Update app.py

app.py

@@ -1,297 +1,297 @@
-import requests
-import gradio as gr
-from ragatouille import RAGPretrainedModel
-import logging
-from pathlib import Path
-from time import perf_counter
-from sentence_transformers import CrossEncoder
-from huggingface_hub import InferenceClient
-from jinja2 import Environment, FileSystemLoader
-import numpy as np
-from os import getenv
-from backend.query_llm import generate_hf, generate_qwen
-from backend.semantic_search import table, retriever
-from huggingface_hub import InferenceClient
-
-
-# Bhashini API translation function
-api_key = getenv('API_KEY')
-user_id = getenv('USER_ID')
-
-def bhashini_translate(text: str, from_code: str = "en", to_code: str = "hi") -> dict:
-    """Translates text from source language to target language using the Bhashini API."""
-    
-    if not text.strip():
-        print('Input text is empty. Please provide valid text for translation.')
-        return {"status_code": 400, "message": "Input text is empty", "translated_content": None, "speech_content": None}
-    else:
-        print('Input text - ',text)
-    print(f'Starting translation process from {from_code} to {to_code}...')
-    print(f'Starting translation process from {from_code} to {to_code}...')
-    gr.Warning(f'Translating to {to_code}...')
-    
-    url = 'https://meity-auth.ulcacontrib.org/ulca/apis/v0/model/getModelsPipeline'
-    headers = {
-        "Content-Type": "application/json",
-        "userID": user_id,
-        "ulcaApiKey": api_key
-    }
-    payload = {
-        "pipelineTasks": [{"taskType": "translation", "config": {"language": {"sourceLanguage": from_code, "targetLanguage": to_code}}}],
-        "pipelineRequestConfig": {"pipelineId": "64392f96daac500b55c543cd"}
-    }
-    
-    print('Sending initial request to get the pipeline...')
-    response = requests.post(url, json=payload, headers=headers)
-    
-    if response.status_code != 200:
-        print(f'Error in initial request: {response.status_code}')
-        return {"status_code": response.status_code, "message": "Error in translation request", "translated_content": None}
-
-    print('Initial request successful, processing response...')
-    response_data = response.json()
-    service_id = response_data["pipelineResponseConfig"][0]["config"][0]["serviceId"]
-    callback_url = response_data["pipelineInferenceAPIEndPoint"]["callbackUrl"]
-    
-    print(f'Service ID: {service_id}, Callback URL: {callback_url}')
-    
-    headers2 = {
-        "Content-Type": "application/json",
-        response_data["pipelineInferenceAPIEndPoint"]["inferenceApiKey"]["name"]: response_data["pipelineInferenceAPIEndPoint"]["inferenceApiKey"]["value"]
-    }
-    compute_payload = {
-        "pipelineTasks": [{"taskType": "translation", "config": {"language": {"sourceLanguage": from_code, "targetLanguage": to_code}, "serviceId": service_id}}],
-        "inputData": {"input": [{"source": text}], "audio": [{"audioContent": None}]}
-    }
-    
-    print(f'Sending translation request with text: "{text}"')
-    compute_response = requests.post(callback_url, json=compute_payload, headers=headers2)
-    
-    if compute_response.status_code != 200:
-        print(f'Error in translation request: {compute_response.status_code}')
-        return {"status_code": compute_response.status_code, "message": "Error in translation", "translated_content": None}
-    
-    print('Translation request successful, processing translation...')
-    compute_response_data = compute_response.json()
-    translated_content = compute_response_data["pipelineResponse"][0]["output"][0]["target"]
-    
-    print(f'Translation successful. Translated content: "{translated_content}"')
-    return {"status_code": 200, "message": "Translation successful", "translated_content": translated_content}
-
-
-# Existing chatbot functions
-VECTOR_COLUMN_NAME = "vector"
-TEXT_COLUMN_NAME = "text"
-HF_TOKEN = getenv("HUGGING_FACE_HUB_TOKEN")
-proj_dir = Path(__file__).parent
-
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1", token=HF_TOKEN)
-env = Environment(loader=FileSystemLoader(proj_dir / 'templates'))
-
-template = env.get_template('template.j2')
-template_html = env.get_template('template_html.j2')
-
-# def add_text(history, text):
-#     history = [] if history is None else history
-#     history = history + [(text, None)]
-#     return history, gr.Textbox(value="", interactive=False)
-
-def bot(history, cross_encoder):
-
-    top_rerank = 25
-    top_k_rank = 20
-    query = history[-1][0] if history else ''
-    print('\nQuery: ',query )
-    print('\nHistory:',history)
-    if not query:
-        gr.Warning("Please submit a non-empty string as a prompt")
-        raise ValueError("Empty string was submitted")
-
-    logger.warning('Retrieving documents...')
