Update app.py

app.py

@@ -1,14 +1,15 @@
 import gradio as gr
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
 import torch
+import torch.nn.functional as F
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline, DistilBertTokenizer, DistilBertForSequenceClassification
 
-# Load models and tokenizers
+# ---------------- Original Sarcasm + Sentiment Models ----------------
 sarcasm_model = AutoModelForSequenceClassification.from_pretrained("dnzblgn/Sarcasm-Detection-Customer-Reviews")
-sentiment_model = AutoModelForSequenceClassification.from_pretrained("dnzblgn/Sentiment-Analysis-Customer-Reviews")
 sarcasm_tokenizer = AutoTokenizer.from_pretrained("dnzblgn/Sarcasm-Detection-Customer-Reviews", use_fast=False)
+
+sentiment_model = AutoModelForSequenceClassification.from_pretrained("dnzblgn/Sentiment-Analysis-Customer-Reviews")
 sentiment_tokenizer = AutoTokenizer.from_pretrained("dnzblgn/Sentiment-Analysis-Customer-Reviews", use_fast=False)
 
-# Function to analyze sentiment
 def analyze_sentiment(sentence):
     inputs = sentiment_tokenizer(sentence, return_tensors="pt", truncation=True, padding=True, max_length=512)
     with torch.no_grad():
@@ -18,7 +19,6 @@ def analyze_sentiment(sentence):
     sentiment_mapping = {1: "Negative", 0: "Positive"}
     return sentiment_mapping[predicted_class]
 
-# Function to detect sarcasm
 def detect_sarcasm(sentence):
     inputs = sarcasm_tokenizer(sentence, return_tensors="pt", truncation=True, padding=True, max_length=512)
     with torch.no_grad():
@@ -27,15 +27,14 @@ def detect_sarcasm(sentence):
     predicted_class = torch.argmax(logits, dim=-1).item()
     return "Sarcasm" if predicted_class == 1 else "Not Sarcasm"
 
-# Combined function for processing sentences
 def process_text_pipeline(text):
-    sentences = text.split("\n")  # Split text into multiple sentences
+    sentences = text.split("\n")
     processed_sentences = []
 
     for sentence in sentences:
         sentence = sentence.strip()
         if not sentence:
-            continue  # Skip empty lines
+            continue
 
         sentiment = analyze_sentiment(sentence)
         if sentiment == "Negative":
@@ -49,7 +48,56 @@ def process_text_pipeline(text):
 
     return "\n".join(processed_sentences)
 
