| import gradio as gr | |
| from phi.agent import Agent | |
| from phi.model.groq import Groq | |
| import os | |
| import logging | |
| from sentence_transformers import CrossEncoder | |
| from backend.semantic_search import table, retriever | |
| import numpy as np | |
| from time import perf_counter | |
| import requests | |
| from jinja2 import Environment, FileSystemLoader | |
| from pathlib import Path | |
| # Set up logging | |
| logging.basicConfig(level=logging.INFO) | |
| logger = logging.getLogger(__name__) | |
| # API Key setup | |
| api_key = os.getenv("GROQ_API_KEY") | |
| if not api_key: | |
| gr.Warning("GROQ_API_KEY not found. Set it in 'Repository secrets'.") | |
| logger.error("GROQ_API_KEY not found.") | |
| api_key = "" # Fallback to empty string, but this will fail without a key | |
| else: | |
| os.environ["GROQ_API_KEY"] = api_key | |
| # Bhashini API setup | |
| bhashini_api_key = os.getenv("API_KEY", "").strip() | |
| bhashini_user_id = os.getenv("USER_ID", "").strip() | |
| 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} | |
| else: | |
| print('Input text - ', text) | |
| 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": bhashini_user_id, | |
| "ulcaApiKey": bhashini_api_key | |
| } | |
| for key, value in headers.items(): | |
| if not isinstance(value, str) or '\n' in value or '\r' in value: | |
| print(f"Invalid header value for {key}: {value}") | |
| return {"status_code": 400, "message": f"Invalid header value for {key}", "translated_content": None} | |
| 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}, Response: {response.text}') | |
| return {"status_code": response.status_code, "message": "Error in translation request", "translated_content": None} | |
| print('Initial request successful, processing response...') | |
| response_data = response.json() | |
| print('Full response data:', response_data) | |
| if "pipelineInferenceAPIEndPoint" not in response_data or "callbackUrl" not in response_data["pipelineInferenceAPIEndPoint"]: | |
| print('Unexpected response structure:', response_data) | |
| return {"status_code": 400, "message": "Unexpected API response structure", "translated_content": None} | |
| 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}, Response: {compute_response.text}') | |
| 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} | |
| # Initialize PhiData Agent | |
| agent = Agent( | |
| name="Science Education Assistant", | |
| role="You are a helpful science tutor for 10th-grade students", | |
| instructions=[ | |
| "You are an expert science teacher specializing in 10th-grade curriculum.", | |
| "Provide clear, accurate, and age-appropriate explanations.", | |
| "Use simple language and examples that students can understand.", | |
| "Focus on concepts from physics, chemistry, and biology.", | |
| "Structure responses with headings and bullet points when helpful.", | |
| "Encourage learning and curiosity." | |
| ], | |
| model=Groq(id="llama3-70b-8192", api_key=api_key), | |
| markdown=True | |
| ) | |
| # Set up Jinja2 environment | |
| proj_dir = Path(__file__).parent | |
| env = Environment(loader=FileSystemLoader(proj_dir / 'templates')) | |
| template = env.get_template('template.j2') # For document context | |
| template_html = env.get_template('template_html.j2') # For HTML output | |
| # Response Generation Function | |
| def retrieve_and_generate_response(query, cross_encoder_choice, history=None): | |
| """Generate response using semantic search and LLM""" | |
| top_rerank = 25 | |
| top_k_rank = 20 | |
| if not query.strip(): | |
| return "Please provide a valid question.", [] | |
| try: | |
| start_time = perf_counter() | |
| # Encode query and search documents | |
| query_vec = retriever.encode(query) | |
| documents = table.search(query_vec, vector_column_name="vector").limit(top_rerank).to_list() | |
| documents = [doc["text"] for doc in documents] | |
| # Re-rank documents using cross-encoder | |
| cross_encoder_model = CrossEncoder('BAAI/bge-reranker-base') if cross_encoder_choice == '(ACCURATE) BGE reranker' else CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2') | |
| query_doc_pair = [[query, doc] for doc in documents] | |
| cross_scores = cross_encoder_model.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]] | |
| # Create context from top documents | |
| context = "\n\n".join(documents[:10]) if documents else "" | |
| context = f"Context information from educational materials:\n{context}\n\n" | |
| # Add conversation history for context | |
| history_context = "" | |
| if history and len(history) > 0: | |
| for user_msg, bot_msg in history[-2:]: # Last 2 exchanges | |
| if user_msg and bot_msg: | |
| history_context += f"Previous Q: {user_msg}\nPrevious A: {bot_msg}\n" | |
| # Create full prompt | |
