Update app3.py

app3.py

@@ -16,16 +16,152 @@ from langchain_community.llms import HuggingFaceEndpoint
 from langchain.memory import ConversationBufferMemory
 from sentence_transformers import SentenceTransformer, util
 import torch
+from ragas import evaluate
+from ragas.metrics import (
+    ContextRecall,
+    AnswerRelevancy,
+    Faithfulness,
+    ContextPrecision
+)
+import pandas as pd
 
 # Constants and setup
 list_llm = ["meta-llama/Meta-Llama-3-8B-Instruct", "mistralai/Mistral-7B-Instruct-v0.2"]
 list_llm_simple = [os.path.basename(llm) for llm in list_llm]
 api_token = os.getenv("HF_TOKEN")
 
+CHUNK_SIZES = {
+    "small": {"recursive": 512, "fixed": 512, "token": 256},
+    "medium": {"recursive": 1024, "fixed": 1024, "token": 512}
+}
+
 # Initialize sentence transformer for evaluation
 sentence_model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
 
-# Text splitting strategies
+class RAGEvaluator:
+    def __init__(self):
+        self.datasets = {
+            "squad": "squad_v2",
+            "msmarco": "ms_marco"
+        }
+        self.current_dataset = None
+        self.test_samples = []
+        
+    def load_dataset(self, dataset_name: str, num_samples: int = 10):
+        """Load a smaller subset of questions with proper error handling"""
+        try:
+            if dataset_name == "squad":
+                dataset = load_dataset("squad_v2", split="validation")
+                # Select diverse questions
+                samples = dataset.select(range(0, 1000, 100))[:num_samples]
+                
+                self.test_samples = []
+                for sample in samples:
+                    # Check if answers exist and are not empty
+                    if sample.get("answers") and isinstance(sample["answers"], dict) and sample["answers"].get("text"):
+                        self.test_samples.append({
+                            "question": sample["question"],
+                            "ground_truth": sample["answers"]["text"][0],
+                            "context": sample["context"]
+                        })
+                
+            elif dataset_name == "msmarco":
+                dataset = load_dataset("ms_marco", "v2.1", split="dev")
+                samples = dataset.select(range(0, 1000, 100))[:num_samples]
+                
+                self.test_samples = []
+                for sample in samples:
+                    # Check for valid answers
+                    if sample.get("answers") and sample["answers"]:
+                        self.test_samples.append({
+                            "question": sample["query"],
+                            "ground_truth": sample["answers"][0],
+                            "context": sample["passages"][0]["passage_text"] 
+                                     if isinstance(sample["passages"], list) 
+                                     else sample["passages"]["passage_text"][0]
+                        })
+            
+            self.current_dataset = dataset_name
+            
+            # Return dataset info
+            return {
+                "dataset": dataset_name,
+                "num_samples": len(self.test_samples),
+                "sample_questions": [s["question"] for s in self.test_samples[:3]],
+                "status": "success"
+            }
+            
+        except Exception as e:
+            print(f"Error loading dataset: {str(e)}")
+            return {
+                "dataset": dataset_name,
+                "error": str(e),
+                "status": "failed"
+            }
+
+    def evaluate_configuration(self, vector_db, qa_chain, splitting_strategy: str, chunk_size: str) -> Dict:
+        """Evaluate with progress tracking and error handling"""
+        if not self.test_samples:
+            return {"error": "No dataset loaded"}
+            
+        results = []
+        total_questions = len(self.test_samples)
+        
+        # Add progress tracking
+        for i, sample in enumerate(self.test_samples):
+            print(f"Evaluating question {i+1}/{total_questions}")
+            
+            try:
+                response = qa_chain.invoke({
+                    "question": sample["question"],
+                    "chat_history": []
+                })
+                
+                results.append({
+                    "question": sample["question"],
+                    "answer": response["answer"],
+                    "contexts": [doc.page_content for doc in response["source_documents"]],
+                    "ground_truths": [sample["ground_truth"]]
+                })
+            except Exception as e:
+                print(f"Error processing question {i+1}: {str(e)}")
+                continue
+        
+        if not results:
+            return {
+                "configuration": f"{splitting_strategy}_{chunk_size}",
+                "error": "No successful evaluations",
+                "questions_evaluated": 0
+            }
+            
+        try:
+            # Calculate RAGAS metrics
+            eval_dataset = Dataset.from_list(results)
+            metrics = [ContextRecall(), AnswerRelevancy(), Faithfulness(), ContextPrecision()]
+            scores = evaluate(eval_dataset, metrics=metrics)
+            
+            return {
+                "configuration": f"{splitting_strategy}_{chunk_size}",
+                "questions_evaluated": len(results),
+                "context_recall": float(scores['context_recall']),
+                "answer_relevancy": float(scores['answer_relevancy']),
+                "faithfulness": float(scores['faithfulness']),
+                "context_precision": float(scores['context_precision']),
+                "average_score": float(np.mean([
+                    scores['context_recall'],
+                    scores['answer_relevancy'],
+                    scores['faithfulness'],
+                    scores['context_precision']
+                ]))
+            }
+        except Exception as e:
+            return {
+                "configuration": f"{splitting_strategy}_{chunk_size}",
+                "error": str(e),
+                "questions_evaluated": len(results)
+            }
+
+# Text splitting and database functions
 def get_text_splitter(strategy: str, chunk_size: int = 1024, chunk_overlap: int = 64):
     splitters = {
         "recursive": RecursiveCharacterTextSplitter(
@@ -43,105 +179,38 @@ def get_text_splitter(strategy: str, chunk_size: int = 1024, chunk_overlap: int
     }
     return splitters.get(strategy)
 
