Update app.py

app.py

@@ -1,78 +1,14 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
-from deep_translator import GoogleTranslator
-from indic_transliteration import sanscript
-from indic_transliteration.detect import detect as detect_script
-from indic_transliteration.sanscript import transliterate
-import langdetect
-import re 
 
 # Initialize clients
 text_client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 image_client = InferenceClient("SG161222/RealVisXL_V3.0")
 
-def detect_language_script(text: str) -> tuple[str, str]:
-    """Detect language and script of the input text.
-    Returns (language_code, script_type)"""
-    try:
-        # Use confidence threshold to avoid false detections
-        lang_detect = langdetect.detect_langs(text)
-        if lang_detect[0].prob > 0.8:
-            lang = lang_detect[0].lang
-        else:
-            lang = 'en'  # Default to English if unsure
-        
-        script = None
-        try:
-            script = detect_script(text)
-        except:
-            pass
-        return lang, script
-    except:
-        return 'en', None
-
-def is_romanized_indic(text: str) -> bool:
-    """Check if text appears to be romanized Indic language.
-    More strict pattern matching."""
-    bengali_patterns = [
-        r'\b(ami|tumi|apni)\b',  # Common pronouns
-        r'\b(ache|achen|thako|thaken)\b',  # Common verbs
-        r'\b(kemon|bhalo|kharap)\b',  # Common adjectives
-        r'\b(ki|kothay|keno)\b'  # Common question words
-    ]
-    
-    text_lower = text.lower()
-    matches = sum(1 for pattern in bengali_patterns if re.search(pattern, text_lower))
-    return matches >= 2 # Require at least 2 matches to consider it Bengali
-
-def translate_text(text: str, target_lang='en') -> tuple[str, str, bool]:
-    """Translate text to target language, with more conservative translation logic."""
-    if len(text.split()) <= 2 or text.lower() in ['hello', 'hi', 'hey']:
-        return text, 'en', False
-    
-    original_lang, script = detect_language_script(text)
-    is_transliterated = False
-    
-    if original_lang != 'en' and len(text.split()) > 2:
-        try:
-            translator = GoogleTranslator(source='auto', target=target_lang)
-            translated = translator.translate(text)
-            return translated, original_lang, is_transliterated
-        except Exception as e:
-            print(f"Translation error: {e}")
-            return text, 'en', False
-    
-    if original_lang == 'en' and len(text.split()) > 2 and is_romanized_indic(text):
-        text = romanized_to_bengali(text)
-        return translate_text(text, target_lang)  # Recursive call with Bengali script
-    
-    return text, 'en', False
-
 def check_custom_responses(message: str) -> str:
     """Check for specific patterns and return custom responses."""
     message_lower = message.lower()
     custom_responses = {
-        # For "what is ur name?"
         "what is ur name?": "xylaria",
         "what is ur Name?": "xylaria",
         "what is Ur name?": "xylaria",
@@ -227,44 +163,16 @@ def generate_image(prompt: str) -> str:
                 "negative_prompt": "(worst quality, low quality, illustration, 3d, 2d, painting, cartoons, sketch), open mouth",
                 "num_inference_steps": 30,
                 "guidance_scale": 7.5,
-                "sampling_steps": 15,  # Adjusted parameter
+                "sampling_steps": 15,
                 "upscaler": "4x-UltraSharp",
-                "denoising_strength": 0.5,  # Denoising strength between 0.1 and 0.5
+                "denoising_strength": 0.5,
             }
         )
-        return response  # Assuming response contains the image as required
+        return response
     except Exception as e:
         print(f"Image generation error: {e}")
         return None
 
-def romanized_to_bengali(text: str) -> str:
-    """Convert romanized Bengali text to Bengali script."""
-    bengali_mappings = {
-        'ami': 'আমি',
-        'tumi': 'তুমি',
-        'apni': 'আপনি',
-        'kemon': 'কেমন',
-        'achen': 'আছেন',
-        'acchen': 'আছেন',
-        'bhalo': 'ভালো',
-        'achi': 'আছি',
-        'ki': 'কি',
-        'kothay': 'কোথায়',
-        'keno': 'কেন',
-    }
-    
-    text_lower = text.lower()
-    for roman, bengali in bengali_mappings.items():
-        text_lower = re.sub(r'\b' + roman + r'\b', bengali, text_lower)
-    
-    if text_lower == text.lower():
-        try:
-            return transliterate(text, sanscript.ITRANS, sanscript.BENGALI)
-        except:
-            return text
-            
-    return text_lower
-
 def respond(
     message,
     history: list[tuple[str, str]],
@@ -289,62 +197,42 @@ def respond(
         except Exception as e:
             return f"An error occurred while generating the image: {str(e)}"
 