-    
-    if cross_encoder == '(HIGH ACCURATE) ColBERT':
-        gr.Warning('Retrieving using ColBERT.. First time query will take a minute for model to load..pls wait')
-        RAG = RAGPretrainedModel.from_pretrained("colbert-ir/colbertv2.0")
-        RAG_db = RAG.from_index('.ragatouille/colbert/indexes/cbseclass10index')
-        documents_full = RAG_db.search(query, k=top_k_rank)
-        
-        documents = [item['content'] for item in documents_full]
-        prompt = template.render(documents=documents, query=query)
-        prompt_html = template_html.render(documents=documents, query=query)
-    
-        generate_fn = generate_hf
-    
-        history[-1][1] = ""
-        for character in generate_fn(prompt, history[:-1]):
-            history[-1][1] = character
-            yield history, prompt_html
-    else:
-        document_start = perf_counter()
-    
-        query_vec = retriever.encode(query)
-        doc1 = table.search(query_vec, vector_column_name=VECTOR_COLUMN_NAME).limit(top_k_rank)
-    
-        documents = table.search(query_vec, vector_column_name=VECTOR_COLUMN_NAME).limit(top_rerank).to_list()
-        documents = [doc[TEXT_COLUMN_NAME] for doc in documents]
-    
-        query_doc_pair = [[query, doc] for doc in documents]
-        if cross_encoder == '(FAST) MiniLM-L6v2':
-            cross_encoder1 = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')
-        elif cross_encoder == '(ACCURATE) BGE reranker':
-            cross_encoder1 = CrossEncoder('BAAI/bge-reranker-base')
-        
-        cross_scores = cross_encoder1.predict(query_doc_pair)
-        sim_scores_argsort = list(reversed(np.argsort(cross_scores)))
-        
-        documents = [documents[idx] for idx in sim_scores_argsort[:top_k_rank]]
-    
-        document_time = perf_counter() - document_start
-    
-        prompt = template.render(documents=documents, query=query)
-        prompt_html = template_html.render(documents=documents, query=query)
-    
-        #generate_fn = generate_hf
-        generate_fn=generate_qwen
-        # Create a new history entry instead of modifying the tuple directly
-        new_history = history[:-1] + [ (prompt, "") ] # query replaced prompt
-        output=''
-        # for character in generate_fn(prompt, history[:-1]):
-        #     #new_history[-1] = (query, character) 
-        #     output+=character
-        output=generate_fn(prompt, history[:-1])
-        
-        print('Output:',output)
-        new_history[-1] = (prompt, output) #query replaced with prompt
-        print('New History',new_history)
-        #print('prompt html',prompt_html)# Update the last tuple with new text
-        
-        history_list = list(history[-1])
-        history_list[1] = output  # Assuming `character` is what you want to assign
-        # Update the history with the modified list converted back to a tuple
-        history[-1] = tuple(history_list)
-
-            #history[-1][1] = character
-        # yield new_history, prompt_html
-        yield history, prompt_html
-         # new_history,prompt_html
-        # history[-1][1] = ""
-        # for character in generate_fn(prompt, history[:-1]):
-        #     history[-1][1] = character
-        #     yield history, prompt_html
-
-#def translate_text(response_text, selected_language):
-    
-def translate_text(selected_language,history):
-    
-    iso_language_codes = {
-        "Hindi": "hi",
-        "Gom": "gom",
-        "Kannada": "kn",
-        "Dogri": "doi",
-        "Bodo": "brx",
-        "Urdu": "ur",
-        "Tamil": "ta",
-        "Kashmiri": "ks",
-        "Assamese": "as",
-        "Bengali": "bn",
-        "Marathi": "mr",
-        "Sindhi": "sd",
-        "Maithili": "mai",
-        "Punjabi": "pa",
-        "Malayalam": "ml",
-        "Manipuri": "mni",
-        "Telugu": "te",
-        "Sanskrit": "sa",
-        "Nepali": "ne",
-        "Santali": "sat",
-        "Gujarati": "gu",
-        "Odia": "or"
-    }
-    
-    to_code = iso_language_codes[selected_language]
-    response_text = history[-1][1] if history else ''
-    print('response_text for translation',response_text)
-    translation = bhashini_translate(response_text, to_code=to_code)
-    return translation['translated_content']
-   
-
-# Gradio interface
-with gr.Blocks(theme='gradio/soft') as CHATBOT:
-    history_state = gr.State([])
-    with gr.Row():
-        with gr.Column(scale=10):
-            gr.HTML(value="""<div style="color: #FF4500;"><h1>m-</h1>MITHRA<h1><span style="color: #008000">student Manual Chatbot </span></h1></div>""")
