Update app.py

app.py

@@ -83,7 +83,7 @@ def retrieve_and_generate_response(query, cross_encoder_choice, history=None):
         logger.error(f"Error in response generation: {e}")
         return f"Error: {str(e)}"
 
-# Gradio Interface (Inspired by Quiz App)
+# Gradio Interface with Chatbot
 with gr.Blocks(title="Science Chatbot", theme=gr.themes.Default(primary_hue="cyan", secondary_hue="yellow", neutral_hue="purple")) as chatbot_app:
     gr.HTML("""
     <center>
@@ -93,49 +93,52 @@ with gr.Blocks(title="Science Chatbot", theme=gr.themes.Default(primary_hue="cya
     </center>
     """)
     
+    chatbot = gr.Chatbot(label="Conversation", bubble_full_width=False)
     with gr.Row():
-        query = gr.Textbox(label="Ask a Science Question", placeholder="E.g., What is an ionic bond?")
+        query = gr.Textbox(label="Ask a Science Question", placeholder="E.g., What is an ionic bond?", show_label=False)
         submit_btn = gr.Button("Submit", variant="primary")
     
-    response_output = gr.Textbox(label="Answer", interactive=False)
     cross_encoder = gr.Radio(choices=['(FAST) MiniLM-L6v2', '(ACCURATE) BGE reranker'], value='(ACCURATE) BGE reranker', label="Embeddings")
-    history_state = gr.State(value=[])
     
     def handle_query(user_input, history, cross_encoder_choice):
         if not user_input.strip():
-            return history, "Please enter a valid question.", history
+            return history, ""
+        history = history or []
         response = retrieve_and_generate_response(user_input, cross_encoder_choice, history)
         history.append([user_input, response])
-        return history, response, history[-2:]  # Limit history to last 2 exchanges for context
+        return history, ""
     
     submit_btn.click(
         fn=handle_query,
-        inputs=[query, history_state, cross_encoder],
-        outputs=[history_state, response_output, history_state]
+        inputs=[query, chatbot, cross_encoder],
+        outputs=[chatbot, query]
     )
 
-if __name__ == "__main__":
-    chatbot_app.queue().launch(server_name="0.0.0.0", server_port=7860)# import gradio as gr
+if __name__ == "__main__":# import gradio as gr
 # import logging
 # from sentence_transformers import CrossEncoder
 # from phi.agent import Agent
 # from phi.model.groq import Groq
 # from backend.semantic_search import table, retriever
-# from os import getenv
 # import numpy as np
 # from time import perf_counter
+# import os
 
 # # Logging setup
 # logging.basicConfig(level=logging.INFO)
 # logger = logging.getLogger(__name__)
 
-# # API Key
-# groq_api_key = getenv('GROQ_API_KEY')
+# # API Key setup
+# api_key = os.getenv("GROQ_API_KEY")
+# if not api_key:
+#     logger.error("GROQ_API_KEY not found.")
+#     gr.Warning("GROQ_API_KEY not found. Set it in 'Repository secrets'.")
+# else:
+#     os.environ["GROQ_API_KEY"] = api_key
 
-# # Phi Agent Setup
+# # Initialize Phi Agent
 # def create_phi_agent():
-#     if not groq_api_key:
-#         logger.error("GROQ_API_KEY not found.")
+#     if not api_key:
 #         return None
 #     agent = Agent(
 #         name="Science Education Assistant",
@@ -148,7 +151,7 @@ if __name__ == "__main__":
 #             "Structure responses with headings and bullet points when helpful.",
 #             "Encourage learning and curiosity."
 #         ],
-#         model=Groq(id="llama3-70b-8192", api_key=groq_api_key),
+#         model=Groq(id="llama3-70b-8192", api_key=api_key),
 #         markdown=True
 #     )
 #     return agent
@@ -196,1494 +199,36 @@ if __name__ == "__main__":
 #         logger.error(f"Error in response generation: {e}")
 #         return f"Error: {str(e)}"
 
