go11

app.py

@@ -13,67 +13,80 @@ import torch
 
 class EnhancedRAGSystem:
     def __init__(self):
-        self.chunk_size = 500
-        self.chunk_overlap = 50
-        self.k_documents = 4
-        
-        self.text_splitter = RecursiveCharacterTextSplitter(
-            chunk_size=self.chunk_size,
-            chunk_overlap=self.chunk_overlap,
-            length_function=len
-        )
-        
-        self.embedding_model_name = "intfloat/multilingual-e5-large"
-        self.llm_model_name = "google/flan-t5-large"
-        
-        self.prompt_template = PromptTemplate(
-            template="""Use the context below to answer the question. 
-            If the answer is not in the context, say "I don't have enough information in the context to answer this question."
+        try:
+            print("Initializing RAG System...")
+            self.chunk_size = 500
+            self.chunk_overlap = 50
+            self.k_documents = 4
             
-            Context: {context}
-            Question: {question}
+            self.text_splitter = RecursiveCharacterTextSplitter(
+                chunk_size=self.chunk_size,
+                chunk_overlap=self.chunk_overlap,
+                length_function=len
+            )
             
-            Detailed answer:""",
-            input_variables=["context", "question"]
-        )
-        
-        self.embeddings = HuggingFaceEmbeddings(
-            model_name=self.embedding_model_name,
-            model_kwargs={'device': 'cuda' if torch.cuda.is_available() else 'cpu'}
-        )
-        
-        self.tokenizer = AutoTokenizer.from_pretrained(self.llm_model_name)
-        self.model = AutoModelForSeq2SeqLM.from_pretrained(self.llm_model_name)
-        
-        self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
-        self.model.to(self.device)
-        
-        self.pipe = pipeline(
-            "text2text-generation",
-            model=self.model,
-            tokenizer=self.tokenizer,
-            max_length=512,
-            device=0 if torch.cuda.is_available() else -1,
-            model_kwargs={"temperature": 0.7}
-        )
-        
-        self.llm = HuggingFacePipeline(pipeline=self.pipe)
-        
+            print("Loading embedding model...")
+            self.embeddings = HuggingFaceEmbeddings(
+                model_name="sentence-transformers/all-MiniLM-L6-v2",
+                model_kwargs={'device': 'cpu'}
+            )
+            
+            print("Loading language model...")
+            self.llm_model_name = "google/flan-t5-small"
+            self.tokenizer = AutoTokenizer.from_pretrained(self.llm_model_name)
+            self.model = AutoModelForSeq2SeqLM.from_pretrained(self.llm_model_name)
+            
+            self.prompt_template = PromptTemplate(
+                template="""Use the context below to answer the question. 
+                If the answer is not in the context, say "I don't have enough information in the context to answer this question."
+                
+                Context: {context}
+                Question: {question}
+                
+                Detailed answer:""",
+                input_variables=["context", "question"]
+            )
+            
+            print("Setting up pipeline...")
+            self.pipe = pipeline(
+                "text2text-generation",
+                model=self.model,
+                tokenizer=self.tokenizer,
+                max_length=512,
+                device=-1,
+                model_kwargs={"temperature": 0.7}
+            )
+            
+            self.llm = HuggingFacePipeline(pipeline=self.pipe)
+            print("RAG System initialized successfully!")
+            
+        except Exception as e:
+            print(f"Error during initialization: {str(e)}")
+            raise
+
     def process_documents(self, text: str) -> bool:
         try:
+            print("Processing documents...")
+            if not text or len(text.strip()) < 10:
+                print("Text is too short or empty")
+                return False
+                
+            print("Splitting text...")
             texts = self.text_splitter.split_text(text)
             
+            print("Creating vectorstore...")
             self.vectorstore = Chroma.from_texts(
                 texts,
                 self.embeddings,
-                metadatas=[{"source": f"chunk_{i}", "text": t} for i, t in enumerate(texts)],
-                collection_name="enhanced_rag_docs"
+                metadatas=[{"source": f"chunk_{i}", "text": t} for i, t in enumerate(texts)]
             )
             
+            print("Setting up retriever...")
             self.retriever = self.vectorstore.as_retriever(
                 search_kwargs={"k": self.k_documents}
             )
             
+            print("Creating QA chain...")
             self.qa_chain = RetrievalQA.from_chain_type(
                 llm=self.llm,
                 chain_type="stuff",
@@ -81,13 +94,20 @@ class EnhancedRAGSystem:
                 return_source_documents=True,
                 chain_type_kwargs={"prompt": self.prompt_template}
             )
+            
+            print("Documents processed successfully!")
             return True
+            
         except Exception as e:
-            print(f"Processing error: {str(e)}")
+            print(f"Error processing documents: {str(e)}")
             return False
 
     def answer_question(self, question: str) -> Tuple[str, str]:
         try:
+            print(f"Answering question: {question}")
+            if not hasattr(self, 'qa_chain'):
+                return "Please process some documents first.", ""
+                
             response = self.qa_chain({"query": question})
             answer = response["result"]
             
@@ -97,112 +117,120 @@ class EnhancedRAGSystem:
                 sources.append(f"Excerpt {i}: {text_preview}")
             
             sources_text = "\n".join(sources)
+            print("Answer generated successfully!")
             return answer, sources_text
+            
         except Exception as e:
-            return f"Error answering: {str(e)}", ""
+            print(f"Error generating answer: {str(e)}")
+            return f"Error generating answer: {str(e)}", ""
 
 def create_enhanced_interface():
-    rag_system = EnhancedRAGSystem()
-    
-    def process_and_answer(text: str, question: str) -> str:
-        if not text.strip() or not question.strip():
-            return "Please provide both text and question."
-        
-        if not rag_system.process_documents(text):
-            return "Error processing the text."
+    try:
+        print("Creating interface...")
+        rag_system = EnhancedRAGSystem()
         
-        answer, sources = rag_system.answer_question(question)
-        
-        if sources:
-            return f"""Answer: {answer}
+        def process_and_answer(text: str, question: str) -> str:
+            print("Processing new request...")
+            if not text.strip() or not question.strip():
+                return "Please provide both text and question."
+            
+            success = rag_system.process_documents(text)
+            if not success:
+                return "Error processing the text. Please check if the text is valid and try again."
+            
+            answer, sources = rag_system.answer_question(question)
+            
+            if sources:
+                return f"""Answer: {answer}
 
 Relevant excerpts consulted:
 {sources}"""
-        return answer
+            return answer
 
-    # HTML para o cabeçalho
-    custom_css = """
-        .custom-description {
-            margin-bottom: 20px;
-            text-align: center;
-        }
-        .custom-description a {
-            text-decoration: none;
-            color: #007bff;
-            margin: 0 5px;
-        }
-        .custom-description a:hover {
-            text-decoration: underline;
-        }
-    """
+        custom_css = """
+            .custom-description {
+                margin-bottom: 20px;
+                text-align: center;
+            }
+            .custom-description a {
+                text-decoration: none;
+                color: #007bff;
+                margin: 0 5px;
+            }
+            .custom-description a:hover {
+                text-decoration: underline;
+            }
+        """
 
-    with gr.Blocks(css=custom_css) as interface:
-        gr.HTML("""
-            <div class="custom-description">
-                <h1>Advanced RAG with Multilingual Support</h1>
-                <p>Ramon Mayor Martins: 
-                    <a href="https://rmayormartins.github.io/" target="_blank">Website</a> | 
-                    <a href="https://huggingface.co/rmayormartins" target="_blank">Spaces</a> |
-                    <a href="https://github.com/rmayormartins" target="_blank">GitHub</a>
-                </p>
-                <p>This system uses Retrieval-Augmented Generation (RAG) to answer questions about your texts in multiple languages.
-                Simply paste your text and ask questions in any language!</p>
+        with gr.Blocks(css=custom_css) as interface:
+            gr.HTML("""
+                <div class="custom-description">
+                    <h1>Advanced RAG with Multilingual Support</h1>
+                    <p>Ramon Mayor Martins: 
+                        <a href="https://rmayormartins.github.io/" target="_blank">Website</a> | 
+                        <a href="https://huggingface.co/rmayormartins" target="_blank">Spaces</a> |
+                        <a href="https://github.com/rmayormartins" target="_blank">GitHub</a>
+                    </p>
+                    <p>This system uses Retrieval-Augmented Generation (RAG) to answer questions about your texts in multiple languages.
+                    Simply paste your text and ask questions in any language!</p>
+                </div>
+            """)
+            
+            with gr.Row():
+                with gr.Column():
+                    text_input = gr.Textbox(
+                        label="Base Text",
+                        placeholder="Paste here the text that will serve as knowledge base...",
+                        lines=10
+                    )
+                    question_input = gr.Textbox(
+                        label="Your Question",
+                        placeholder="What would you like to know about the text?"
+                    )
+                    submit_btn = gr.Button("Submit")
                 
