fix(model): Replace Farsi model with a public one

app.py

@@ -2,46 +2,67 @@
 import gradio as gr
 from transformers import pipeline
 
-# Load the sentiment analysis pipeline
-# This will download the model and tokenizer for the first time.
-# The model is 'distilbert-base-uncased-finetuned-sst-2-english'.
-# It's a lightweight and fast model fine-tuned for sentiment analysis.
-print("Loading sentiment analysis pipeline...")
-sentiment_pipeline = pipeline("sentiment-analysis")
-print("Pipeline loaded successfully.")
-
-# Define the function that will perform the sentiment analysis
-def analyze_sentiment(text):
+# --- Configuration ---
+# Define the models we want to offer in our application.
+# We are now using a confirmed, publicly available Persian model.
+MODELS = {
+    "English": "distilbert-base-uncased-finetuned-sst-2-english",
+    "Persian (Farsi)": "m3hrdadfi/albert-fa-base-v2-sentiment-binary",
+}
+
+# --- State and Caching ---
+# Create a dictionary to cache the loaded pipelines.
+pipeline_cache = {}
+
+def get_pipeline(model_name):
     """
-    Analyzes the sentiment of a given text.
-    
-    Args:
-        text (str): The input text from the user.
-        
-    Returns:
-        str: A formatted string with the prediction and score.
+    Loads a sentiment-analysis pipeline for the given model name.
+    Uses a cache to avoid reloading models that are already in memory.
+    """
+    if model_name not in pipeline_cache:
+        print(f"Loading pipeline for model: {model_name}...")
+        pipeline_cache[model_name] = pipeline("sentiment-analysis", model=model_name)
+        print("Pipeline loaded successfully.")
+    return pipeline_cache[model_name]
+
+# --- Core Logic ---
+def analyze_sentiment(text, model_choice):
+    """
+    Analyzes the sentiment of a given text using the selected model.
     """
-    if text:
-        # The pipeline returns a list of dictionaries.
-        # e.g., [{'label': 'POSITIVE', 'score': 0.999}]
-        result = sentiment_pipeline(text)[0]
-        label = result['label']
-        score = result['score']
-        
-        # Format the output for better readability
-        return f"Sentiment: {label} (Score: {score:.4f})"
-    else:
+    if not text:
         return "Please enter some text to analyze."
+    
+    model_name = MODELS[model_choice]
+    sentiment_pipeline = get_pipeline(model_name)
+    
+    result = sentiment_pipeline(text)[0]
+    label = result['label']
+    score = result['score']
+    
+    return f"Sentiment: {label} (Score: {score:.4f})"
+
+# --- Gradio Interface ---
+with gr.Blocks() as iface:
+    gr.Markdown("# Multi-Lingual Sentiment Analyzer")
+    gr.Markdown("Select a language, enter some text, and see the sentiment analysis. The first time you select a language, the model will take a moment to load.")
+    
+    with gr.Row():
+        model_selector = gr.Dropdown(
+            choices=list(MODELS.keys()), 
+            value="English", 
+            label="Select Language Model"
+        )
+        output_text = gr.Textbox(label="Result", interactive=False)
+
+    input_text = gr.Textbox(lines=5, placeholder="Enter text here...")
+    submit_button = gr.Button("Analyze Sentiment")
 
-# Create the Gradio interface for the application
-iface = gr.Interface(
-    fn=analyze_sentiment,
-    inputs=gr.Textbox(lines=5, placeholder="Enter text here..."),
-    outputs="text",
-    title="Simple Sentiment Analyzer",
-    description="Enter any English text to find out if its sentiment is POSITIVE or NEGATIVE. Powered by Hugging Face Transformers.",
-    allow_flagging="never" # Disables the flagging feature
-)
+    submit_button.click(
+        fn=analyze_sentiment,
+        inputs=[input_text, model_selector],
+        outputs=output_text
+    )
 
 # Launch the web application
 if __name__ == "__main__":