-            gr.HTML(value=f"""<p style="font-family: sans-serif; font-size: 16px;">Using GenAI for CBIC Capacity Building - A free chat bot developed by National Customs Targeting Center using Open source LLMs for CBIC Officers</p>""")
-            gr.HTML(value=f"""<p style="font-family: Arial, sans-serif; font-size: 14px;">Developed by NCTC,Mumbai. Suggestions may be sent to <a href="mailto:nctc-admin@gov.in" style="color: #00008B; font-style: italic;">nctc-admin@gov.in</a>.</p>""")
-
-        with gr.Column(scale=3):
-            gr.Image(value='logo.png', height=200, width=200)
-
-    chatbot = gr.Chatbot(
-        [],
-        elem_id="chatbot",
-        avatar_images=('https://aui.atlassian.com/aui/8.8/docs/images/avatar-person.svg',
-                       'https://huggingface.co/datasets/huggingface/brand-assets/resolve/main/hf-logo.svg'),
-        bubble_full_width=False,
-        show_copy_button=True,
-        show_share_button=True,
-    )
-
-    with gr.Row():
-        txt = gr.Textbox(
-            scale=3,
-            show_label=False,
-            placeholder="Enter text and press enter",
-            container=False,
-        )
-        txt_btn = gr.Button(value="Submit text", scale=1)
-    
-    cross_encoder = gr.Radio(choices=['(FAST) MiniLM-L6v2', '(ACCURATE) BGE reranker', '(HIGH ACCURATE) ColBERT'], value='(ACCURATE) BGE reranker', label="Embeddings", info="Only First query to Colbert may take little time)")
-    language_dropdown = gr.Dropdown(
-        choices=[
-            "Hindi", "Gom", "Kannada", "Dogri", "Bodo", "Urdu", "Tamil", "Kashmiri", "Assamese", "Bengali", "Marathi",
-            "Sindhi", "Maithili", "Punjabi", "Malayalam", "Manipuri", "Telugu", "Sanskrit", "Nepali", "Santali",
-            "Gujarati", "Odia"
-        ],
-        value="Hindi",  # default to Hindi
-        label="Select Language for Translation"
-    )
-    
-    prompt_html = gr.HTML()
-    
-    translated_textbox = gr.Textbox(label="Translated Response")
-    def update_history_and_translate(txt, cross_encoder, history_state, language_dropdown):
-        print('History state',history_state)
-        history = history_state
-        history.append((txt, ""))
-        #history_state.value=(history)
-        
-        # Call bot function
-        # bot_output = list(bot(history, cross_encoder))
-        bot_output = next(bot(history, cross_encoder))
-        print('bot_output',bot_output)
-        #history, prompt_html = bot_output[-1]
-        history, prompt_html = bot_output
-        print('History',history)
-        # Update the history state
-        history_state[:] = history
-        
-        # Translate text
-        translated_text = translate_text(language_dropdown, history)
-        return history, prompt_html, translated_text
-
-    txt_msg = txt_btn.click(update_history_and_translate, [txt, cross_encoder, history_state, language_dropdown], [chatbot, prompt_html, translated_textbox])
-    txt_msg = txt.submit(update_history_and_translate, [txt, cross_encoder, history_state, language_dropdown], [chatbot, prompt_html, translated_textbox])
-
-    examples = ['My transhipment cargo is missing','can u explain and tabulate difference between b 17 bond and a warehousing bond',
-            'What are benefits of  the AEO Scheme and eligibility criteria?',
-            'What are penalties for customs offences? ', 'what are penalties to customs officers misusing their powers under customs act?','What are eligibility criteria for exemption from cost recovery charges','list in detail what is procedure for obtaining new approval for openeing a CFS attached to an ICD']
-
-    gr.Examples(examples, txt)
-
-
-# Launch the Gradio application
-CHATBOT.launch(share=True,debug=True)
-
+import requests
+import gradio as gr
+from ragatouille import RAGPretrainedModel
+import logging
+from pathlib import Path
+from time import perf_counter
+from sentence_transformers import CrossEncoder
+from huggingface_hub import InferenceClient
+from jinja2 import Environment, FileSystemLoader
+import numpy as np
+from os import getenv
+from backend.query_llm import generate_hf, generate_qwen
+from backend.semantic_search import table, retriever
+from huggingface_hub import InferenceClient
+
+
+# Bhashini API translation function
+api_key = getenv('API_KEY')
+user_id = getenv('USER_ID')
+
+def bhashini_translate(text: str, from_code: str = "en", to_code: str = "hi") -> dict:
+    """Translates text from source language to target language using the Bhashini API."""