-            </div>
-        """)
-        
-        with gr.Row():
-            with gr.Column():
-                text_input = gr.Textbox(
-                    label="Base Text",
-                    placeholder="Paste here the text that will serve as knowledge base...",
-                    lines=10
-                )
-                question_input = gr.Textbox(
-                    label="Your Question",
-                    placeholder="What would you like to know about the text?"
-                )
-                submit_btn = gr.Button("Submit")
-            
-            with gr.Column():
-                output = gr.Textbox(label="Answer")
+                with gr.Column():
+                    output = gr.Textbox(label="Answer")
 
-        examples = [
-            ["The Earth is the third planet from the Sun. It has one natural satellite called the Moon. It is the only known planet to harbor life.",
-             "What is Earth's natural satellite?"],
-            
-            ["La Tierra es el tercer planeta del Sistema Solar. Tiene un satélite natural llamado Luna. Es el único planeta conocido que alberga vida.",
-             "¿Cuál es el satélite natural de la Tierra?"],
-            
-            ["A Terra é o terceiro planeta do Sistema Solar. Tem um satélite natural chamado Lua. É o único planeta conhecido que abriga vida.",
-             "Qual é o satélite natural da Terra?"],
-            
-            ["The Sun is a medium-sized star at the center of our Solar System. It provides light and heat to all planets.",
-             "What is the Sun?"],
-            
-            ["El Sol es una estrella de tamaño medio en el centro de nuestro Sistema Solar. Proporciona luz y calor a todos los planetas.",
-             "¿Qué es el Sol?"],
-            
-            ["O Sol é uma estrela de tamanho médio no centro do nosso Sistema Solar. Ele fornece luz e calor para todos os planetas.",
-             "O que é o Sol?"]
-        ]
+            examples = [
+                # English example
+                ["The solar system consists of the Sun and the celestial bodies that orbit it. These include eight planets (Mercury, Venus, Earth, Mars, Jupiter, Saturn, Uranus, and Neptune), their moons, asteroids, comets, and other objects.",
+                 "How many planets are in the solar system?"],
+                
+                # Spanish example
+                ["El sistema solar está formado por el Sol y los cuerpos celestes que orbitan a su alrededor. Estos incluyen ocho planetas (Mercurio, Venus, Tierra, Marte, Júpiter, Saturno, Urano y Neptuno), sus lunas, asteroides, cometas y otros objetos.",
+                 "¿Cuántos planetas hay en el sistema solar?"],
+                
+                # Portuguese example
+                ["O sistema solar é composto pelo Sol e pelos corpos celestes que orbitam ao seu redor. Isso inclui oito planetas (Mercúrio, Vênus, Terra, Marte, Júpiter, Saturno, Urano e Netuno), suas luas, asteroides, cometas e outros objetos.",
+                 "Quantos planetas existem no sistema solar?"]
+            ]
 
-        gr.Examples(
-            examples=examples,
-            inputs=[text_input, question_input],
-            outputs=output,
-            fn=process_and_answer,
-            cache_examples=True
-        )
+            gr.Examples(
+                examples=examples,
+                inputs=[text_input, question_input],
+                outputs=output,
+                fn=process_and_answer,
+                cache_examples=True
+            )
 
-        submit_btn.click(
-            fn=process_and_answer,
-            inputs=[text_input, question_input],
-            outputs=output
-        )
+            submit_btn.click(
+                fn=process_and_answer,
+                inputs=[text_input, question_input],
+                outputs=output
+            )
 
-    return interface
+        print("Interface created successfully!")
+        return interface
+        
+    except Exception as e:
+        print(f"Error creating interface: {str(e)}")
+        raise
 
 if __name__ == "__main__":
-    demo = create_enhanced_interface()
-    demo.launch()
+    print("Starting application...")
+    try:
+        demo = create_enhanced_interface()
+        demo.launch()
+    except Exception as e:
+        print(f"Application failed to start: {str(e)}")