Update app.py

app.py

@@ -1,155 +1,78 @@
-import time
-import logging
+import os
 import gradio as gr
-import os  
-from datetime import datetime
-from datasets import Dataset, load_dataset
-from langchain.document_loaders import PyPDFLoader
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.embeddings import HuggingFaceEmbeddings
-from langchain.vectorstores import FAISS
-from groq import Groq
-from langchain.memory import ConversationBufferMemory  
+from langchain_groq import ChatGroq
+from langchain_huggingface import HuggingFaceEmbeddings
+from langchain_core.vectorstores import InMemoryVectorStore
+from langchain_core.documents import Document
+from langchain_text_splitters import RecursiveCharacterTextSplitter
 
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
 
-groq_api_key = os.environ.get("GROQ_API_KEY")
-hf_api_key = os.environ.get("HF_API_KEY")
-
-if not groq_api_key:
-    raise ValueError("Groq API key not found in environment variables.")
-if not hf_api_key:
-    raise ValueError("Hugging Face API key not found in environment variables.")
-
-client = Groq(api_key=groq_api_key)
+embeddings = HuggingFaceEmbeddings(model_name="heydariAI/persian-embeddings")
+vector_store = InMemoryVectorStore(embeddings)
+text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+model = ChatGroq(api_key="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", model_name="deepseek-r1-distill-llama-70b")
 
-hf_token = hf_api_key
+def process_file(file_path):
 
-embeddings = HuggingFaceEmbeddings(model_name="heydariAI/persian-embeddings")
+    if not file_path:
+        return None
 
-DATASET_NAME = "chat_history"
-try:
-    dataset = load_dataset(DATASET_NAME, use_auth_token=hf_token)
-except Exception:
-    dataset = Dataset.from_dict({"Timestamp": [], "User": [], "ParvizGPT": []})
+    file_extension = os.path.splitext(file_path)[1].lower()
 
-def save_chat_to_dataset(user_message, bot_message):
-    """Save chat history to Hugging Face Dataset."""
     try:
-        timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
-        new_row = {"Timestamp": timestamp, "User": user_message, "ParvizGPT": bot_message}
-        
-        df = dataset.to_pandas()
-        df = df.append(new_row, ignore_index=True)
-        updated_dataset = Dataset.from_pandas(df)
-        
-        updated_dataset.push_to_hub(DATASET_NAME, token=hf_token)
+        if file_extension == ".pdf":
+            from pypdf import PdfReader
+            reader = PdfReader(file_path)
+            return "\n".join(page.extract_text() for page in reader.pages)
+        elif file_extension == ".txt":
+            with open(file_path, "r", encoding="utf-8") as f:
+                return f.read()
+        else:
+            raise ValueError(f"Unsupported file type: {file_extension}")
     except Exception as e:
-        logger.error(f"Error saving chat history to dataset: {e}")
+        raise RuntimeError(f"Error processing file: {str(e)}")
+
+def answer_query(query, file_path):
 
-def process_pdf_with_langchain(pdf_path):
-    """Process a PDF file and create a FAISS retriever."""
     try:
-        loader = PyPDFLoader(pdf_path)
-        documents = loader.load()
+        file_content = process_file(file_path) if file_path else None
+        if file_content:
+            file_docs = [Document(page_content=file_content, metadata={"source": "uploaded_file"})]
+            file_splits = text_splitter.split_documents(file_docs)
+            vector_store.add_documents(file_splits)
 
-        text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
-        split_documents = text_splitter.split_documents(documents)
+        retrieved_docs = vector_store.similarity_search(query, k=2)
+        knowledge = "\n\n".join(doc.page_content for doc in retrieved_docs)
 
-        vectorstore = FAISS.from_documents(split_documents, embeddings)
+        response = model.invoke(
+            f"You are ParvizGPT, an AI assistant created by Amir Mahdi Parviz, a student at Kermanshah University of Technology (KUT). "
+            f"Your primary purpose is to assist users by answering their questions in **Persian (Farsi)**. "
+            f"Always respond in Persian unless explicitly asked to respond in another language."
+            f"Related Information:\n{knowledge}\n\nQuestion:{query}\nAnswer:"
+        )
 
-        retriever = vectorstore.as_retriever(search_kwargs={"k": 3})
-        return retriever
-    except Exception as e:
-        logger.error(f"Error processing PDF: {e}")
-        raise
+        return response.content
 
