url:
+            content = extract_content_from_url(user_url)
+            if content:
+                result = classify_and_highlight(content)
+                st.markdown(result, unsafe_allow_html=True)
+                st.markdown("---")
+                st.write("This is a custom sentiment classification model developed by [Dejan Marketing](https://dejanmarketing.com/). If you'd like to do a large-scale sentiment analysis on your website or discuss your needs with our team, please [book an appointment here](https://dejanmarketing.com/conference/).")
+            else:
+                st.write("Failed to extract content from the URL.")
+        else:
+            st.write("Please enter a URL.")

requirements.txt

@@ -0,0 +1,5 @@
+streamlit
+transformers
+torch
+trafilatura
+nltk