-# # Gradio Interface
-# with gr.Blocks(theme='gradio/soft') as chatbot_app:
-#     gr.Markdown("# Science Chatbot for 10th Grade Students")
-#     chatbot = gr.Chatbot()
+# # Gradio Interface (Inspired by Quiz App)
+# with gr.Blocks(title="Science Chatbot", theme=gr.themes.Default(primary_hue="cyan", secondary_hue="yellow", neutral_hue="purple")) as chatbot_app:
+#     gr.HTML("""
+#     <center>
+#         <h1><span style="color: purple;">Science Chatbot for 10th Grade Students</span></h1>
+#         <h2>AI-powered Science Tutor</h2>
+#         <i>⚠️ Ask any question from 10th-grade science (physics, chemistry, biology) and get clear, accurate answers! ⚠️</i>
+#     </center>
+#     """)
+    
 #     with gr.Row():
-#         query = gr.Textbox(placeholder="Ask a science question...", label="Question")
-#         submit_btn = gr.Button("Submit")
+#         query = gr.Textbox(label="Ask a Science Question", placeholder="E.g., What is an ionic bond?")
+#         submit_btn = gr.Button("Submit", variant="primary")
+    
+#     response_output = gr.Textbox(label="Answer", interactive=False)
 #     cross_encoder = gr.Radio(choices=['(FAST) MiniLM-L6v2', '(ACCURATE) BGE reranker'], value='(ACCURATE) BGE reranker', label="Embeddings")
+#     history_state = gr.State(value=[])
     
 #     def handle_query(user_input, history, cross_encoder_choice):
 #         if not user_input.strip():
-#             return history
-#         history = history or []
-#         history.append([user_input, None])
-#         response = retrieve_and_generate_response(user_input, cross_encoder_choice, history[:-1])
-#         history[-1][1] = response
-#         return history
+#             return history, "Please enter a valid question.", history
+#         response = retrieve_and_generate_response(user_input, cross_encoder_choice, history)
+#         history.append([user_input, response])
+#         return history, response, history[-2:]  # Limit history to last 2 exchanges for context
     
 #     submit_btn.click(
 #         fn=handle_query,
-#         inputs=[query, chatbot, cross_encoder],
-#         outputs=[chatbot]
+#         inputs=[query, history_state, cross_encoder],
+#         outputs=[history_state, response_output, history_state]
 #     )
 
