Update app.py

app.py

@@ -1,12 +1,75 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
+from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForCausalLM
+from aksharamukha import transliterate
+import torch
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+# Set up device
+device = "cuda" if torch.cuda.is_available() else "cpu"
 
+# Load translation models and tokenizers
+trans_model = AutoModelForSeq2SeqLM.from_pretrained("facebook/nllb-200-distilled-600M").to(device)
+eng_trans_tokenizer = AutoTokenizer.from_pretrained("facebook/nllb-200-distilled-600M")
+translator = pipeline('translation', model=trans_model, tokenizer=eng_trans_tokenizer, src_lang="eng_Latn", tgt_lang='sin_Sinh', max_length=400, device=device)
 
+sin_trans_model = AutoModelForSeq2SeqLM.from_pretrained("thilina/mt5-sinhalese-english").to(device)
+si_trans_tokenizer = AutoTokenizer.from_pretrained("thilina/mt5-sinhalese-english")
+
+singlish_pipe = pipeline("text2text-generation", model="Dhahlan2000/Simple_Translation-model-for-GPT-v14")
+
+# Translation functions
+def translate_Singlish_to_sinhala(text):
+    translated_text = singlish_pipe(f"translate Singlish to Sinhala: {text}", clean_up_tokenization_spaces=False)[0]['generated_text']
+    return translated_text
+
+def translate_english_to_sinhala(text):
+    parts = text.split("\n")
+    translated_parts = [translator(part, clean_up_tokenization_spaces=False)[0]['translation_text'] for part in parts]
+    return "\n".join(translated_parts).replace("ප් රභූවරුන්", "")
+
+def translate_sinhala_to_english(text):
+    parts = text.split("\n")
+    translated_parts = []
+    for part in parts:
+        inputs = si_trans_tokenizer(part.strip(), return_tensors="pt", padding=True, truncation=True, max_length=512).to(device)
+        outputs = sin_trans_model.generate(**inputs)
+        translated_part = si_trans_tokenizer.decode(outputs[0], skip_special_tokens=True, clean_up_tokenization_spaces=False)
+        translated_parts.append(translated_part)
+    return "\n".join(translated_parts)
+
+def transliterate_from_sinhala(text):
+    latin_text = transliterate.process('Sinhala', 'Velthuis', text).replace('.', '').replace('*', '').replace('"', '').lower()
+    return latin_text
+
+def transliterate_to_sinhala(text):
+    return transliterate.process('Velthuis', 'Sinhala', text)
+
+# Load conversation model
+conv_model_name = "google/gemma-7b"
+tokenizer = AutoTokenizer.from_pretrained(conv_model_name)
+model = AutoModelForCausalLM.from_pretrained(conv_model_name).to(device)
+
+def conversation_predict(text):
+    input_ids = tokenizer(text, return_tensors="pt").to(device)
+    outputs = model.generate(**input_ids)
+    return tokenizer.decode(outputs[0])
+
+def ai_predicted(user_input):
+    if user_input.lower() == 'exit':
+        return "Goodbye!"
+
+    user_input = translate_Singlish_to_sinhala(user_input)
+    user_input = transliterate_to_sinhala(user_input)
+    user_input = translate_sinhala_to_english(user_input)
+
+    ai_response = conversation_predict(user_input)
+    ai_response_lines = ai_response.split("</s>")
+
+    response = translate_english_to_sinhala(ai_response_lines[-1])
+    response = transliterate_from_sinhala(response)
+    return response
+
+# Gradio Interface
 def respond(
     message,
     history: list[tuple[str, str]],
@@ -25,23 +88,10 @@ def respond(
 
     messages.append({"role": "user", "content": message})
 
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
+    response = ai_predicted(message)
 
-        response += token
-        yield response
+    yield response
 
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
 demo = gr.ChatInterface(
     respond,
     additional_inputs=[
@@ -58,6 +108,5 @@ demo = gr.ChatInterface(
     ],
 )
 
-
 if __name__ == "__main__":
     demo.launch()