Update app.py

app.py

@@ -1,274 +1,4 @@
-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
-
-# Set up logging
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-# API Key setup
-api_key = os.getenv("GROQ_API_KEY")
-if not api_key:
-    gr.Warning("GROQ_API_KEY not found. Set it in 'Repository secrets'.")
-    logger.error("GROQ_API_KEY not found.")
-    api_key = ""  # Fallback to empty string, but this will fail without a key
-else:
-    os.environ["GROQ_API_KEY"] = api_key
-
-# Bhashini API setup
-bhashini_api_key = os.getenv("API_KEY")
-bhashini_user_id = os.getenv("USER_ID")
-
-def bhashini_translate(text: str, from_code: str = "en", to_code: str = "hi") -> dict:
-    """Translates text from source language to target language using the Bhashini API."""
-    if not text.strip():
-        print('Input text is empty. Please provide valid text for translation.')
-        return {"status_code": 400, "message": "Input text is empty", "translated_content": None}
-    else:
-        print('Input text - ', text)
-    print(f'Starting translation process from {from_code} to {to_code}...')
-    gr.Warning(f'Translating to {to_code}...')
-    
-    url = 'https://meity-auth.ulcacontrib.org/ulca/apis/v0/model/getModelsPipeline'
-    headers = {
-        "Content-Type": "application/json",
-        "userID": bhashini_user_id,
-        "ulcaApiKey": bhashini_api_key
-    }
-    payload = {
-        "pipelineTasks": [{"taskType": "translation", "config": {"language": {"sourceLanguage": from_code, "targetLanguage": to_code}}}],
-        "pipelineRequestConfig": {"pipelineId": "64392f96daac500b55c543cd"}
-    }
-    
-    print('Sending initial request to get the pipeline...')
-    response = requests.post(url, json=payload, headers=headers)
-    
-    if response.status_code != 200:
-        print(f'Error in initial request: {response.status_code}, Response: {response.text}')
-        return {"status_code": response.status_code, "message": "Error in translation request", "translated_content": None}
-
-    print('Initial request successful, processing response...')
-    response_data = response.json()
-    print('Full response data:', response_data)  # Debug the full response
-    if "pipelineInferenceAPIEndPoint" not in response_data or "callbackUrl" not in response_data["pipelineInferenceAPIEndPoint"]:
-        print('Unexpected response structure:', response_data)
-        return {"status_code": 400, "message": "Unexpected API response structure", "translated_content": None}
-
-    service_id = response_data["pipelineResponseConfig"][0]["config"][0]["serviceId"]
-    callback_url = response_data["pipelineInferenceAPIEndPoint"]["callbackUrl"]
-    
-    print(f'Service ID: {service_id}, Callback URL: {callback_url}')
-    
-    headers2 = {
-        "Content-Type": "application/json",
-        response_data["pipelineInferenceAPIEndPoint"]["inferenceApiKey"]["name"]: response_data["pipelineInferenceAPIEndPoint"]["inferenceApiKey"]["value"]
-    }
-    compute_payload = {
-        "pipelineTasks": [{"taskType": "translation", "config": {"language": {"sourceLanguage": from_code, "targetLanguage": to_code}, "serviceId": service_id}}],
-        "inputData": {"input": [{"source": text}], "audio": [{"audioContent": None}]}
-    }
-    
-    print(f'Sending translation request with text: "{text}"')
-    compute_response = requests.post(callback_url, json=compute_payload, headers=headers2)
-    
-    if compute_response.status_code != 200:
-        print(f'Error in translation request: {compute_response.status_code}, Response: {compute_response.text}')
-        return {"status_code": compute_response.status_code, "message": "Error in translation", "translated_content": None}
-    
-    print('Translation request successful, processing translation...')
-    compute_response_data = compute_response.json()
-    translated_content = compute_response_data["pipelineResponse"][0]["output"][0]["target"]
-    
-    print(f'Translation successful. Translated content: "{translated_content}"')
-    return {"status_code": 200, "message": "Translation successful", "translated_content": translated_content}
-
-# Initialize PhiData Agent
-agent = Agent(
-    name="Science Education Assistant",
-    role="You are a helpful science tutor for 10th-grade students",
-    instructions=[
-        "You are an expert science teacher specializing in 10th-grade curriculum.",
-        "Provide clear, accurate, and age-appropriate explanations.",
-        "Use simple language and examples that students can understand.",
-        "Focus on concepts from physics, chemistry, and biology.",
-        "Structure responses with headings and bullet points when helpful.",
-        "Encourage learning and curiosity."
