Create app.py

app.py

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+import os
+import dotenv
+import gradio as gr
+import lancedb
+import logging
+from langchain.embeddings.cohere import CohereEmbeddings
+from langchain.llms import Cohere
+from langchain.prompts import PromptTemplate
+from langchain.chains import RetrievalQA
+from langchain.vectorstores import LanceDB
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+# Assume these loaders are implemented based on your specific requirements
+from custom_document_loaders import TextLoader, PyPDFLoader, DocxLoader, ImageLoader
+import argostranslate.package
+import argostranslate.translate
+import shutil
+
+# Configuration and Logging
+dotenv.load_dotenv(".env")
+DB_PATH = "/tmp/lancedb"
+COHERE_MODEL_NAME = "multilingual-22-12"
+LANGUAGE_ISO_CODES = {"English": "en", "Hindi": "hi", "Turkish": "tr", "French": "fr"}
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+# Initialize argostranslate
+argostranslate.package.update_package_index()
+
+def initialize_documents_and_embeddings(input_file_path):
+    logger.info(f"Processing file: {input_file_path}")
+    file_extension = os.path.splitext(input_file_path)[1].lower()
+    loader = None
+
+    if file_extension in [".txt"]:
+        loader = TextLoader(input_file_path)
+    elif file_extension in [".pdf"]:
+        loader = PyPDFLoader(input_file_path)
+    elif file_extension in [".doc", ".docx"]:
+        loader = DocxLoader(input_file_path)
+    elif file_extension in [".jpg", ".jpeg", ".png"]:
+        loader = ImageLoader(input_file_path)
+    else:
+        raise ValueError("Unsupported file type. Supported files are .txt, .pdf, .docx, and image files.")
+
+    documents = loader.load()
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=300, chunk_overlap=50)
+    texts = text_splitter.split_documents(documents)
+    embeddings = CohereEmbeddings(model=COHERE_MODEL_NAME)
+    return texts, embeddings
+
+def initialize_database(texts, embeddings):
+    if os.path.exists(DB_PATH):
+        shutil.rmtree(DB_PATH)  # Ensure a fresh start
+    db = lancedb.connect(DB_PATH)
+    table = db.create_table("multiling-rag", mode="overwrite")
+    return LanceDB.from_documents(texts, embeddings, connection=table)
+
+def translate_text(text, from_code, to_code):
+    installed_languages = argostranslate.translate.get_installed_languages()
+    from_lang = next((lang for lang in installed_languages if lang.code == from_code), None)
+    to_lang = next((lang for lang in installed_languages if lang.code == to_code), None)
+    if not from_lang or not to_lang:
+        logger.error("Translation languages not installed.")
+        return "Translation error"
+    translation = from_lang.get_translation(to_lang)
+    return translation.translate(text)
+
+def answer_question(question, input_language, output_language, db):
+    try:
+        input_lang_code = LANGUAGE_ISO_CODES[input_language]
+        output_lang_code = LANGUAGE_ISO_CODES[output_language]
+        question_in_english = translate_text(question, input_lang_code, "en") if input_language != "English" else question
+        # Simplified retrieval and response logic for demonstration
+        response = "This is a simulated response based on the question."
+        result_in_target_language = translate_text(response, "en", output_lang_code) if output_language != "English" else response
+        return result_in_target_language
+    except Exception as e:
+        logger.error(f"Error in answer_question: {str(e)}")
+        return "An error occurred while processing your question."
+
+def document_analysis_and_feedback(document_path, feedback):
+    # Placeholder for document analysis logic
+    response = "Document analysis and feedback processing is not fully implemented."
+    return response
+
+def setup_gradio_interface(db):
+    with gr.Blocks() as demo:
+        gr.Markdown("# Multilingual Health and Wellness Chatbot")
+        with gr.Tab("Ask a Question"):
+            with gr.Row():
+                input_language = gr.Dropdown(list(LANGUAGE_ISO_CODES.keys()), label="Input Language")
+                output_language = gr.Dropdown(list(LANGUAGE_ISO_CODES.keys()), label="Output Language")
+            question = gr.Textbox(label="Your question")
+            answer = gr.Textbox(label="Answer")
+            question.submit(lambda q, i, o: answer_question(q, i, o, db), inputs=[question, input_language, output_language], outputs=answer)
+        
+        with gr.Tab("Upload Document"):
+            with gr.Row():
+                document = gr.File(label="Upload your health document")
+                feedback_box = gr.Textbox(label="Feedback (optional)")
+            upload_response = gr.Textbox(label="Analysis Result")
+            document.submit(document_analysis_and_feedback, inputs=[document, feedback_box], outputs=upload_response)
+
+    return demo
+
+def main():
+    INPUT_FILE_PATH = "sample-text.txt"  # Placeholder file path
+    texts, embeddings = initialize_documents_and_embeddings(INPUT_FILE_PATH)
+    db = initialize_database(texts, embeddings)
+    demo = setup_gradio_interface(db)
+    demo.launch()
+
+if __name__ == "__main__":
+    main()