Create app.py

app.py

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+import pickle
+import gradio as gr
+import numpy as np
+from gensim.models import Word2Vec
+
+# Load the logistic regression model
+with open('llogistic_model.pkl', 'rb') as file:
+    model = pickle.load(file)
+
+# Load the Word2Vec model
+word2vec_model = Word2Vec.load('word2vec_model.model')
+
+def sentence_vector(tokens, model):
+    """Calculate the sentence vector by averaging word vectors."""
+    valid_words = [word for word in tokens if word in model.wv]
+    if valid_words:
+        return np.mean(model.wv[valid_words], axis=0)
+    else:
+        return np.zeros(model.vector_size)
+
+def classify_comment(comment):
+    """Classify the sentiment of a comment as bearish, bullish, or neutral."""
+    try:
+        # Tokenize the comment
+        tokens = comment.lower().split()
+
+        # Generate sentence vector using Word2Vec
+        processed_comment = sentence_vector(tokens, word2vec_model).reshape(1, -1)
+        
+        # Predict sentiment
+        prediction = model.predict(processed_comment)[0]
+        
+        # Map prediction to labels (ensure the model output aligns with these labels)
+        sentiment_map = {0: "neutral", 1: "bullish", 2: "bearish"}
+        sentiment = sentiment_map.get(prediction, "unknown")
+
+        return sentiment
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+# Create Gradio interface
+interface = gr.Interface(
+    fn=classify_comment,
+    inputs=gr.Textbox(label="Enter your comment (e.g., about BTC or stock markets):"),
+    outputs=gr.Label(label="Sentiment"),
+    title="BTC Sentiment Analyzer",
+    description="Predict whether a comment is bullish, bearish, or neutral using a logistic regression model."
+)
+
+# Launch the Gradio interface
+interface.launch()