-# Custom evaluation metrics
-def calculate_semantic_similarity(text1: str, text2: str) -> float:
-    embeddings1 = sentence_model.encode([text1], convert_to_tensor=True)
-    embeddings2 = sentence_model.encode([text2], convert_to_tensor=True)
-    similarity = util.pytorch_cos_sim(embeddings1, embeddings2)
-    return float(similarity[0][0])
-
-def evaluate_response(question: str, answer: str, ground_truth: str, contexts: List[str]) -> Dict[str, float]:
-    # Answer similarity with ground truth
-    answer_similarity = calculate_semantic_similarity(answer, ground_truth)
-    
-    # Context relevance - average similarity between question and contexts
-    context_scores = [calculate_semantic_similarity(question, ctx) for ctx in contexts]
-    context_relevance = np.mean(context_scores)
-    
-    # Answer relevance - similarity between question and answer
-    answer_relevance = calculate_semantic_similarity(question, answer)
-    
-    return {
-        "answer_similarity": answer_similarity,
-        "context_relevance": context_relevance,
-        "answer_relevance": answer_relevance,
-        "average_score": np.mean([answer_similarity, context_relevance, answer_relevance])
-    }
-
-# Load and split PDF document
-def load_doc(list_file_path: List[str], splitting_strategy: str = "recursive"):
+def load_doc(list_file_path: List[str], splitting_strategy: str, chunk_size: str):
+    chunk_size_value = CHUNK_SIZES[chunk_size][splitting_strategy]
     loaders = [PyPDFLoader(x) for x in list_file_path]
     pages = []
     for loader in loaders:
         pages.extend(loader.load())
     
-    text_splitter = get_text_splitter(splitting_strategy)
+    text_splitter = get_text_splitter(splitting_strategy, chunk_size_value)
     doc_splits = text_splitter.split_documents(pages)
     return doc_splits
 