-# CSS for transparent boxes and background image
+# ---------------- Additional Sentiment Models (No Sarcasm) ----------------
+additional_models = {
+    "siebert/sentiment-roberta-large-english": pipeline("sentiment-analysis", model="siebert/sentiment-roberta-large-english"),
+    "assemblyai/bert-large-uncased-sst2": AutoModelForSequenceClassification.from_pretrained("assemblyai/bert-large-uncased-sst2"),
+    "j-hartmann/sentiment-roberta-large-english-3-classes": pipeline("text-classification", model="j-hartmann/sentiment-roberta-large-english-3-classes", return_all_scores=True),
+    "cardiffnlp/twitter-xlm-roberta-base-sentiment": pipeline("sentiment-analysis", model="cardiffnlp/twitter-xlm-roberta-base-sentiment", tokenizer="cardiffnlp/twitter-xlm-roberta-base-sentiment"),
+    "sohan-ai/sentiment-analysis-model-amazon-reviews": DistilBertForSequenceClassification.from_pretrained("sohan-ai/sentiment-analysis-model-amazon-reviews")
+}
+
+def run_sentiment_with_selected_model(text, model_name):
+    if model_name == "siebert/sentiment-roberta-large-english":
+        result = additional_models[model_name](text)[0]
+        emoji = "✅" if result["label"].lower() == "positive" else "❌"
+        return f"{emoji} '{text}' -> {result['label']}"
+
+    elif model_name == "assemblyai/bert-large-uncased-sst2":
+        tokenizer = AutoTokenizer.from_pretrained(model_name)
+        model = additional_models[model_name]
+        tokens = tokenizer([text], return_tensors="pt", padding=True, truncation=True)
+        outputs = F.softmax(model(**tokens).logits, dim=1)
+        prob_pos = outputs[0][1].item()
+        prob_neg = outputs[0][0].item()
+        emoji = "✅" if prob_pos > prob_neg else "❌"
+        return f"{emoji} '{text}' -> Positive: {prob_pos:.2%}, Negative: {prob_neg:.2%}"
+
+    elif model_name == "j-hartmann/sentiment-roberta-large-english-3-classes":
+        results = additional_models[model_name](text)[0]
+        label_scores = {res['label']: res['score'] for res in results}
+        label = max(label_scores, key=label_scores.get)
+        emoji = "✅" if "positive" in label.lower() else "❌" if "negative" in label.lower() else "⚠️"
+        score_str = ", ".join([f"{k}: {v:.2%}" for k, v in label_scores.items()])
+        return f"{emoji} '{text}' -> {score_str}"
+
+    elif model_name == "cardiffnlp/twitter-xlm-roberta-base-sentiment":
+        result = additional_models[model_name](text)[0]
+        emoji = "✅" if result["label"].lower() == "positive" else "❌" if result["label"].lower() == "negative" else "⚠️"
+        return f"{emoji} '{text}' -> {result['label']}"
+
+    elif model_name == "sohan-ai/sentiment-analysis-model-amazon-reviews":
+        tokenizer = DistilBertTokenizer.from_pretrained("distilbert-base-uncased")
+        model = additional_models[model_name]
+        inputs = tokenizer(text, return_tensors="pt")
+        outputs = model(**inputs)
+        label = "Positive" if outputs.logits.argmax().item() == 1 else "Negative"
+        emoji = "✅" if label == "Positive" else "❌"
+        return f"{emoji} '{text}' -> {label}"
+
+    return f"⚠️ Could not process with selected model."
+
+# ---------------- Gradio UI ----------------
 background_css = """
 .gradio-container {
     background-image: url('https://huggingface.co/spaces/dnzblgn/Sarcasm_Detection/resolve/main/image.png');
@@ -57,20 +105,17 @@ background_css = """
     background-position: center;
     color: white;
 }
-
 .gr-input, .gr-textbox {
-    background-color: rgba(255, 255, 255, 0.3) !important;  /* Make the boxes transparent */
+    background-color: rgba(255, 255, 255, 0.3) !important;
     border-radius: 10px;
     padding: 10px;
     color: black !important;
 }
-
 h1, h2, p {
-    text-shadow: 1px 1px 2px rgba(0, 0, 0, 0.8);  /* Add subtle shadow to text for better readability */
+    text-shadow: 1px 1px 2px rgba(0, 0, 0, 0.8);
 }
 """
 
-# Gradio UI with updated header and transparent design
 with gr.Blocks(css=background_css) as interface:
     gr.Markdown(
         """
@@ -81,14 +126,9 @@ with gr.Blocks(css=background_css) as interface:
 
     with gr.Tab("Text Input"):
         with gr.Row():
-            text_input = gr.Textbox(
-                lines=10,
-                label="Enter Sentences",
-                placeholder="Enter one or more sentences, each on a new line."
-            )
+            text_input = gr.Textbox(lines=10, label="Enter Sentences", placeholder="Enter one or more sentences, each on a new line.")
             result_output = gr.Textbox(label="Results", lines=10, interactive=False)
         analyze_button = gr.Button("🔍 Analyze")
-
         analyze_button.click(process_text_pipeline, inputs=text_input, outputs=result_output)
 
     with gr.Tab("Upload Text File"):
@@ -101,6 +141,19 @@ with gr.Blocks(css=background_css) as interface:
 
         file_input.change(process_file, inputs=file_input, outputs=file_output)
 
-# Run the interface
+    with gr.Tab("Try Other Sentiment Models (No Sarcasm)"):
+        with gr.Row():
+            other_model_selector = gr.Dropdown(
+                choices=list(additional_models.keys()),
+                label="Choose a Sentiment Model"
+            )
+        with gr.Row():
+            model_text_input = gr.Textbox(lines=5, label="Enter Sentence")
+            model_result_output = gr.Textbox(label="Sentiment", lines=3, interactive=False)
+
+        run_model_btn = gr.Button("Run")
+        run_model_btn.click(run_sentiment_with_selected_model, inputs=[model_text_input, other_model_selector], outputs=model_result_output)
+
+# ---------------- Run App ----------------
 if __name__ == "__main__":
     interface.launch()