+    
+    if not text.strip():
+        print('Input text is empty. Please provide valid text for translation.')
+        return {"status_code": 400, "message": "Input text is empty", "translated_content": None, "speech_content": None}
+    else:
+        print('Input text - ',text)
+    print(f'Starting translation process from {from_code} to {to_code}...')
+    print(f'Starting translation process from {from_code} to {to_code}...')
+    gr.Warning(f'Translating to {to_code}...')
+    
+    url = 'https://meity-auth.ulcacontrib.org/ulca/apis/v0/model/getModelsPipeline'
+    headers = {
+        "Content-Type": "application/json",
+        "userID": user_id,
+        "ulcaApiKey": api_key
+    }
+    payload = {
+        "pipelineTasks": [{"taskType": "translation", "config": {"language": {"sourceLanguage": from_code, "targetLanguage": to_code}}}],
+        "pipelineRequestConfig": {"pipelineId": "64392f96daac500b55c543cd"}
+    }
+    
+    print('Sending initial request to get the pipeline...')
+    response = requests.post(url, json=payload, headers=headers)
+    
+    if response.status_code != 200:
+        print(f'Error in initial request: {response.status_code}')
+        return {"status_code": response.status_code, "message": "Error in translation request", "translated_content": None}
+
+    print('Initial request successful, processing response...')
+    response_data = response.json()
+    service_id = response_data["pipelineResponseConfig"][0]["config"][0]["serviceId"]
+    callback_url = response_data["pipelineInferenceAPIEndPoint"]["callbackUrl"]
+    
+    print(f'Service ID: {service_id}, Callback URL: {callback_url}')
+    
+    headers2 = {
+        "Content-Type": "application/json",
+        response_data["pipelineInferenceAPIEndPoint"]["inferenceApiKey"]["name"]: response_data["pipelineInferenceAPIEndPoint"]["inferenceApiKey"]["value"]
+    }
+    compute_payload = {
+        "pipelineTasks": [{"taskType": "translation", "config": {"language": {"sourceLanguage": from_code, "targetLanguage": to_code}, "serviceId": service_id}}],
+        "inputData": {"input": [{"source": text}], "audio": [{"audioContent": None}]}
+    }
+    
+    print(f'Sending translation request with text: "{text}"')
+    compute_response = requests.post(callback_url, json=compute_payload, headers=headers2)
+    
+    if compute_response.status_code != 200:
+        print(f'Error in translation request: {compute_response.status_code}')
+        return {"status_code": compute_response.status_code, "message": "Error in translation", "translated_content": None}
+    
+    print('Translation request successful, processing translation...')
+    compute_response_data = compute_response.json()
+    translated_content = compute_response_data["pipelineResponse"][0]["output"][0]["target"]
+    
+    print(f'Translation successful. Translated content: "{translated_content}"')
+    return {"status_code": 200, "message": "Translation successful", "translated_content": translated_content}
+
+
+# Existing chatbot functions
+VECTOR_COLUMN_NAME = "vector"
+TEXT_COLUMN_NAME = "text"
+HF_TOKEN = getenv("HUGGING_FACE_HUB_TOKEN")
+proj_dir = Path(__file__).parent
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1", token=HF_TOKEN)
+env = Environment(loader=FileSystemLoader(proj_dir / 'templates'))
+
+template = env.get_template('template.j2')
+template_html = env.get_template('template_html.j2')
+
+# def add_text(history, text):
+#     history = [] if history is None else history
+#     history = history + [(text, None)]
+#     return history, gr.Textbox(value="", interactive=False)
+
+def bot(history, cross_encoder):
+
+    top_rerank = 25
+    top_k_rank = 20
+    query = history[-1][0] if history else ''
+    print('\nQuery: ',query )
+    print('\nHistory:',history)
+    if not query:
+        gr.Warning("Please submit a non-empty string as a prompt")
+        raise ValueError("Empty string was submitted")
+
+    logger.warning('Retrieving documents...')