| full_prompt = f"{history_context}{context}Question: {query}\n\nPlease answer the question using the context provided above. If the context doesn't contain relevant information, use your general knowledge about 10th-grade science topics." | |
| # Generate response | |
| response = agent.run(full_prompt) | |
| response_text = response.content if hasattr(response, 'content') else str(response) | |
| logger.info(f"Response generation took {perf_counter() - start_time:.2f} seconds") | |
| return response_text, documents # Return documents for template | |
| except Exception as e: | |
| logger.error(f"Error in response generation: {e}") | |
| return f"Error generating response: {str(e)}", [] | |
| def simple_chat_function(message, history, cross_encoder_choice): | |
| """Chat function with semantic search and retriever integration""" | |
| if not message.strip(): | |
| return "", history, "" | |
| # Generate response and get documents | |
| response, documents = retrieve_and_generate_response(message, cross_encoder_choice, history) | |
| # Add to history | |
| history.append([message, response]) | |
| # Render template with documents and query | |
| prompt_html = template_html.render(documents=documents, query=message) | |
| return "", history, prompt_html | |
| def translate_text(selected_language, history): | |
| """Translate the last response in history to the selected language.""" | |
| 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 and history[-1][1] else '' | |
| print('response_text for translation', response_text) | |
| translation = bhashini_translate(response_text, to_code=to_code) | |
| return translation.get('translated_content', 'Translation failed.') | |
| # Gradio Interface with layout template | |
| with gr.Blocks(title="Science Chatbot", theme='gradio/soft') as demo: | |
| # Header section | |
| with gr.Row(): | |
| with gr.Column(scale=10): | |
| gr.HTML(value="""<div style="color: #FF4500;"><h1>Welcome! I am your friend!</h1>Ask me !I will help you<h1><span style="color: #008000">I AM A CHATBOT FOR 10TH SCIENCE WITH TRANSLATION IN 22 LANGUAGES</span></h1></div>""") | |
| gr.HTML(value=f"""<p style="font-family: sans-serif; font-size: 16px;">A free chat bot developed by K.M.RAMYASRI,TGT,GHS.SUTHUKENY using Open source LLMs for 10 std students</p>""") | |
| gr.HTML(value=f"""<p style="font-family: Arial, sans-serif; font-size: 14px;"> Suggestions may be sent to <a href="mailto:ramyasriraman2019@gmail.com" style="color: #00008B; font-style: italic;">ramyadevi1607@yahoo.com</a>.</p>""") | |
| with gr.Column(scale=3): | |
| try: | |
| gr.Image(value='logo.png', height=200, width=200) | |
| except: | |
| gr.HTML("<div style='height: 200px; width: 200px; background-color: #f0f0f0; display: flex; align-items: center; justify-content: center;'>Logo</div>") | |
| # Chat and input components | |
| 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(): | |
| msg = gr.Textbox( | |
| scale=3, | |
| show_label=False, | |
| placeholder="Enter text and press enter", | |
| container=False, | |
| ) | |
| submit_btn = gr.Button(value="Submit text", scale=1, variant="primary") | |
| # Additional controls | |
| cross_encoder = gr.Radio( | |
| choices=['(FAST) MiniLM-L6v2', '(ACCURATE) BGE reranker'], | |
| value='(ACCURATE) BGE reranker', | |
| label="Embeddings Model", | |
| info="Select the model for document ranking" | |
| ) | |
| 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", | |
| label="Select Language for Translation" | |
| ) | |
| translated_textbox = gr.Textbox(label="Translated Response") | |
| prompt_html = gr.HTML() # Add HTML component for the template | |
| # Event handlers | |
| def update_chat_and_translate(message, history, cross_encoder_choice, selected_language): | |
| if not message.strip(): | |
| return "", history, "", "" | |
| # Generate response and get documents | |
| response, documents = retrieve_and_generate_response(message, cross_encoder_choice, history) | |
| history.append([message, response]) | |
| # Translate response | |
| translated_text = translate_text(selected_language, history) | |
| # Render template with documents and query | |
| prompt_html_content = template_html.render(documents=documents, query=message) | |
| return "", history, translated_text, prompt_html_content | |
| msg.submit(update_chat_and_translate, [msg, chatbot, cross_encoder, language_dropdown], [msg, chatbot, translated_textbox, prompt_html]) | |
| submit_btn.click(update_chat_and_translate, [msg, chatbot, cross_encoder, language_dropdown], [msg, chatbot, translated_textbox, prompt_html]) | |
| clear = gr.Button("Clear Conversation") | |
| clear.click(lambda: ([], "", "", ""), outputs=[chatbot, msg, translated_textbox, prompt_html]) | |
| # Example questions | |
| gr.Examples( | |
| examples=[ | |
| 'What is the difference between metals and non-metals?', | |
| 'What is an ionic bond?', | |
