Update app.py

app.py

@@ -31,35 +31,6 @@ from langchain.chains import LLMChain
 
 OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
 
-from googletrans import Translator
-
-def translate_text(text, source_lang, target_lang):
-    translator = Translator()
-    translated = translator.translate(text, src=source_lang, dest=target_lang)
-    return translated.text
-
-# Translate English to Tamil
-english_text = "Hello, how are you?"
-tamil_text = translate_text(english_text, 'en', 'ta')
-print("English to Tamil:", tamil_text)
-
-# Translate Tamil to English
-tamil_text = "வணக்கம், நீங்கள் எப்படி இருக்கின்றீர்கள்?"
-english_text = translate_text(tamil_text, 'ta', 'en')
-print("Tamil to English:", english_text)
-
-# Translate English to Kannada
-english_text = "Hello, how are you?"
-kannada_text = translate_text(english_text, 'en', 'kn')
-print("English to Kannada:", kannada_text)
-
-# Translate Kannada to English
-kannada_text = "ಹಲೋ, ನೀವು ಹೇಗಿದ್ದೀರಿ?"
-english_text = translate_text(kannada_text, 'kn', 'en')
-print("Kannada to English:", english_text)
-
-
-
 def predict(text):
     # loader = UnstructuredPDFLoader(file_obj.orig_name)
     # data = loader.load()
@@ -79,10 +50,23 @@ def predict(text):
 
 Question: {question}
 Product details:"""
+
+    prompt_template_lang = """
+    You are the world's best languages translator. Will give you some text or paragraph which you have to convert into Tamil, Hindi, Kannada
+    and French.
+    Input Text: {text}
+    Tamil: 
+    Hindi:
+    Kannada:
+    French:
+    """
     PROMPT = PromptTemplate(
         template=prompt_template, input_variables=["question"]
     )
-    chain_type_kwargs = {"prompt": PROMPT}
+    PROMPT_lang = PromptTemplate(
+        template=prompt_template_lang, input_variables=["text"]
+    )
+    # chain_type_kwargs = {"prompt": PROMPT}
     # qa = RetrievalQA.from_chain_type(llm=OpenAI(), chain_type="stuff", retriever=docsearch.as_retriever(), chain_type_kwargs=chain_type_kwargs)
 
     #Actually, Hi, how are you doing? Actually, I am looking for the hearing aid for my grandfather. He has like age around 62, 65 year old and one of the like major thing that I am looking for the hearing aid product which is like maximum comfort. So if you have anything in that category, so can you please tell me? Thank you.
@@ -93,7 +77,11 @@ Product details:"""
     #     template="What is a good name for a company that makes {product}?",
     # )
     chain = LLMChain(llm=llm, prompt=PROMPT)
+    chain_lang = LLMChain(llm=llm, prompt=PROMPT_lang)
+    
     resp = chain.run(question=text)
+    resp_lang = chain_lang.run(text=resp)
+    
     # print(resp)
     
     # response = []
@@ -116,7 +104,7 @@ Product details:"""
     #         html_output += value_html
 
     
-    return resp
+    return [resp, resp_lang]
     
 # def ai(qa,category):
 #     query = "please suggest "+ category +" interview questions"
@@ -195,7 +183,7 @@ def inference(audio, sentiment_option):
     prediction  = predict(result.text)
     sentiment_output = display_sentiment_results(sentiment_results, sentiment_option)
 
-    return lang.upper(), result.text, sentiment_output, prediction
+    return lang.upper(), result.text, sentiment_output, prediction[0], prediction[1]
 
 title = """<h1 align="center">🎤 Multilingual ASR 💬</h1>"""
 image_path = "thmbnail.jpg"
@@ -271,8 +259,11 @@ with block:
         sentiment_output = gr.Textbox(label="Sentiment Analysis Results", output=True)
 
         prediction = gr.Textbox(label="Prediction")
+        
+        language_translation = gr.Textbox(label="Language Translation")
+        
 
-        btn.click(inference, inputs=[audio, sentiment_option], outputs=[lang_str, text, sentiment_output, prediction])
+        btn.click(inference, inputs=[audio, sentiment_option], outputs=[lang_str, text, sentiment_output, prediction,language_translation])
 
         # gr.HTML('''
         # <div class="footer">