-    ],
-    model=Groq(id="llama3-70b-8192", api_key=api_key),
-    markdown=True
-)
-
-# Response Generation Function
-def retrieve_and_generate_response(query, cross_encoder_choice, history=None):
-    """Generate response using semantic search and LLM"""
-    top_rerank = 25
-    top_k_rank = 20
-    
-    if not query.strip():
-        return "Please provide a valid question."
-    
-    try:
-        start_time = perf_counter()
-        
-        # Encode query and search documents
-        query_vec = retriever.encode(query)
-        documents = table.search(query_vec, vector_column_name="vector").limit(top_rerank).to_list()
-        documents = [doc["text"] for doc in documents]
-        
-        # Re-rank documents using cross-encoder
-        cross_encoder_model = CrossEncoder('BAAI/bge-reranker-base') if cross_encoder_choice == '(ACCURATE) BGE reranker' else CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')
-        query_doc_pair = [[query, doc] for doc in documents]
-        cross_scores = cross_encoder_model.predict(query_doc_pair)
-        sim_scores_argsort = list(reversed(np.argsort(cross_scores)))
-        documents = [documents[idx] for idx in sim_scores_argsort[:top_k_rank]]
-        
-        # Create context from top documents
-        context = "\n\n".join(documents[:10]) if documents else ""
-        context = f"Context information from educational materials:\n{context}\n\n"
-        
-        # Add conversation history for context
-        history_context = ""
-        if history and len(history) > 0:
-            for user_msg, bot_msg in history[-2:]:  # Last 2 exchanges
-                if user_msg and bot_msg:
-                    history_context += f"Previous Q: {user_msg}\nPrevious A: {bot_msg}\n"
-        
-        # Create full prompt
-        full_prompt = f"{history_context}{context}Question: {query}\n\nPlease answer the question using the context provided above. If the context doesn't contain relevant information, use your general knowledge about 10th-grade science topics."
-        
-        # Generate response
-        response = agent.run(full_prompt)
-        response_text = response.content if hasattr(response, 'content') else str(response)
-        
-        logger.info(f"Response generation took {perf_counter() - start_time:.2f} seconds")
-        return response_text
-    
-    except Exception as e:
-        logger.error(f"Error in response generation: {e}")
-        return f"Error generating response: {str(e)}"
-
-def simple_chat_function(message, history, cross_encoder_choice):
-    """Chat function with semantic search and retriever integration"""
-    if not message.strip():
-        return "", history
-    
-    # Generate response using the semantic search function
-    response = retrieve_and_generate_response(message, cross_encoder_choice, history)
-    
-    # Add to history
-    history.append([message, response])
-    
-    return "", history
-
-def translate_text(selected_language, history):
-    """Translate the last response in history to the selected language."""
-    iso_language_codes = {
-        "Hindi": "hi", "Gom": "gom", "Kannada": "kn", "Dogri": "doi", "Bodo": "brx", "Urdu": "ur",
-        "Tamil": "ta", "Kashmiri": "ks", "Assamese": "as", "Bengali": "bn", "Marathi": "mr",
-        "Sindhi": "sd", "Maithili": "mai", "Punjabi": "pa", "Malayalam": "ml", "Manipuri": "mni",
-        "Telugu": "te", "Sanskrit": "sa", "Nepali": "ne", "Santali": "sat", "Gujarati": "gu", "Odia": "or"
-    }
-    
-    to_code = iso_language_codes[selected_language]
-    response_text = history[-1][1] if history and history[-1][1] else ''
-    print('response_text for translation', response_text)
-    translation = bhashini_translate(response_text, to_code=to_code)
-    return translation.get('translated_content', 'Translation failed.')