+    
+    if cross_encoder == '(HIGH ACCURATE) ColBERT':
+        gr.Warning('Retrieving using ColBERT.. First time query will take a minute for model to load..pls wait')
+        RAG = RAGPretrainedModel.from_pretrained("colbert-ir/colbertv2.0")
+        RAG_db = RAG.from_index('.ragatouille/colbert/indexes/cbseclass10index')
+        documents_full = RAG_db.search(query, k=top_k_rank)
+        
+        documents = [item['content'] for item in documents_full]
+        prompt = template.render(documents=documents, query=query)
+        prompt_html = template_html.render(documents=documents, query=query)
+    
+        generate_fn = generate_hf
+    
+        history[-1][1] = ""
+        for character in generate_fn(prompt, history[:-1]):
+            history[-1][1] = character
+            yield history, prompt_html
+    else:
+        document_start = perf_counter()
+    
+        query_vec = retriever.encode(query)
+        doc1 = table.search(query_vec, vector_column_name=VECTOR_COLUMN_NAME).limit(top_k_rank)
+    
+        documents = table.search(query_vec, vector_column_name=VECTOR_COLUMN_NAME).limit(top_rerank).to_list()
+        documents = [doc[TEXT_COLUMN_NAME] for doc in documents]
+    
+        query_doc_pair = [[query, doc] for doc in documents]
+        if cross_encoder == '(FAST) MiniLM-L6v2':
+            cross_encoder1 = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')
+        elif cross_encoder == '(ACCURATE) BGE reranker':
+            cross_encoder1 = CrossEncoder('BAAI/bge-reranker-base')
+        
+        cross_scores = cross_encoder1.predict(query_doc_pair)
+        sim_scores_argsort = list(reversed(np.argsort(cross_scores)))
+        
+        documents = [documents[idx] for idx in sim_scores_argsort[:top_k_rank]]
+    
+        document_time = perf_counter() - document_start
+    
+        prompt = template.render(documents=documents, query=query)
+        prompt_html = template_html.render(documents=documents, query=query)
+    
+        #generate_fn = generate_hf
+        generate_fn=generate_qwen
+        # Create a new history entry instead of modifying the tuple directly
+        new_history = history[:-1] + [ (prompt, "") ] # query replaced prompt
+        output=''
+        # for character in generate_fn(prompt, history[:-1]):
+        #     #new_history[-1] = (query, character) 
+        #     output+=character
+        output=generate_fn(prompt, history[:-1])
+        
+        print('Output:',output)
+        new_history[-1] = (prompt, output) #query replaced with prompt
+        print('New History',new_history)
+        #print('prompt html',prompt_html)# Update the last tuple with new text
+        
+        history_list = list(history[-1])
+        history_list[1] = output  # Assuming `character` is what you want to assign
+        # Update the history with the modified list converted back to a tuple
+        history[-1] = tuple(history_list)
+
+            #history[-1][1] = character
+        # yield new_history, prompt_html
+        yield history, prompt_html
+         # new_history,prompt_html
+        # history[-1][1] = ""
+        # for character in generate_fn(prompt, history[:-1]):
+        #     history[-1][1] = character
+        #     yield history, prompt_html
+
+#def translate_text(response_text, selected_language):
+    
+def translate_text(selected_language,history):
+    
+    iso_language_codes = {
+        "Hindi": "hi",
+        "Gom": "gom",
+        "Kannada": "kn",
+        "Dogri": "doi",
+        "Bodo": "brx",
+        "Urdu": "ur",
+        "Tamil": "ta",
+        "Kashmiri": "ks",
+        "Assamese": "as",
+        "Bengali": "bn",
+        "Marathi": "mr",
+        "Sindhi": "sd",
+        "Maithili": "mai",
+        "Punjabi": "pa",
+        "Malayalam": "ml",
+        "Manipuri": "mni",
+        "Telugu": "te",
+        "Sanskrit": "sa",
+        "Nepali": "ne",
+        "Santali": "sat",
+        "Gujarati": "gu",
+        "Odia": "or"
+    }