| 'Explain asexual reproduction', | |
| 'What is photosynthesis?', | |
| 'Explain Newton\'s laws of motion' | |
| ], | |
| inputs=msg, | |
| label="Try these example questions:" | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch(server_name="0.0.0.0", server_port=7860)# import gradio as gr | |
| # from phi.agent import Agent | |
| # from phi.model.groq import Groq | |
| # import os | |
| # import logging | |
| # from sentence_transformers import CrossEncoder | |
| # from backend.semantic_search import table, retriever | |
| # import numpy as np | |
| # from time import perf_counter | |
| # import requests | |
| # from jinja2 import Environment, FileSystemLoader | |
| # # Set up logging | |
| # logging.basicConfig(level=logging.INFO) | |
| # logger = logging.getLogger(__name__) | |
| # # API Key setup | |
| # api_key = os.getenv("GROQ_API_KEY") | |
| # if not api_key: | |
| # gr.Warning("GROQ_API_KEY not found. Set it in 'Repository secrets'.") | |
| # logger.error("GROQ_API_KEY not found.") | |
| # api_key = "" # Fallback to empty string, but this will fail without a key | |
| # else: | |
| # os.environ["GROQ_API_KEY"] = api_key | |
| # # Bhashini API setup | |
| # bhashini_api_key = os.getenv("API_KEY") | |
| # bhashini_user_id = os.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} | |
| # else: | |
| # print('Input text - ', text) | |
| # 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": bhashini_user_id, | |
| # "ulcaApiKey": bhashini_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}, Response: {response.text}') | |
| # return {"status_code": response.status_code, "message": "Error in translation request", "translated_content": None} | |
| # print('Initial request successful, processing response...') | |
| # response_data = response.json() | |
| # print('Full response data:', response_data) # Debug the full response | |
| # if "pipelineInferenceAPIEndPoint" not in response_data or "callbackUrl" not in response_data["pipelineInferenceAPIEndPoint"]: | |
| # print('Unexpected response structure:', response_data) | |
| # return {"status_code": 400, "message": "Unexpected API response structure", "translated_content": None} | |
| # 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}, Response: {compute_response.text}') | |
| # 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} | |
| # # Initialize PhiData Agent | |
| # agent = Agent( | |
| # name="Science Education Assistant", | |
| # role="You are a helpful science tutor for 10th-grade students", | |
| # instructions=[ | |
| # "You are an expert science teacher specializing in 10th-grade curriculum.", | |
| # "Provide clear, accurate, and age-appropriate explanations.", | |
| # "Use simple language and examples that students can understand.", | |
| # "Focus on concepts from physics, chemistry, and biology.", | |
| # "Structure responses with headings and bullet points when helpful.", | |
| # "Encourage learning and curiosity." | |
| # ], | |
| # model=Groq(id="llama3-70b-8192", api_key=api_key), | |
| # markdown=True | |
| # ) | |
| # # Set up Jinja2 environment | |
| # proj_dir = Path(__file__).parent | |
| # env = Environment(loader=FileSystemLoader(proj_dir / 'templates')) | |
| # template_html = env.get_template('template_html.j2') | |
| # # Response Generation Function | |
| # def retrieve_and_generate_response(query, cross_encoder_choice, history=None): | |
| # """Generate response using semantic search and LLM""" | |
| # top_rerank = 25 | |
| # top_k_rank = 20 | |
| # if not query.strip(): | |
| # return "Please provide a valid question." | |
| # try: | |
| # start_time = perf_counter() | |
| # # Encode query and search documents | |
| # query_vec = retriever.encode(query) | |
| # documents = table.search(query_vec, vector_column_name="vector").limit(top_rerank).to_list() | |
| # documents = [doc["text"] for doc in documents] | |
| # # Re-rank documents using cross-encoder | |
| # cross_encoder_model = CrossEncoder('BAAI/bge-reranker-base') if cross_encoder_choice == '(ACCURATE) BGE reranker' else CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2') | |
| # query_doc_pair = [[query, doc] for doc in documents] | |
| # cross_scores = cross_encoder_model.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]] | |
| # # Create context from top documents | |
| # context = "\n\n".join(documents[:10]) if documents else "" | |
| # context = f"Context information from educational materials:\n{context}\n\n" | |
| # # Add conversation history for context | |
| # history_context = "" | |
| # if history and len(history) > 0: | |
| # for user_msg, bot_msg in history[-2:]: # Last 2 exchanges | |
| # if user_msg and bot_msg: | |
| # history_context += f"Previous Q: {user_msg}\nPrevious A: {bot_msg}\n" | |
| # # Create full prompt | |