-# Vector database creation functions
-def create_faiss_db(splits, embeddings):
-    return FAISS.from_documents(splits, embeddings)
-
-def create_chroma_db(splits, embeddings):
-    return Chroma.from_documents(splits, embeddings)
-
-def create_qdrant_db(splits, embeddings):
-    return Qdrant.from_documents(
-        splits,
-        embeddings,
-        location=":memory:",
-        collection_name="pdf_docs"
-    )
-
 def create_db(splits, db_choice: str = "faiss"):
     embeddings = HuggingFaceEmbeddings()
     db_creators = {
-        "faiss": create_faiss_db,
-        "chroma": create_chroma_db,
-        "qdrant": create_qdrant_db
-    }
-    return db_creators[db_choice](splits, embeddings)
-
-def load_evaluation_dataset():
-    dataset = load_dataset("explodinggradients/fiqa", split="test", trust_remote_code=True)
-    return dataset
-
-def evaluate_rag_pipeline(qa_chain, dataset):
-    # Sample a few examples for evaluation
-    eval_samples = dataset.select(range(5))
-    
-    results = []
-    for sample in eval_samples:
-        question = sample["question"]
-        
-        # Get response from the chain
-        response = qa_chain.invoke({
-            "question": question,
-            "chat_history": []
-        })
-        
-        # Evaluate response
-        eval_result = evaluate_response(
-            question=question,
-            answer=response["answer"],
-            ground_truth=sample["answer"],
-            contexts=[doc.page_content for doc in response["source_documents"]]
+        "faiss": lambda: FAISS.from_documents(splits, embeddings),
+        "chroma": lambda: Chroma.from_documents(splits, embeddings),
+        "qdrant": lambda: Qdrant.from_documents(
+            splits,
+            embeddings,
+            location=":memory:",
+            collection_name="pdf_docs"
         )
-        
-        results.append(eval_result)
-    
-    # Calculate average scores across all samples
-    avg_results = {
-        metric: float(np.mean([r[metric] for r in results]))
-        for metric in results[0].keys()
     }
-    
-    return avg_results
+    return db_creators[db_choice]()
+
+def initialize_database(list_file_obj, splitting_strategy, chunk_size, db_choice, progress=gr.Progress()):
+    list_file_path = [x.name for x in list_file_obj if x is not None]
+    doc_splits = load_doc(list_file_path, splitting_strategy, chunk_size)
+    vector_db = create_db(doc_splits, db_choice)
+    return vector_db, f"Database created using {splitting_strategy} splitting and {db_choice} vector database!"
 
-# Initialize langchain LLM chain
 def initialize_llmchain(llm_choice, temperature, max_tokens, top_k, vector_db, progress=gr.Progress()):
-    # Get the full model name from the index
     llm_model = list_llm[llm_choice]
     
     llm = HuggingFaceEndpoint(
@@ -149,8 +218,7 @@ def initialize_llmchain(llm_choice, temperature, max_tokens, top_k, vector_db, p
         huggingfacehub_api_token=api_token,
         temperature=temperature,
         max_new_tokens=max_tokens,
-        top_k=top_k,
-        model=llm_model  # Add model parameter
+        top_k=top_k
     )
     
     memory = ConversationBufferMemory(
@@ -163,162 +231,278 @@ def initialize_llmchain(llm_choice, temperature, max_tokens, top_k, vector_db, p
     qa_chain = ConversationalRetrievalChain.from_llm(
         llm,
         retriever=retriever,
-        chain_type="stuff",
         memory=memory,
-        return_source_documents=True,
-        verbose=False,
+        return_source_documents=True
     )
     return qa_chain, "LLM initialized successfully!"
 
-def initialize_database(list_file_obj, splitting_strategy, db_choice, progress=gr.Progress()):
-    list_file_path = [x.name for x in list_file_obj if x is not None]
-    doc_splits = load_doc(list_file_path, splitting_strategy)
-    vector_db = create_db(doc_splits, db_choice)
-    return vector_db, f"Database created using {splitting_strategy} splitting and {db_choice} vector database!"
-
-def format_chat_history(message, chat_history):
-    formatted_chat_history = []
-    for user_message, bot_message in chat_history:
-        formatted_chat_history.append(f"User: {user_message}")
-        formatted_chat_history.append(f"Assistant: {bot_message}")
-    return formatted_chat_history
-
 def conversation(qa_chain, message, history):
-    formatted_chat_history = format_chat_history(message, history)
+    """Fixed conversation function returning all required outputs"""
     response = qa_chain.invoke({
         "question": message,
-        "chat_history": formatted_chat_history
+        "chat_history": [(hist[0], hist[1]) for hist in history]
     })
     
     response_answer = response["answer"]
-    if response_answer.find("Helpful Answer:") != -1:
+    if "Helpful Answer:" in response_answer:
         response_answer = response_answer.split("Helpful Answer:")[-1]
     
-    response_sources = response["source_documents"]
-    response_source1 = response_sources[0].page_content.strip()
-    response_source2 = response_sources[1].page_content.strip()
-    response_source3 = response_sources[2].page_content.strip()
+    # Get source documents, ensure we have exactly 3
+    sources = response["source_documents"][:3]
+    source_contents = []
+    source_pages = []
     