 # if __name__ == "__main__":
-#     chatbot_app.launch(server_name="0.0.0.0", server_port=7860)# import gradio as gr
-# # import logging
-# # from sentence_transformers import CrossEncoder
-# # from phi.agent import Agent
-# # from phi.model.groq import Groq
-# # from backend.semantic_search import table, retriever
-# # from os import getenv
-# # import requests
-# # import numpy as np
-# # from time import perf_counter
-
-# # # Logging setup
-# # logging.basicConfig(level=logging.INFO)
-# # logger = logging.getLogger(__name__)
-
-# # # API Keys
-# # groq_api_key = getenv('GROQ_API_KEY')
-# # api_key = getenv('API_KEY')
-# # user_id = getenv('USER_ID')
-
-# # # Bhashini Translation
-# # def bhashini_translate(text: str, from_code: str = "en", to_code: str = "hi") -> dict:
-# #     if not text.strip():
-# #         return {"status_code": 400, "message": "Input text is empty", "translated_content": text}
-    
-# #     try:
-# #         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"}
-# #         }
-        
-# #         response = requests.post(url, json=payload, headers=headers)
-# #         if response.status_code != 200:
-# #             return {"status_code": response.status_code, "message": "Error in translation request", "translated_content": text}
-
-# #         response_data = response.json()
-# #         service_id = response_data["pipelineResponseConfig"][0]["config"][0]["serviceId"]
-# #         callback_url = response_data["pipelineInferenceAPIEndPoint"]["callbackUrl"]
-        
-# #         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}]}
-# #         }
-        
-# #         compute_response = requests.post(callback_url, json=compute_payload, headers=headers2)
-# #         if compute_response.status_code != 200:
-# #             return {"status_code": compute_response.status_code, "message": "Error in translation", "translated_content": text}
-        
-# #         translated_content = compute_response.json()["pipelineResponse"][0]["output"][0]["target"]
-# #         return {"status_code": 200, "message": "Translation successful", "translated_content": translated_content}
-    
-# #     except Exception as e:
-# #         return {"status_code": 500, "message": f"Translation error: {e}", "translated_content": text}
-
-# # # Phi Agent Setup
-# # def create_phi_agent():
-# #     if not groq_api_key:
-# #         return None
-# #     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 AnnoDomini", api_key=groq_api_key),
-# #         markdown=True
-# #     )
-# #     return agent
-
-# # phi_agent = create_phi_agent()
-
-# # # Response Generation
-# # def retrieve_and_generate_response(query, cross_encoder_choice, history=None):
-# #     top_rerank = 25
-# #     top_k_rank = 20
-    
-# #     if not query:
-# #         return "Please provide a valid question."
-    
-# #     try:
-# #         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]
-        
-# #         cross_encoder1 = 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_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]]
-        
-# #         context = "\n\n".join(documents[:10]) if documents else ""
-# #         context = f"Context information from educational materials:\n{context}\n\n"
-        
-# #         history_context = ""
-# #         if history:
-# #             for user_msg, bot_msg in history[-2:]:
-# #                 if user_msg and bot_msg:
-# #                     history_context += f"Previous Q: {user_msg}\nPrevious A: {bot_msg}\n\n"
-        
-# #         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."
-        
-# #         if not phi_agent:
-# #             return "Chatbot not configured properly."
-# #         response = phi_agent.run(full_prompt)
-# #         return response.content if hasattr(response, 'content') else str(response)
-    
-# #     except Exception as e:
-# #         return f"Error: {str(e)}"
-
-# # # Gradio Interface
-# # with gr.Blocks(theme='gradio/soft') as CHATBOT:
-# #     gr.Markdown("# Science Chatbot for 10th Grade Students")
-# #     chatbot = gr.Chatbot()
-# #     with gr.Row():
-# #         query = gr.Textbox(placeholder="Ask a science question...", label="Question")
-# #         submit_btn = gr.Button("Submit")
-# #     cross_encoder = gr.Radio(choices=['(FAST) MiniLM-L6v2', '(ACCURATE) BGE reranker'], value='(ACCURATE) BGE reranker', label="Embeddings")
-# #     language = gr.Dropdown(choices=["Hindi", "Tamil", "Telugu", "Kannada", "Marathi", "Gujarati", "Bengali", "Odia", "Punjabi", "Malayalam", "Assamese", "Urdu"], value="Hindi", label="Translation Language")
-# #     translated_output = gr.Textbox(label="Translated Response")
-    
-# #     def handle_query(user_input, history, cross_encoder_choice):
-# #         if not user_input.strip():
-# #             return history, ""
-# #         history = history or []
-# #         history.append([user_input, None])
-# #         response = retrieve_and_generate_response(user_input, cross_encoder_choice, history[:-1])
-# #         history[-1][1] = response
-# #         translated = bhashini_translate(response, to_code={"Hindi": "hi", "Tamil": "ta", "Telugu": "te", "Kannada": "kn", "Marathi": "mr", "Gujarati": "gu", "Bengali": "bn", "Odia": "or", "Punjabi": "pa", "Malayalam": "ml", "Assamese": "as", "Urdu": "ur"}.get(language, "hi"))