-
-# Gradio Interface with layout template
-with gr.Blocks(title="Science Chatbot", theme='gradio/soft') as demo:
-    # Header section
-    with gr.Row():
-        with gr.Column(scale=10):
-            gr.HTML(value="""<div style="color: #FF4500;"><h1>Welcome! I am your friend!</h1>Ask me !I will help you<h1><span style="color: #008000">I AM A CHATBOT FOR 10TH SCIENCE WITH TRANSLATION IN 22 LANGUAGES</span></h1></div>""")
-            gr.HTML(value=f"""<p style="font-family: sans-serif; font-size: 16px;">A free chat bot developed by K.M.RAMYASRI,TGT,GHS.SUTHUKENY using Open source LLMs for 10 std students</p>""")
-            gr.HTML(value=f"""<p style="font-family: Arial, sans-serif; font-size: 14px;"> Suggestions may be sent to <a href="mailto:[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>")
-
-    # Chat and input components
-    chatbot = gr.Chatbot(
-        [],
-        elem_id="chatbot",
-        avatar_images=('https://aui.atlassian.com/aui/8.8/docs/images/avatar-person.svg',
-                       'https://huggingface.co/datasets/huggingface/brand-assets/resolve/main/hf-logo.svg'),
-        bubble_full_width=False,
-        show_copy_button=True,
-        show_share_button=True,
-    )
-
-    with gr.Row():
-        msg = gr.Textbox(
-            scale=3,
-            show_label=False,
-            placeholder="Enter text and press enter",
-            container=False,
-        )
-        submit_btn = gr.Button(value="Submit text", scale=1, variant="primary")
-
-    # Additional controls
-    cross_encoder = gr.Radio(
-        choices=['(FAST) MiniLM-L6v2', '(ACCURATE) BGE reranker'],
-        value='(ACCURATE) BGE reranker',
-        label="Embeddings Model",
-        info="Select the model for document ranking"
-    )
-    language_dropdown = gr.Dropdown(
-        choices=[
-            "Hindi", "Gom", "Kannada", "Dogri", "Bodo", "Urdu", "Tamil", "Kashmiri", "Assamese", "Bengali", "Marathi",
-            "Sindhi", "Maithili", "Punjabi", "Malayalam", "Manipuri", "Telugu", "Sanskrit", "Nepali", "Santali",
-            "Gujarati", "Odia"
-        ],
-        value="Hindi",
-        label="Select Language for Translation"
-    )
-    translated_textbox = gr.Textbox(label="Translated Response")
-
-    # Event handlers
-    def update_chat_and_translate(message, history, cross_encoder_choice, selected_language):
-        if not message.strip():
-            return "", history, ""
-        
-        # Generate response
-        response = retrieve_and_generate_response(message, cross_encoder_choice, history)
-        history.append([message, response])
-        
-        # Translate response
-        translated_text = translate_text(selected_language, history)
-        
-        return "", history, translated_text
-
-    msg.submit(update_chat_and_translate, [msg, chatbot, cross_encoder, language_dropdown], [msg, chatbot, translated_textbox])
-    submit_btn.click(update_chat_and_translate, [msg, chatbot, cross_encoder, language_dropdown], [msg, chatbot, translated_textbox])
-
-    clear = gr.Button("Clear Conversation")
-    clear.click(lambda: ([], "", ""), outputs=[chatbot, msg, translated_textbox])
-
-    # Example questions
-    gr.Examples(
-        examples=[
-            'What is the difference between metals and non-metals?',
-            'What is an ionic bond?',
-            'Explain asexual reproduction',
-            'What is photosynthesis?',
-            'Explain Newton\'s laws of motion'
-        ],
-        inputs=msg,
-        label="Try these example questions:"
-    )
-
-if __name__ == "__main__":
-    demo.launch(server_name="0.0.0.0", server_port=7860)# import gradio as gr
+# import gradio as gr
 # from phi.agent import Agent
 # from phi.model.groq import Groq
 # import os
@@ -278,6 +8,7 @@ if __name__ == "__main__":
 # import numpy as np
 # from time import perf_counter
 # import requests
+# from jinja2 import Environment, FileSystemLoader
 
 # # Set up logging
 # logging.basicConfig(level=logging.INFO)
@@ -321,11 +52,16 @@ if __name__ == "__main__":
 #     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)  # 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"]
     
@@ -344,7 +80,7 @@ if __name__ == "__main__":
 #     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...')
@@ -369,6 +105,12 @@ if __name__ == "__main__":
 #     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):
@@ -534,213 +276,3 @@ if __name__ == "__main__":
 
 # 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
-
-# # 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
-
-# # 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
-# )
-
-# # Response Generation Function
-# def retrieve_and_generate_response(query, cross_encoder_choice, history=None):
-#     """Generate response using semantic search and LLM"""
-#     top_rerank = 25
-#     top_k_rank = 20
-    
-#     if not query.strip():
-#         return "Please provide a valid question."
-    
-#     try:
-#         start_time = perf_counter()
-        
-#         # Encode query and search documents
-#         query_vec = retriever.encode(query)
-#         documents = table.search(query_vec, vector_column_name="vector").limit(top_rerank).to_list()
-#         documents = [doc["text"] for doc in documents]
-        
-#         # Re-rank documents using cross-encoder
-#         cross_encoder_model = CrossEncoder('BAAI/bge-reranker-base') if cross_encoder_choice == '(ACCURATE) BGE reranker' else CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')
-#         query_doc_pair = [[query, doc] for doc in documents]
-#         cross_scores = cross_encoder_model.predict(query_doc_pair)
-#         sim_scores_argsort = list(reversed(np.argsort(cross_scores)))
-#         documents = [documents[idx] for idx in sim_scores_argsort[:top_k_rank]]
-        
-#         # Create context from top documents
-#         context = "\n\n".join(documents[:10]) if documents else ""
-#         context = f"Context information from educational materials:\n{context}\n\n"
-        
-#         # Add conversation history for context
-#         history_context = ""
-#         if history and len(history) > 0:
-#             for user_msg, bot_msg in history[-2:]:  # Last 2 exchanges
-#                 if user_msg and bot_msg:
-#                     history_context += f"Previous Q: {user_msg}\nPrevious A: {bot_msg}\n"
-        
-#         # Create full prompt
-#         full_prompt = f"{history_context}{context}Question: {query}\n\nPlease answer the question using the context provided above. If the context doesn't contain relevant information, use your general knowledge about 10th-grade science topics."