-    response_source1_page = response_sources[0].metadata["page"] + 1
-    response_source2_page = response_sources[1].metadata["page"] + 1
-    response_source3_page = response_sources[2].metadata["page"] + 1
+    # Process available sources
+    for source in sources:
+        source_contents.append(source.page_content.strip())
+        source_pages.append(source.metadata.get("page", 0) + 1)
     
-    new_history = history + [(message, response_answer)]
+    # Pad with empty values if we have fewer than 3 sources
+    while len(source_contents) < 3:
+        source_contents.append("")
+        source_pages.append(0)
     
-    return qa_chain, gr.update(value=""), new_history, response_source1, response_source1_page, response_source2, response_source2_page, response_source3, response_source3_page
+    # Return all required outputs in correct order
+    return (
+        qa_chain,  # State
+        gr.update(value=""),  # Clear message box
+        history + [(message, response_answer)],  # Updated chat history
+        source_contents[0],  # First source
+        source_pages[0],    # First page
+        source_contents[1],  # Second source
+        source_pages[1],    # Second page
+        source_contents[2],  # Third source
+        source_pages[2]     # Third page
+    )
 
 def demo():
+    evaluator = RAGEvaluator()
+    
     with gr.Blocks(theme=gr.themes.Default(primary_hue="red", secondary_hue="pink", neutral_hue="sky")) as demo:
         vector_db = gr.State()
         qa_chain = gr.State()
         
-        gr.HTML("<center><h1>Enhanced RAG PDF Chatbot</h1></center>")
-        gr.Markdown("""<b>Query your PDF documents with advanced RAG capabilities!</b>""")
+        gr.HTML("<center><h1>Enhanced RAG PDF Chatbot with Evaluation</h1></center>")
         
-        with gr.Row():
-            with gr.Column(scale=86):
-                gr.Markdown("<b>Step 1 - Configure and Initialize RAG Pipeline</b>")
+        with gr.Tabs():
+            # Custom PDF Tab
+            with gr.Tab("Custom PDF Chat"):
                 with gr.Row():
-                    document = gr.Files(height=300, file_count="multiple", file_types=["pdf"], interactive=True, label="Upload PDF documents")
+                    with gr.Column(scale=86):
+                        gr.Markdown("<b>Step 1 - Configure and Initialize RAG Pipeline</b>")
+                        with gr.Row():
+                            document = gr.Files(
+                                height=300,
+                                file_count="multiple",
+                                file_types=["pdf"],
+                                interactive=True,
+                                label="Upload PDF documents"
+                            )
+                        
+                        with gr.Row():
+                            splitting_strategy = gr.Radio(
+                                ["recursive", "fixed", "token"],
+                                label="Text Splitting Strategy",
+                                value="recursive"
+                            )
+                            db_choice = gr.Radio(
+                                ["faiss", "chroma", "qdrant"],
+                                label="Vector Database",
+                                value="faiss"
+                            )
+                            chunk_size = gr.Radio(
+                                ["small", "medium"],
+                                label="Chunk Size",
+                                value="medium"
+                            )
+                        
+                        with gr.Row():
+                            db_btn = gr.Button("Create vector database")
+                            db_progress = gr.Textbox(
+                                value="Not initialized",
+                                show_label=False
+                            )
+                        
+                        gr.Markdown("<b>Step 2 - Configure LLM</b>")
+                        with gr.Row():
+                            llm_choice = gr.Radio(
+                                list_llm_simple,
+                                label="Available LLMs",
+                                value=list_llm_simple[0],
+                                type="index"
+                            )
+                        
+                        with gr.Row():
+                            with gr.Accordion("LLM Parameters", open=False):
+                                temperature = gr.Slider(
+                                    minimum=0.01,
+                                    maximum=1.0,
+                                    value=0.5,
+                                    step=0.1,
+                                    label="Temperature"
+                                )
+                                max_tokens = gr.Slider(
+                                    minimum=128,
+                                    maximum=4096,
+                                    value=2048,
+                                    step=128,
+                                    label="Max Tokens"
+                                )
+                                top_k = gr.Slider(
+                                    minimum=1,
+                                    maximum=10,
+                                    value=3,
+                                    step=1,
+                                    label="Top K"
+                                )
+                        
+                        with gr.Row():
+                            init_llm_btn = gr.Button("Initialize LLM")
+                            llm_progress = gr.Textbox(
+                                value="Not initialized",
+                                show_label=False
+                            )
+                    
+                    with gr.Column(scale=200):
+                        gr.Markdown("<b>Step 3 - Chat with Documents</b>")
+                        chatbot = gr.Chatbot(height=505)
+                        
+                        with gr.Accordion("Source References", open=False):
+                            with gr.Row():
+                                source1 = gr.Textbox(label="Source 1", lines=2)
+                                page1 = gr.Number(label="Page")
+                            with gr.Row():
+                                source2 = gr.Textbox(label="Source 2", lines=2)
+                                page2 = gr.Number(label="Page")
+                            with gr.Row():
+                                source3 = gr.Textbox(label="Source 3", lines=2)
+                                page3 = gr.Number(label="Page")
+                        
+                        with gr.Row():
+                            msg = gr.Textbox(
+                                placeholder="Ask a question",
+                                show_label=False
+                            )
+                        with gr.Row():
+                            submit_btn = gr.Button("Submit")
+                            clear_btn = gr.ClearButton(
+                                [msg, chatbot],
+                                value="Clear Chat"
+                            )
+
+            # Evaluation Tab
+            with gr.Tab("RAG Evaluation"):
+                with gr.Row():
+                    dataset_choice = gr.Dropdown(
+                        choices=list(evaluator.datasets.keys()),
+                        label="Select Evaluation Dataset",
+                        value="squad"
+                    )
+                    load_dataset_btn = gr.Button("Load Dataset")
                 