-    # Handle translation with more conservative approach
-    translated_msg, original_lang, was_transliterated = translate_text(message)
-
-    # Prepare conversation history - only translate if necessary
+    # Prepare conversation history
     messages = [{"role": "system", "content": system_message}]
     for val in history:
         if val[0]:
-            # Only translate longer messages
-            if len(val[0].split()) > 2:
-                trans_user_msg, _, _ = translate_text(val[0])
-                messages.append({"role": "user", "content": trans_user_msg})
-            else:
-                messages.append({"role": "user", "content": val[0]})
+            messages.append({"role": "user", "content": val[0]})
         if val[1]:
             messages.append({"role": "assistant", "content": val[1]})
     
-    messages.append({"role": "user", "content": translated_msg})
+    messages.append({"role": "user", "content": message})
 
     # Get response from model
     response = ""
     for message in text_client.chat_completion(
         messages,
         max_tokens=max_tokens,
-        stream=True,  # Ensure streaming is enabled
+        stream=True,
         temperature=temperature,
         top_p=top_p,
     ):
         token = message.choices[0].delta.content
         response += token
-        yield response  # Yield progressively for animation
-
-    # Only translate back if the original was definitely non-English
-    if original_lang != 'en' and len(message.split()) > 2:
-        try:
-            translator = GoogleTranslator(source='en', target=original_lang)
-            translated_response = translator.translate(response)
-            yield translated_response
-        except:
-            yield response
-    else:
         yield response
 
-# Custom CSS for the Gradio interface  
-custom_css = """  
-@import url('https://fonts.googleapis.com/css2?family=Inter:wght@400;600&display=swap');  
-  
-/* Apply Inter font to the entire interface */  
-body, .gradio-container {  
-   font-family: 'Inter', sans-serif;  
-}  
-  
-/* Additional styling (add any other required custom CSS here) */  
-"""  
-  
-# System message (hidden from users, editable in code)  
-system_message = """  
+    yield response
+
+# Custom CSS for the Gradio interface
+custom_css = """
+@import url('https://fonts.googleapis.com/css2?family=Inter:wght@400;600&display=swap');
+
+body, .gradio-container {
+    font-family: 'Inter', sans-serif;
+}
+"""
+
+# System message
+system_message = """
 You are Xylaria, a friendly and capable AI assistant. Your goal is to be helpful and engaging, whether the user wants to discuss math, code, or any other topic.
 
 CORE CAPABILITIES:
@@ -359,52 +247,40 @@ COMMUNICATION STYLE:
 - Sprinkle in emojis and casual expressions to keep things fun 😎
 - Provide the right level of detail, from high-level overviews to deep dives
 
-PROBLEM-SOLVING APPROACH:
-- Carefully understand the user's request or problem
-- Identify the key information and most effective solution method
-- Show step-by-step work and explain your reasoning clearly
-- Verify the final answer is correct and provide any additional context
-
-VERSATILITY IN ACTION:
-- For math problems: "Ooh, a juicy math challenge! Let's do this 🧮"
-- For general questions: "Sure, happy to chat about that! What would you like to know?"
-- For casual conversation: "Hey there! What's on your mind today? I'm all ears 👂"
-
 I'm here to help with all kinds of tasks, from complex problem-solving to friendly discussion. Just let me know what you need, and I'll do my best to assist! 🙌
-"""  
-  
-# Gradio chat interface with the updated CSS  
-demo = gr.ChatInterface(  
-   respond,  
-   additional_inputs=[  
-      gr.Textbox(  
-        value=system_message,  
-        visible=False,  
-          
-      ),  
-      gr.Slider(  
-        minimum=1,  
-        maximum=2048,  
-        value=2048,  
-        step=1,  
-        label="Max new tokens"  
-      ),  
-      gr.Slider(  
-        minimum=0.1,  
-        maximum=4.0,  
-        value=0.7,  
-        step=0.1,  
-        label="Temperature"  
-      ),  
-      gr.Slider(  
-        minimum=0.1,  
-        maximum=1.0,  
-        value=0.95,  
-        step=0.05,  
-        label="Top-p (nucleus sampling)"  
-      ),  
-   ],  
-   css=custom_css  # Apply the custom CSS  
+"""
+
+# Gradio chat interface
+demo = gr.ChatInterface(
+    respond,
+    additional_inputs=[
+        gr.Textbox(
+            value=system_message,
+            visible=False,
+        ),
+        gr.Slider(
+            minimum=1,
+            maximum=2048,
+            value=2048,
+            step=1,
+            label="Max new tokens"
+        ),
+        gr.Slider(
+            minimum=0.1,
+            maximum=4.0,
+            value=0.7,
+            step=0.1,
+            label="Temperature"
+        ),
+        gr.Slider(
+            minimum=0.1,
+            maximum=1.0,
+            value=0.95,
+            step=0.05,
+            label="Top-p (nucleus sampling)"
+        ),
+    ],
+    css=custom_css
 )
 
 demo.launch()