-# #         return history, translated["translated_content"]
-    
-# #     submit_btn.click(
-# #         fn=handle_query,
-# #         inputs=[query, chatbot, cross_encoder, language],
-# #         outputs=[chatbot, translated_output]
-# #     )
-
-# # if __name__ == "__main__":
-# #     CHATBOT.launch(server_name="0.0.0.0", server_port=7860)# import requests
-# # # import gradio as gr
-# # # import logging
-# # # from pathlib import Path
-# # # from time import perf_counter
-# # # from sentence_transformers import CrossEncoder
-# # # from jinja2 import Environment, FileSystemLoader
-# # # import numpy as np
-# # # from os import getenv
-
-# # # # Phi Agent imports
-# # # from phi.agent import Agent, RunResponse
-# # # from phi.model.groq import Groq
-
-# # # from backend.semantic_search import table, retriever
-
-# # # # API Keys
-# # # api_key = getenv('API_KEY')
-# # # user_id = getenv('USER_ID')
-# # # groq_api_key = getenv('GROQ_API_KEY')
-
-# # # # Check for required API keys
-# # # if not groq_api_key:
-# # #     gr.Warning("GROQ_API_KEY not found. Set it in 'Repository secrets'.")
-# # #     logging.error("GROQ_API_KEY not found.")
-
-# # # 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": text}
-    
-# # #     print(f'Input text - {text}')
-# # #     print(f'Starting translation process from {from_code} to {to_code}...')
-# # #     gr.Warning(f'Translating to {to_code}...')
-    
-# # #     try:
-# # #         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": text}
-
-# # #         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": text}
-        
-# # #         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}
-    
-# # #     except Exception as e:
-# # #         print(f'Translation error: {e}')
-# # #         return {"status_code": 500, "message": f"Translation error: {e}", "translated_content": text}
-
-# # # # Initialize Phi Agent
-# # # def create_phi_agent():
-# # #     """Create and return a Phi Agent for science education chatbot"""
-# # #     if not groq_api_key:
-# # #         return None
-        
-# # #     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.",
-# # #             "When answering, structure your response with clear headings and bullet points when helpful.",
-# # #             "Always encourage learning and curiosity.",
-# # #             "If you're not sure about something, acknowledge it honestly."
-# # #         ],
-# # #         model=Groq(id="llama3-groq-70b-8192-tool-use-preview", api_key=groq_api_key),
-# # #         markdown=True,
-# # #         show_tool_calls=False,
-# # #         debug_mode=False
-# # #     )
-# # #     return agent
-
-# # # # Create the agent instance
-# # # phi_agent = create_phi_agent()
-
-# # # def generate_phi_response(prompt: str, history: list = None) -> str:
-# # #     """Generate response using Phi Agent"""
-# # #     if not phi_agent:
-# # #         return "Sorry, the chatbot is not properly configured. Please check the API key settings."
-        
-# # #     try:
-# # #         # Build context from history if available
-# # #         context = ""
-# # #         if history:
-# # #             for user_msg, bot_msg in history[-3:]:  # Last 3 exchanges for context
-# # #                 if user_msg and bot_msg:
-# # #                     context += f"Previous Q: {user_msg}\nPrevious A: {bot_msg}\n\n"
-        
-# # #         # Combine context with current prompt
-# # #         full_prompt = f"{context}Current Question: {prompt}"
-        
-# # #         # Run the agent
-# # #         response: RunResponse = phi_agent.run(full_prompt)
-        
-# # #         # Extract the content from the response
-# # #         if hasattr(response, 'content') and response.content:
-# # #             return response.content
-# # #         elif hasattr(response, 'messages') and response.messages:
-# # #             # Get the last message content
-# # #             last_message = response.messages[-1]
-# # #             if hasattr(last_message, 'content'):
-# # #                 return last_message.content
-        
-# # #         return "I apologize, but I couldn't generate a proper response. Please try again."
-        
-# # #     except Exception as e:
-# # #         print(f"Error in Phi Agent: {e}")
-# # #         return f"I encountered an error while processing your question: {str(e)}. Please try again."
-
-# # # # Constants
-# # # VECTOR_COLUMN_NAME = "vector"
-# # # TEXT_COLUMN_NAME = "text"
-# # # proj_dir = Path(__file__).parent
-
-# # # logging.basicConfig(level=logging.INFO)
-# # # logger = logging.getLogger(__name__)
-
-# # # # Setup Jinja2 templates
-# # # env = Environment(loader=FileSystemLoader(proj_dir / 'templates'))
-# # # try:
-# # #     template = env.get_template('template.j2')
-# # #     template_html = env.get_template('template_html.j2')
-# # # except:
-# # #     # Fallback if templates don't exist
-# # #     template = None
-# # #     template_html = None
-
-# # # def retrieve_and_generate_response(query, cross_encoder, history=None):
-# # #     """Retrieve documents and generate response"""
-# # #     top_rerank = 25
-# # #     top_k_rank = 20
-    
-# # #     if not query:
-# # #         return "Please provide a valid question."
-    
-# # #     logger.warning('Retrieving documents...')
-    
-# # #     try:
-# # #         document_start = perf_counter()
-
-# # #         query_vec = retriever.encode(query)
-# # #         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')
-# # #         else:
-# # #             cross_encoder1 = CrossEncoder('BAAI/bge-reranker-base')  # Default
-        
-# # #         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
-# # #         print(f"Document retrieval took {document_time:.2f} seconds")
-
-# # #         # Create context from documents
-# # #         context = ""
-# # #         if documents:
-# # #             context = "\n\n".join(documents[:10])  # Use top 10 documents
-# # #             context = f"Context information from educational materials:\n{context}\n\n"
-        
-# # #         # Build conversation history
-# # #         history_context = ""
-# # #         if history:
-# # #             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\n"
-        
-# # #         # Create enhanced prompt
-# # #         full_prompt = f"""
-# # #         {history_context}
-# # #         {context}
-# # #         Question: {query}
-        
-# # #         Please 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 using Phi Agent
-# # #         output = generate_phi_response(full_prompt, history)
-        
-# # #         print('Output:', output)
-        
-# # #         return output
-        
-# # #     except Exception as e:
-# # #         print(f"Error in retrieve_and_generate_response: {e}")
-# # #         return f"Sorry, I encountered an error: {str(e)}"
-
-# # # def translate_response(selected_language, response_text):
-# # #     """Translate the response to selected language"""
-# # #     if not response_text or not selected_language:
-# # #         return response_text
-        
-# # #     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.get(selected_language, "hi")
-# # #     translation = bhashini_translate(response_text, to_code=to_code)
-# # #     return translation.get('translated_content', response_text)
-
-# # # # Simplified Gradio interface
-# # # with gr.Blocks(theme='gradio/soft') as CHATBOT:
-# # #     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 10 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 Phi Agent & Groq 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:
-# # #                 pass  # Skip if logo not found
-
-# # #     chatbot = gr.Chatbot(
-# # #         [],
-# # #         elem_id="chatbot",
-# # #         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'], 
-# # #         value='(ACCURATE) BGE reranker', 
-# # #         label="Embeddings"
-# # #     )
-    
-# # #     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")
-    
-# # #     def user_message(user_input, history):
-# # #         """Handle user input and add to history"""
-# # #         if not user_input.strip():
-# # #             return "", history
-# # #         return "", history + [[user_input, None]]
-    
-# # #     def bot_response(history, cross_encoder_choice):
-# # #         """Generate bot response"""
-# # #         if not history:
-# # #             return history
-            
-# # #         user_input = history[-1][0]
-        
-# # #         # Generate response
-# # #         response = retrieve_and_generate_response(user_input, cross_encoder_choice, history[:-1])
-        
-# # #         # Update history
-# # #         history[-1][1] = response
-# # #         return history
-    
-# # #     def translate_current_response(history, selected_language):
-# # #         """Translate the current response"""
-# # #         if not history or not history[-1][1]:
-# # #             return ""
-        
-# # #         response_text = history[-1][1]
-# # #         translated_text = translate_response(selected_language, response_text)
-# # #         return translated_text
-    
-# # #     # Event handlers
-# # #     txt_msg = txt.submit(user_message, [txt, chatbot], [txt, chatbot]).then(
-# # #         bot_response, [chatbot, cross_encoder], chatbot
-# # #     ).then(
-# # #         translate_current_response, [chatbot, language_dropdown], translated_textbox
-# # #     )
-    
-# # #     txt_btn.click(user_message, [txt, chatbot], [txt, chatbot]).then(
-# # #         bot_response, [chatbot, cross_encoder], chatbot
-# # #     ).then(
-# # #         translate_current_response, [chatbot, language_dropdown], 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
-# # # if __name__ == "__main__":
-# # #     CHATBOT.queue().launch(server_name="0.0.0.0", server_port=7860)
-# # # # import gradio as gr
-# # # # # from ragatouille import RAGPretrainedModel  # COMMENTED OUT
-# # # # 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
-
-# # # # # Phi Agent imports
-# # # # from phi.agent import Agent, RunResponse
-# # # # from phi.model.groq import Groq
-# # # # from phi.utils.pprint import pprint_run_response
-
-# # # # #from backend.query_llm import generate_hf
-# # # # from backend.semantic_search import table, retriever
-
-# # # # # Bhashini API translation function
-# # # # api_key = getenv('API_KEY')
-# # # # user_id = getenv('USER_ID')
-# # # # groq_api_key = getenv('GROQ_API_KEY')  # Add GROQ API key
-
-# # # # 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}
-
-# # # # # Initialize Phi Agent
-# # # # def create_phi_agent():
-# # # #     """Create and return a Phi Agent for science education chatbot"""
-# # # #     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.",
-# # # #             "When answering, structure your response with clear headings and bullet points when helpful.",
-# # # #             "Always encourage learning and curiosity.",
-# # # #             "If you're not sure about something, acknowledge it honestly."
-# # # #         ],
-# # # #         model=Groq(id="llama3-groq-70b-8192-tool-use-preview", api_key=groq_api_key),
-# # # #         markdown=True,
-# # # #         show_tool_calls=False,
-# # # #         debug_mode=False
-# # # #     )
-# # # #     return agent
-
-# # # # # Create the agent instance
-# # # # phi_agent = create_phi_agent()
-
-# # # # def generate_phi_response(prompt: str, history: list = None) -> str:
-# # # #     """Generate response using Phi Agent"""
-# # # #     try:
-# # # #         # Build context from history if available
-# # # #         context = ""
-# # # #         if history:
-# # # #             for user_msg, bot_msg in history[-3:]:  # Last 3 exchanges for context
-# # # #                 if user_msg and bot_msg:
-# # # #                     context += f"Previous Q: {user_msg}\nPrevious A: {bot_msg}\n\n"
-        
-# # # #         # Combine context with current prompt
-# # # #         full_prompt = f"{context}Current Question: {prompt}"
-        
-# # # #         # Run the agent
-# # # #         response: RunResponse = phi_agent.run(full_prompt)
-        
-# # # #         # Extract the content from the response
-# # # #         if hasattr(response, 'content') and response.content:
-# # # #             return response.content
-# # # #         elif hasattr(response, 'messages') and response.messages:
-# # # #             # Get the last message content
-# # # #             last_message = response.messages[-1]
-# # # #             if hasattr(last_message, 'content'):
-# # # #                 return last_message.content
-        
-# # # #         return "I apologize, but I couldn't generate a proper response. Please try again."
-        
-# # # #     except Exception as e:
-# # # #         print(f"Error in Phi Agent: {e}")
-# # # #         return f"I encountered an error while processing your question: {str(e)}. Please try again."
-
-# # # # # 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 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...')
-    
-# # # #     # COMMENTED OUT RAGatouille section
-# # # #     # 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:
-    
-# # # #     # Handle ColBERT case differently for now
-# # # #     if cross_encoder == '(HIGH ACCURATE) ColBERT':
-# # # #         gr.Warning('ColBERT is temporarily disabled. Using BGE reranker instead.')
-# # # #         cross_encoder = '(ACCURATE) BGE reranker'
-    
-# # # #     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)
-
-# # # #     # REPLACED: Use Phi Agent instead of generate_qwen
-# # # #     print("Using Phi Agent for response generation...")
-# # # #     output = generate_phi_response(prompt, history[:-1])
-    
-# # # #     print('Output:',output)
-    
-# # # #     # Update history with the response
-# # # #     history_list = list(history[-1])
-# # # #     history_list[1] = output
-# # # #     history[-1] = tuple(history_list)
-
-# # # #     yield history, prompt_html
-
-# # # # 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 - SIMPLIFIED to avoid schema issues
-# # # # 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>Welcome! I am your friend!</h1>Ask me !I will help you<h1><span style="color: #008000">I AM A CHATBOT FOR 10 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 Phi Agent & Groq 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:
-# # # #                 pass  # Skip if logo not found
-
-# # # #     chatbot = gr.Chatbot(
-# # # #         [],
-# # # #         elem_id="chatbot",
-# # # #         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)
-    
-# # # #     # SIMPLIFIED: Remove ColBERT option temporarily
-# # # #     cross_encoder = gr.Radio(
-# # # #         choices=['(FAST) MiniLM-L6v2', '(ACCURATE) BGE reranker'], 
-# # # #         value='(ACCURATE) BGE reranker', 
-# # # #         label="Embeddings"
-# # # #     )
-    
-# # # #     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, ""))
-        
-# # # #         # Call bot function
-# # # #         bot_output = next(bot(history, cross_encoder))
-# # # #         print('bot_output',bot_output)
-# # # #         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, gr.Textbox(value="", interactive=True)  # Clear input
-
-# # # #     txt_msg = txt_btn.click(update_history_and_translate, [txt, cross_encoder, history_state, language_dropdown], [chatbot, prompt_html, translated_textbox, txt])
-# # # #     txt_msg = txt.submit(update_history_and_translate, [txt, cross_encoder, history_state, language_dropdown], [chatbot, prompt_html, translated_textbox, txt])
-
-# # # #     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
-# # # # if __name__ == "__main__":
-# # # #     CHATBOT.queue().launch(server_name="0.0.0.0", server_port=7860)# 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
-
-# # # # # Phi Agent imports
-# # # # from phi.agent import Agent, RunResponse
-# # # # from phi.model.groq import Groq
-# # # # from phi.utils.pprint import pprint_run_response
-
-# # # # #from backend.query_llm import generate_hf
-# # # # from backend.semantic_search import table, retriever
-
-# # # # # Bhashini API translation function
-# # # # api_key = getenv('API_KEY')
-# # # # user_id = getenv('USER_ID')
-# # # # groq_api_key = getenv('GROQ_API_KEY')  # Add GROQ API key
-
-# # # # 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}
-
-# # # # # Initialize Phi Agent
-# # # # def create_phi_agent():
-# # # #     """Create and return a Phi Agent for science education chatbot"""
-# # # #     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.",
-# # # #             "When answering, structure your response with clear headings and bullet points when helpful.",
-# # # #             "Always encourage learning and curiosity.",
-# # # #             "If you're not sure about something, acknowledge it honestly."
-# # # #         ],
-# # # #         model=Groq(id="llama3-groq-70b-8192-tool-use-preview", api_key=groq_api_key),
-# # # #         markdown=True,
-# # # #         show_tool_calls=False,
-# # # #         debug_mode=False
-# # # #     )
-# # # #     return agent
-
-# # # # # Create the agent instance
-# # # # phi_agent = create_phi_agent()
-
-# # # # def generate_phi_response(prompt: str, history: list = None) -> str:
-# # # #     """Generate response using Phi Agent"""
-# # # #     try:
-# # # #         # Build context from history if available
-# # # #         context = ""
-# # # #         if history:
-# # # #             for user_msg, bot_msg in history[-3:]:  # Last 3 exchanges for context
-# # # #                 if user_msg and bot_msg:
-# # # #                     context += f"Previous Q: {user_msg}\nPrevious A: {bot_msg}\n\n"
-        
-# # # #         # Combine context with current prompt
-# # # #         full_prompt = f"{context}Current Question: {prompt}"
-        
-# # # #         # Run the agent
-# # # #         response: RunResponse = phi_agent.run(full_prompt)
-        
-# # # #         # Extract the content from the response
-# # # #         if hasattr(response, 'content') and response.content:
-# # # #             return response.content
-# # # #         elif hasattr(response, 'messages') and response.messages:
-# # # #             # Get the last message content
-# # # #             last_message = response.messages[-1]
-# # # #             if hasattr(last_message, 'content'):
-# # # #                 return last_message.content
-        
-# # # #         return "I apologize, but I couldn't generate a proper response. Please try again."
-        
-# # # #     except Exception as e:
-# # # #         print(f"Error in Phi Agent: {e}")
-# # # #         return f"I encountered an error while processing your question: {str(e)}. Please try again."
-
-# # # # # 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 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)
-    
-# # # #         # REPLACED: Use Phi Agent instead of generate_qwen
-# # # #         print("Using Phi Agent for response generation...")
-# # # #         output = generate_phi_response(prompt, history[:-1])
-        
-# # # #         print('Output:',output)
-        
-# # # #         # Update history with the response
-# # # #         history_list = list(history[-1])
-# # # #         history_list[1] = output
-# # # #         history[-1] = tuple(history_list)
-
-# # # #         yield history, prompt_html
-
-# # # # 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>Welcome! I am your friend!</h1>Ask me !I will help you<h1><span style="color: #008000">I AM A CHATBOT FOR 10 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 Phi Agent & Groq 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):
-# # # #             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, ""))
-        
-# # # #         # Call bot function
-# # # #         bot_output = next(bot(history, cross_encoder))
-# # # #         print('bot_output',bot_output)
-# # # #         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, gr.Textbox(value="", interactive=True)  # Clear input
-
-# # # #     txt_msg = txt_btn.click(update_history_and_translate, [txt, cross_encoder, history_state, language_dropdown], [chatbot, prompt_html, translated_textbox, txt])
-# # # #     txt_msg = txt.submit(update_history_and_translate, [txt, cross_encoder, history_state, language_dropdown], [chatbot, prompt_html, translated_textbox, txt])
-
-# # # #     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
-# # # # if __name__ == "__main__":
-# # # #     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>Welcome! I am your friend!</h1>Ask me !I will help you<h1><span style="color: #008000">I AM A CHATBOT FOR 10 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):
-# # # #             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)
-
+#     chatbot_app.queue().launch(server_name="0.0.0.0", server_port=7860)# import gradio as gr