                 with gr.Row():
-                    splitting_strategy = gr.Radio(
+                    dataset_info = gr.JSON(label="Dataset Information")
+                
+                with gr.Row():
+                    eval_splitting_strategy = gr.Radio(
                         ["recursive", "fixed", "token"],
                         label="Text Splitting Strategy",
                         value="recursive"
                     )
-                    db_choice = gr.Radio(
-                        ["faiss", "chroma", "qdrant"],
-                        label="Vector Database",
-                        value="faiss"
+                    eval_chunk_size = gr.Radio(
+                        ["small", "medium"],
+                        label="Chunk Size",
+                        value="medium"
                     )
                 
                 with gr.Row():
-                    db_btn = gr.Button("Create vector database")
-                    evaluate_btn = gr.Button("Evaluate RAG Pipeline")
-                
-                with gr.Row():
-                    db_progress = gr.Textbox(value="Not initialized", show_label=False)
-                    evaluation_results = gr.JSON(label="Evaluation Results")
-                
-                gr.Markdown("<b>Select Large Language Model (LLM) and input parameters</b>")
-                with gr.Row():
-                    llm_btn = gr.Radio(list_llm_simple, label="Available LLMs", value=list_llm_simple[0], type="index")
-                
-                with gr.Row():
-                    with gr.Accordion("LLM input parameters", open=False):
-                        slider_temperature = gr.Slider(minimum=0.01, maximum=1.0, value=0.5, step=0.1, label="Temperature")
-                        slider_maxtokens = gr.Slider(minimum=128, maximum=9192, value=4096, step=128, label="Max New Tokens")
-                        slider_topk = gr.Slider(minimum=1, maximum=10, value=3, step=1, label="top-k")
-                
-                with gr.Row():
-                    qachain_btn = gr.Button("Initialize Question Answering Chatbot")
-                    llm_progress = gr.Textbox(value="Not initialized", show_label=False)
-            
-            with gr.Column(scale=200):
-                gr.Markdown("<b>Step 2 - Chat with your Document</b>")
-                chatbot = gr.Chatbot(height=505)
-                
-                with gr.Accordion("Relevant context from the source document", open=False):
-                    with gr.Row():
-                        doc_source1 = gr.Textbox(label="Reference 1", lines=2, container=True, scale=20)
-                        source1_page = gr.Number(label="Page", scale=1)
-                    with gr.Row():
-                        doc_source2 = gr.Textbox(label="Reference 2", lines=2, container=True, scale=20)
-                        source2_page = gr.Number(label="Page", scale=1)
-                    with gr.Row():
-                        doc_source3 = gr.Textbox(label="Reference 3", lines=2, container=True, scale=20)
-                        source3_page = gr.Number(label="Page", scale=1)
-                
-                with gr.Row():
-                    msg = gr.Textbox(placeholder="Ask a question", container=True)
-                with gr.Row():
-                    submit_btn = gr.Button("Submit")
-                    clear_btn = gr.ClearButton([msg, chatbot], value="Clear")
-        
+                    evaluate_btn = gr.Button("Run Evaluation")
+                    evaluation_results = gr.DataFrame(label="Evaluation Results")
+
         # Event handlers
         db_btn.click(
             initialize_database,
-            inputs=[document, splitting_strategy, db_choice],
+            inputs=[document, splitting_strategy, chunk_size, db_choice],
             outputs=[vector_db, db_progress]
         )
         
