Update app.py

**The Eternal Equation: (- = +)**
A conceptual framework made real. This tool demonstrates how simple interfaces can harness complex AI.

**🎯 Purpose:**
To make powerful AI models accessible, understandable, and usable for everyone through a clean, intuitive interface based on the universal pattern of Input -> Process -> Output.

**⚡ Current Capabilities:**
• **Sentiment Analysis:** Determine the emotional tone (Positive/Negative) of any text.
• **Text Summarization:** Condense long articles, reports, or paragraphs into concise summaries.

**⚠️ Important Limitations:**
• **Summarization Length:** The summarization model works best with texts between **50 and 500 words**. Shorter texts will fail; longer texts will be automatically truncated, potentially losing context.
• **Processing Speed:** Hosted on free-tier CPU hardware. Processing, especially for summarization, may take 10-30 seconds.
• **Model Specificity:** These are general-purpose models. Performance may vary on highly technical, poetic, or informal language (e.g., social media slang).
• **Beta Stage:** This is a live demo and proof-of-concept. Outputs may occasionally be imperfect.

**🔮 The Future:** This is the first step. Translation, code analysis, and image-based models are coming soon.

**Your feedback and ideas are the most valuable input of all.**
"""

app.py

@@ -4,13 +4,22 @@ from transformers import pipeline
 # Title and Description for your app
 TITLE = "🧠 Eternal Equation AI Processor (- = +)"
 DESCRIPTION = """
-Paste your text, choose a processing mode, and see the AI magic happen!
+**The Eternal Equation: (- = +)**  
+A conceptual framework made real. This tool demonstrates how simple interfaces can harness complex AI.
 
-**Current Model Capabilities:**
-*   **Sentiment Analysis:** Handles very long texts.
-*   **Text Summarization:** Best for articles or long paragraphs (up to ~500 words). Longer texts will be truncated.
+**🎯 Purpose:**  
+To make powerful AI models accessible, understandable, and usable for everyone through a clean, intuitive interface based on the universal pattern of Input -> Process -> Output.
 
-This demo runs on free CPU hardware, so please be patient.
+**⚡ Current Capabilities:**  
+• **Sentiment Analysis:** Determine the emotional tone (Positive/Negative) of any text.  
+• **Text Summarization:** Condense long articles, reports, or paragraphs into concise summaries.
+
+**⚠️ Important Limitations:**  
+• **Summarization Length:** The summarization model works best with texts between **50 and 500 words**. Longer texts will be automatically truncated to fit the model's limits.
+• **Processing Speed:** Hosted on free-tier CPU hardware. Processing, especially for summarization, may take 10-30 seconds.
+• **Beta Stage:** This is a live demo and proof-of-concept. Outputs may occasionally be imperfect.
+
+**🔮 The Future:** This is the first step. Translation, code analysis, and image-based models are coming soon.
 """
 
 # Load your specialist models (The Orchestra) - They load when the Space starts
@@ -40,12 +49,25 @@ def process_text(input_text, mode):
         if word_count < 50:
             return "📝 **Please provide a longer text for summarization (at least 50 words).** This model is designed for articles and paragraphs."
         
-        # Now we know the text is long enough, so we try the model
+        # --- NEW CODE: SMART TRUNCATION ---
+        # If the text is too long, we need to shorten it for the model
+        max_word_limit = 500  # A safe estimate well under the model's token limit
+        if word_count > max_word_limit:
+            # Truncate the list of words and join them back into a string
+            truncated_text = " ".join(word_list[:max_word_limit])
+            # Inform the user we did this
+            warning_msg = f"⚠️ Note: Your text was very long ({word_count} words). Summarized the first {max_word_limit} words to ensure processing.\n\n"
+        else:
+            truncated_text = input_text
+            warning_msg = ""
+        # --- END NEW CODE ---
+        
+        # Now we try the model with the (potentially truncated) text
         try:
-            result = summarize_pipeline(input_text, max_length=130, min_length=30, do_sample=False)
-            return f"📄 **Summary:**\n\n{result[0]['summary_text']}"
+            result = summarize_pipeline(truncated_text, max_length=130, min_length=30, do_sample=False)
+            return f"{warning_msg}📄 **Summary:**\n\n{result[0]['summary_text']}"
         except Exception as e:
-            return f"❌ The summarization model failed. This might be due to a very unusual text format. Please try a different article or paragraph.\n(Error: {str(e)})"
+            return f"❌ The summarization model failed. Please try a different article or paragraph.\n(Error: {str(e)})"
             
     # Handle any other mode that might be added in the future
     else: