Update app.py

app.py

@@ -1,41 +1,54 @@
-import requests
 import gradio as gr
-from ragatouille import RAGPretrainedModel
+from phi.agent import Agent
+from phi.model.groq import Groq
+import os
 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
+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 translation function
-api_key = getenv('API_KEY')
-user_id = getenv('USER_ID')
+# 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, "speech_content": None}
+        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}...')
+        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": user_id,
-        "ulcaApiKey": api_key
+        "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"}
@@ -45,11 +58,16 @@ def bhashini_translate(text: str, from_code: str = "en", to_code: str = "hi") ->
     response = requests.post(url, json=payload, headers=headers)
     
     if response.status_code != 200:
-        print(f'Error in initial request: {response.status_code}')
+        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"]
     
@@ -68,7 +86,7 @@ def bhashini_translate(text: str, from_code: str = "en", to_code: str = "hi") ->
     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}')
+        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...')
@@ -78,157 +96,123 @@ def bhashini_translate(text: str, from_code: str = "en", to_code: str = "hi") ->
     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
+)
 
-# Existing chatbot functions
-VECTOR_COLUMN_NAME = "vector"
-TEXT_COLUMN_NAME = "text"
-HF_TOKEN = getenv("HUGGING_FACE_HUB_TOKEN")
+# Set up Jinja2 environment
 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')  # For document context
+template_html = env.get_template('template_html.j2')  # For HTML output
 
-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):
-
+# 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
-    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()
+    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)
-        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')
+        documents = table.search(query_vec, vector_column_name="vector").limit(top_rerank).to_list()
+        documents = [doc["text"] for doc in documents]
         
-        cross_scores = cross_encoder1.predict(query_doc_pair)
+        # 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]]
-    
-        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
+        # 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"
         
-        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
+        # 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 translate_text(response_text, selected_language):
+def simple_chat_function(message, history, cross_encoder_choice):
+    """Chat function with semantic search and retriever integration"""
+    if not message.strip():
+        return "", history, ""
     
-def translate_text(selected_language,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"
+        "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)
+    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['translated_content']
-   
+    return translation.get('translated_content', 'Translation failed.')
 
-# Gradio interface
-with gr.Blocks(theme='gradio/soft') as CHATBOT:
-    history_state = gr.State([])
+# 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 10 SOCIAL WITH TRANSLATION IN 22 LANGUAGES</span></h1></div>""")
+            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:[email protected]" style="color: #00008B; font-style: italic;">[email protected]</a>.</p>""")
-
         with gr.Column(scale=3):
-            gr.Image(value='logo.png', height=200, width=200)
+            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",
@@ -240,56 +224,345 @@ with gr.Blocks(theme='gradio/soft') as CHATBOT:
     )
 
     with gr.Row():
-        txt = gr.Textbox(
+        msg = 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)")
+        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",  # default to Hindi
+        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}...')
     
-    prompt_html = gr.HTML()
+#     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"}
+#     }
     
-    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)
+#     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()
         
-        # 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
+#         # 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]
         
-        # Translate text
-        translated_text = translate_text(language_dropdown, history)
-        return history, prompt_html, translated_text
+#         # 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)}"
 
-    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])
+# 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:[email protected]" style="color: #00008B; font-style: italic;">[email protected]</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>")
 
-    examples = ['WHAT IS POWER SHARING?','WHAT IS THE REASON FOR RISE OF NATIONALISM IN INDIA?']            
+#     # 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
 
-    gr.Examples(examples, txt)
+#     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])
 
-# Launch the Gradio application
-CHATBOT.launch(share=True,debug=True)
+#     # 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