-        evaluate_btn.click(
-            lambda qa_chain: evaluate_rag_pipeline(qa_chain, load_evaluation_dataset()) if qa_chain else None,
-            inputs=[qa_chain],
-            outputs=[evaluation_results]
-        )
-        
-        qachain_btn.click(
-            initialize_llmchain,  # Fixed function name here
-            inputs=[llm_btn, slider_temperature, slider_maxtokens, slider_topk, vector_db],
+        init_llm_btn.click(
+            initialize_llmchain,
+            inputs=[llm_choice, temperature, max_tokens, top_k, vector_db],
             outputs=[qa_chain, llm_progress]
-        ).then(
-            lambda: [None, "", 0, "", 0, "", 0],
-            inputs=None,
-            outputs=[chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page],
-            queue=False
         )
         
-        msg.submit(conversation,
+        msg.submit(
+            conversation,
             inputs=[qa_chain, msg, chatbot],
-            outputs=[qa_chain, msg, chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page],
-            queue=False
+            outputs=[qa_chain, msg, chatbot, source1, page1, source2, page2, source3, page3]
         )
         
-        submit_btn.click(conversation,
+        submit_btn.click(
+            conversation,
             inputs=[qa_chain, msg, chatbot],
-            outputs=[qa_chain, msg, chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page],
-            queue=False
+            outputs=[qa_chain, msg, chatbot, source1, page1, source2, page2, source3, page3]
         )
+
+        def load_dataset_handler(dataset_name):
+            try:
+                result = evaluator.load_dataset(dataset_name)
+                if result.get("status") == "success":
+                    return {
+                        "dataset": result["dataset"],
+                        "samples_loaded": result["num_samples"],
+                        "example_questions": result["sample_questions"],
+                        "status": "ready for evaluation"
+                    }
+                else:
+                    return {
+                        "error": result.get("error", "Unknown error occurred"),
+                        "status": "failed to load dataset"
+                    }
+            except Exception as e:
+                return {
+                    "error": str(e),
+                    "status": "failed to load dataset"
+                }
         
+        def run_evaluation(dataset_choice, splitting_strategy, chunk_size, vector_db, qa_chain):
+            if not evaluator.current_dataset:
+                return pd.DataFrame()
+            
+            results = evaluator.evaluate_configuration(
+                vector_db=vector_db,
+                qa_chain=qa_chain,
+                splitting_strategy=splitting_strategy,
+                chunk_size=chunk_size
+            )
+            
+            return pd.DataFrame([results])
+
+        load_dataset_btn.click(
+            load_dataset_handler,
+            inputs=[dataset_choice],
+            outputs=[dataset_info]
+        )
+        
+        evaluate_btn.click(
+            run_evaluation,
+            inputs=[
+                dataset_choice,
+                eval_splitting_strategy,
+                eval_chunk_size,
+                vector_db,
+                qa_chain
+            ],
+            outputs=[evaluation_results]
+        )
+
+        # Clear button handlers
         clear_btn.click(
             lambda: [None, "", 0, "", 0, "", 0],
-            inputs=None,
-            outputs=[chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page],
-            queue=False
+            outputs=[chatbot, source1, page1, source2, page2, source3, page3]
         )
-    
+
+    # Launch the demo
     demo.queue().launch(debug=True)
 
 if __name__ == "__main__":