| # full_prompt = f"{history_context}{context}Question: {query}\n\nPlease answer the question using the context provided above. If the context doesn't contain relevant information, use your general knowledge about 10th-grade science topics." | |
| # # Generate response | |
| # response = agent.run(full_prompt) | |
| # response_text = response.content if hasattr(response, 'content') else str(response) | |
| # logger.info(f"Response generation took {perf_counter() - start_time:.2f} seconds") | |
| # return response_text | |
| # except Exception as e: | |
| # logger.error(f"Error in response generation: {e}") | |
| # return f"Error generating response: {str(e)}" | |
| # def simple_chat_function(message, history, cross_encoder_choice): | |
| # """Chat function with semantic search and retriever integration""" | |
| # if not message.strip(): | |
| # return "", history | |
| # # Generate response using the semantic search function | |
| # response = retrieve_and_generate_response(message, cross_encoder_choice, history) | |
| # # Add to history | |
| # history.append([message, response]) | |
| # return "", history | |
| # def translate_text(selected_language, history): | |
| # """Translate the last response in history to the selected language.""" | |
| # 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 and history[-1][1] else '' | |
| # print('response_text for translation', response_text) | |
| # translation = bhashini_translate(response_text, to_code=to_code) | |
| # return translation.get('translated_content', 'Translation failed.') | |
| # # Gradio Interface with layout template | |
| # with gr.Blocks(title="Science Chatbot", theme='gradio/soft') as demo: | |
| # # Header section | |
| # with gr.Row(): | |
| # with gr.Column(scale=10): | |
| # gr.HTML(value="""<div style="color: #FF4500;"><h1>Welcome! I am your friend!</h1>Ask me !I will help you<h1><span style="color: #008000">I AM A CHATBOT FOR 10TH SCIENCE WITH TRANSLATION IN 22 LANGUAGES</span></h1></div>""") | |
| # gr.HTML(value=f"""<p style="font-family: sans-serif; font-size: 16px;">A free chat bot developed by K.M.RAMYASRI,TGT,GHS.SUTHUKENY using Open source LLMs for 10 std students</p>""") | |
| # gr.HTML(value=f"""<p style="font-family: Arial, sans-serif; font-size: 14px;"> Suggestions may be sent to <a href="mailto:ramyasriraman2019@gmail.com" style="color: #00008B; font-style: italic;">ramyadevi1607@yahoo.com</a>.</p>""") | |
| # with gr.Column(scale=3): | |
| # try: | |
| # gr.Image(value='logo.png', height=200, width=200) | |
| # except: | |
| # gr.HTML("<div style='height: 200px; width: 200px; background-color: #f0f0f0; display: flex; align-items: center; justify-content: center;'>Logo</div>") | |
| # # Chat and input components | |
| # 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(): | |
| # msg = gr.Textbox( | |
| # scale=3, | |
| # show_label=False, | |
| # placeholder="Enter text and press enter", | |
| # container=False, | |
| # ) | |
| # submit_btn = gr.Button(value="Submit text", scale=1, variant="primary") | |
| # # Additional controls | |
| # cross_encoder = gr.Radio( | |
| # choices=['(FAST) MiniLM-L6v2', '(ACCURATE) BGE reranker'], | |
| # value='(ACCURATE) BGE reranker', | |
| # label="Embeddings Model", | |
| # info="Select the model for document ranking" | |
| # ) | |
| # 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", | |
| # label="Select Language for Translation" | |
| # ) | |
| # translated_textbox = gr.Textbox(label="Translated Response") | |
| # # Event handlers | |
| # def update_chat_and_translate(message, history, cross_encoder_choice, selected_language): | |
| # if not message.strip(): | |
| # return "", history, "" | |
| # # Generate response | |
| # response = retrieve_and_generate_response(message, cross_encoder_choice, history) | |
| # history.append([message, response]) | |
| # # Translate response | |
| # translated_text = translate_text(selected_language, history) | |
| # return "", history, translated_text | |
| # msg.submit(update_chat_and_translate, [msg, chatbot, cross_encoder, language_dropdown], [msg, chatbot, translated_textbox]) | |
| # submit_btn.click(update_chat_and_translate, [msg, chatbot, cross_encoder, language_dropdown], [msg, chatbot, translated_textbox]) | |
| # clear = gr.Button("Clear Conversation") | |
| # clear.click(lambda: ([], "", ""), outputs=[chatbot, msg, translated_textbox]) | |
| # # Example questions | |
| # gr.Examples( | |
| # examples=[ | |
| # 'What is the difference between metals and non-metals?', | |
| # 'What is an ionic bond?', | |
| # 'Explain asexual reproduction', | |
| # 'What is photosynthesis?', | |
| # 'Explain Newton\'s laws of motion' | |
| # ], | |
| # inputs=msg, | |
| # label="Try these example questions:" | |
| # ) | |
| # if __name__ == "__main__": | |
| # demo.launch(server_name="0.0.0.0", server_port=7860)# import gradio as gr | |