-        
-#         # Generate response
-#         response = agent.run(full_prompt)
-#         response_text = response.content if hasattr(response, 'content') else str(response)
-        
-#         logger.info(f"Response generation took {perf_counter() - start_time:.2f} seconds")
-#         return response_text
-    
-#     except Exception as e:
-#         logger.error(f"Error in response generation: {e}")
-#         return f"Error generating response: {str(e)}"
-
-# def simple_chat_function(message, history, cross_encoder_choice):
-#     """Chat function with semantic search and retriever integration"""
-#     if not message.strip():
-#         return "", history
-    
-#     # Generate response using the semantic search function
-#     response = retrieve_and_generate_response(message, cross_encoder_choice, history)
-    
-#     # Add to history
-#     history.append([message, response])
-    
-#     return "", history
-
-# # Minimal working interface
-# with gr.Blocks(title="Science Chatbot") as demo:
-#     # Cross-encoder selection
-#     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"
-#     )
-    
-#     chatbot = gr.Chatbot(label="Science Tutor Conversation")
-#     msg = gr.Textbox(placeholder="Type your message here...")
-#     clear = gr.Button("Clear")
-
-#     msg.submit(simple_chat_function, [msg, chatbot, cross_encoder], [msg, chatbot])
-#     clear.click(lambda: ([], ""), outputs=[chatbot, msg])
-
-# if __name__ == "__main__":
-#     demo.launch()# import gradio as gr
-
-
-
-
-# from phi.agent import Agent
-# from phi.model.groq import Groq
-# import os
-# import logging
-# from sentence_transformers import SentenceTransformer
-# from typing import List
-
-# # 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
-
-# # Initialize PhiData Agent
-# agent = Agent(
-#     model=Groq(model="llama3-70b-8192", api_key=api_key),
-#     instructions=[
-#         "You are a helpful assistant designed to answer questions on various topics.",
-#         "Use the provided context from retrieved documents to answer questions.",
-#         "If you don't have enough information, say 'I don’t have enough information to answer that.'"
-#     ],
-#     markdown=True
-# )
-
-# # Load a simple embedding model
-# embedding_model = SentenceTransformer('all-MiniLM-L6-v2')
-
-# # Simulated document corpus
-# documents = [
-#     "The capital of France is Paris.",
-#     "Python is a popular programming language.",
-#     "Semantic search uses embeddings to find relevant documents.",
-#     "The Eiffel Tower is located in Paris."
-# ]
-
-# # Convert documents to embeddings and store them
-# document_embeddings = embedding_model.encode(documents, convert_to_tensor=True)
-# import numpy as np
-# def retrieve_documents(query: str, k: int = 2) -> List[str]:
-#     """Simple retriever using cosine similarity."""
-#     query_embedding = embedding_model.encode(query, convert_to_tensor=True)
-#     similarities = np.dot(document_embeddings, query_embedding.T).cpu().numpy()
-#     top_k_indices = similarities.argsort()[-k:][::-1]
-#     return [documents[i] for i in top_k_indices]
-
-# def simple_chat_function(message, history):
-#     """Chat function with semantic search and retriever integration"""
-#     if not message.strip():
-#         return "", history
-    
-#     # Retrieve relevant documents
-#     context = retrieve_documents(message)
-#     context_text = "\n".join(context) if context else "No relevant context found."
-    
-#     # Generate response using PhiData agent with context
-#     try:
-#         response = agent.run(f"Context: {context_text}\n\nQuestion: {message}")
-#         response_text = response.content if hasattr(response, 'content') else "Error generating response."
-#     except Exception as e:
-#         logger.error(f"Agent error: {e}")
-#         response_text = "Sorry, there was an error processing your request."
-
-#     # Add to history
-#     history.append([message, response_text])
-    
-#     return "", history
-
-# # Minimal working interface
-# with gr.Blocks() as demo:
-#     chatbot = gr.Chatbot()
-#     msg = gr.Textbox(placeholder="Type your message here...")
-#     clear = gr.Button("Clear")
-
-#     msg.submit(simple_chat_function, [msg, chatbot], [msg, chatbot])
-#     clear.click(lambda: ([], ""), outputs=[chatbot, msg])
-
-# if __name__ == "__main__":
-#     demo.launch()