+    
+    to_code = iso_language_codes[selected_language]
+    response_text = history[-1][1] if history else ''
+    print('response_text for translation',response_text)
+    translation = bhashini_translate(response_text, to_code=to_code)
+    return translation['translated_content']
+   
+
+# Gradio interface
+with gr.Blocks(theme='gradio/soft') as CHATBOT:
+    history_state = gr.State([])
+    with gr.Row():
+        with gr.Column(scale=10):
+            gr.HTML(value="""<div style="color: #FF4500;"><h1>m-</h1>MITHRA<h1><span style="color: #008000">student Manual Chatbot </span></h1></div>""")
+            gr.HTML(value=f"""<p style="font-family: sans-serif; font-size: 16px;">Using GenAI for CBIC Capacity Building - A free chat bot developed by National Customs Targeting Center using Open source LLMs for CBIC Officers</p>""")
+            gr.HTML(value=f"""<p style="font-family: Arial, sans-serif; font-size: 14px;">Developed by NCTC,Mumbai. Suggestions may be sent to <a href="mailto:nctc-admin@gov.in" style="color: #00008B; font-style: italic;">nctc-admin@gov.in</a>.</p>""")
+
+        with gr.Column(scale=3):
+            gr.Image(value='logo.png', height=200, width=200)
+
+    chatbot = gr.Chatbot(
+        [],
+        elem_id="chatbot",
+        avatar_images=('https://aui.atlassian.com/aui/8.8/docs/images/avatar-person.svg',
+                       'https://huggingface.co/datasets/huggingface/brand-assets/resolve/main/hf-logo.svg'),
+        bubble_full_width=False,
+        show_copy_button=True,
+        show_share_button=True,
+    )
+
+    with gr.Row():
+        txt = gr.Textbox(
+            scale=3,
+            show_label=False,
+            placeholder="Enter text and press enter",
+            container=False,
+        )
+        txt_btn = gr.Button(value="Submit text", scale=1)
+    
+    cross_encoder = gr.Radio(choices=['(FAST) MiniLM-L6v2', '(ACCURATE) BGE reranker', '(HIGH ACCURATE) ColBERT'], value='(ACCURATE) BGE reranker', label="Embeddings", info="Only First query to Colbert may take little time)")
+    language_dropdown = gr.Dropdown(
+        choices=[
+            "Hindi", "Gom", "Kannada", "Dogri", "Bodo", "Urdu", "Tamil", "Kashmiri", "Assamese", "Bengali", "Marathi",
+            "Sindhi", "Maithili", "Punjabi", "Malayalam", "Manipuri", "Telugu", "Sanskrit", "Nepali", "Santali",
+            "Gujarati", "Odia"
+        ],
+        value="Hindi",  # default to Hindi
+        label="Select Language for Translation"
+    )
+    
+    prompt_html = gr.HTML()
+    
+    translated_textbox = gr.Textbox(label="Translated Response")
+    def update_history_and_translate(txt, cross_encoder, history_state, language_dropdown):
+        print('History state',history_state)
+        history = history_state
+        history.append((txt, ""))
+        #history_state.value=(history)
+        
+        # Call bot function
+        # bot_output = list(bot(history, cross_encoder))
+        bot_output = next(bot(history, cross_encoder))
+        print('bot_output',bot_output)
+        #history, prompt_html = bot_output[-1]
+        history, prompt_html = bot_output
+        print('History',history)
+        # Update the history state
+        history_state[:] = history
+        
+        # Translate text
+        translated_text = translate_text(language_dropdown, history)
+        return history, prompt_html, translated_text
+
+    txt_msg = txt_btn.click(update_history_and_translate, [txt, cross_encoder, history_state, language_dropdown], [chatbot, prompt_html, translated_textbox])
+    txt_msg = txt.submit(update_history_and_translate, [txt, cross_encoder, history_state, language_dropdown], [chatbot, prompt_html, translated_textbox])
+
+    examples = ['CAN U SAY THE DIFFERENCES BETWEEN METALS AND NON METALS?','WHAT IS IONIC BOND?',
+            'EXPLAIN ASEXUAL REPRODUCTION',
+            
+
+    gr.Examples(examples, txt)
+
+
+# Launch the Gradio application
+CHATBOT.launch(share=True,debug=True)
+