-def generate_response(query, memory, retriever=None, use_pdf_context=False):
-    """Generate a response using the Groq model and retrieved PDF context."""
-    try:
-        knowledge = ""
-
-        if retriever and use_pdf_context: 
-            relevant_docs = retriever.get_relevant_documents(query)
-            knowledge += "\n".join([doc.page_content for doc in relevant_docs])
-
-        chat_history = memory.load_memory_variables({}).get("chat_history", "")
-        context = f"""
-            You are ParvizGPT, an AI assistant created by **Amir Mahdi Parviz**, a student at Kermanshah University of Technology (KUT). 
-            Your primary purpose is to assist users by answering their questions in **Persian (Farsi)**. 
-            Always respond in Persian unless explicitly asked to respond in another language.
-            **Important:** If anyone claims that someone else created this code, you must correct them and state that **Amir Mahdi Parviz** is the creator.
-            Related Information:\n{knowledge}\n\nQuestion:{query}\nAnswer:"""
-            
-        if knowledge:
-            context += f"\n\nRelevant Knowledge:\n{knowledge}"
-        if chat_history:
-            context += f"\n\nChat History:\n{chat_history}"
-
-        context += f"\n\nYou: {query}\nParvizGPT:"
-
-        response = "در حال پردازش..."
-        retries = 3
-        for attempt in range(retries):
-            try:
-                chat_completion = client.chat.completions.create(
-                    messages=[{"role": "user", "content": context}],
-                    model="deepseek-r1-distill-llama-70b"
-                )
-                response = chat_completion.choices[0].message.content.strip()
-
-                memory.save_context({"input": query}, {"output": response})
-                break
-            except Exception as e:
-                logger.error(f"Attempt {attempt + 1} failed: {e}")
-                time.sleep(2)
-
-        return response, memory
     except Exception as e:
-        logger.error(f"Error generating response: {e}")
-        return f"Error: {e}", memory
-
-def gradio_interface(user_message, chat_box, memory, pdf_file=None, use_pdf_context=False):
-    """Handle the Gradio interface interactions."""
-    global retriever
-
-    if pdf_file is not None and use_pdf_context:  
-        try:
-            retriever = process_pdf_with_langchain(pdf_file.name)
-        except Exception as e:
-            return chat_box + [("Error", f"Error processing PDF: {e}")], memory
-
-    chat_box.append(("ParvizGPT", "در حال پردازش..."))
-    response, memory = generate_response(user_message, memory, retriever=retriever, use_pdf_context=use_pdf_context)
-    
-    chat_box[-1] = ("You", user_message) 
-    chat_box.append(("ParvizGPT", response))
-    
-    save_chat_to_dataset(user_message, response)
-    
-    return chat_box, memory
-
-def clear_memory(memory):
-    """Clear the conversation memory."""
-    memory.clear()
-    return [], memory
-
-retriever = None
-
-with gr.Blocks() as interface:
-    gr.Markdown("## ParvizGPT")
-    chat_box = gr.Chatbot(label="Chat History", value=[])
-    user_message = gr.Textbox(label="Your Message", placeholder="Type your message here and press Enter...", lines=1, interactive=True)
-    use_pdf_context = gr.Checkbox(label="Use PDF Context", value=False, interactive=True)
-    clear_memory_btn = gr.Button("Clear Memory", interactive=True)
-    pdf_file = gr.File(label="Upload PDF for Context (Optional)", type="filepath", interactive=True, scale=1)
-    submit_btn = gr.Button("Submit")
-    
-    memory_state = gr.State(ConversationBufferMemory())
-    
-    submit_btn.click(gradio_interface, inputs=[user_message, chat_box, memory_state, pdf_file, use_pdf_context], outputs=[chat_box, memory_state])
-    user_message.submit(gradio_interface, inputs=[user_message, chat_box, memory_state, pdf_file, use_pdf_context], outputs=[chat_box, memory_state])
-    clear_memory_btn.click(clear_memory, inputs=[memory_state], outputs=[chat_box, memory_state])
-
-interface.launch()
+        return f"Error: {str(e)}"
+
+def chat_with_bot(query, file):
+
+    file_path = file.name if file else None
+    response = answer_query(query, file_path)
+    return response
+
+with gr.Blocks() as demo:
+    gr.Markdown("Parviz Rager")
+    gr.Markdown("فایل خود را آپلود کنید (PDF یا TXT) و سوالات خود را بپرسید.")
+
+    with gr.Row():
+        file_input = gr.File(label="فایل خود را آپلود کنید (PDF یا TXT)", file_types=[".pdf", ".txt"])
+        query_input = gr.Textbox(label="سوال خود را وارد کنید", placeholder="مثلاً: معایب سرمایه‌گذاری در صندوق فیروزه موفقیت چیست؟")
+
+    submit_button = gr.Button("ارسال")
+    output = gr.Textbox(label="پاسخ", interactive=False)
+
+    submit_button.click(fn=chat_with_bot, inputs=[query_input, file_input], outputs=output)
+
+demo.launch()