Update app.py

app.py

@@ -1,12 +1,51 @@
+import os
+import tempfile
+import shutil
+import chromadb
+from langchain_community.document_loaders import PyPDFLoader
+from langchain_community.vectorstores import Chroma
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain.chains import RetrievalQA
+from langchain_community.llms import HuggingFaceHub
 import gradio as gr
 
-try:
-    from langchain_community.document_loaders import PyPDFLoader
-except ImportError:
-    from langchain.document_loaders import PyPDFLoader
+DB_DIR = "chroma_db"
+os.makedirs(DB_DIR, exist_ok=True)
 
-def dummy_func(pdf):
-    return "✅ تم رفع الملف:", pdf.name
+def load_and_index_pdf(pdf_file):
+    with tempfile.TemporaryDirectory() as tmpdir:
+        pdf_path = os.path.join(tmpdir, pdf_file.name)
+        shutil.copy(pdf_file.name, pdf_path)
+        loader = PyPDFLoader(pdf_path)
+        documents = loader.load_and_split()
+        embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+        vectordb = Chroma.from_documents(documents, embedding=embeddings, persist_directory=DB_DIR)
+        vectordb.persist()
+    return "✅ PDF تمت معالجته بنجاح! يمكنك الآن طرح الأسئلة."
 
-iface = gr.Interface(fn=dummy_func, inputs="file", outputs="text")
-iface.launch()
+def answer_question(question):
+    if not os.path.exists(DB_DIR) or not os.listdir(DB_DIR):
+        return "❌ الرجاء رفع ملف PDF أولًا."
+    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+    vectordb = Chroma(persist_directory=DB_DIR, embedding_function=embeddings)
+    retriever = vectordb.as_retriever()
+    llm = HuggingFaceHub(repo_id="mistralai/Mistral-7B-Instruct-v0.2", model_kwargs={"temperature": 0.5, "max_new_tokens": 512})
+    qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever)
+    return qa.run(question)
+
+with gr.Blocks() as demo:
+    gr.Markdown("## 🤖 Smart PDF Assistant - مساعدك الذكي في قراءة وفهم ملفات PDF")
+    
+    with gr.Tab("📁 تحميل PDF"):
+        pdf_input = gr.File(label="ارفع ملف PDF", file_types=[".pdf"])
+        upload_output = gr.Textbox(label="حالة المعالجة")
+        upload_btn = gr.Button("📄 معالجة الملف")
+        upload_btn.click(fn=load_and_index_pdf, inputs=pdf_input, outputs=upload_output)
+
+    with gr.Tab("❓ اسأل سؤالك"):
+        question = gr.Textbox(label="اكتب سؤالك هنا")
+        answer = gr.Textbox(label="الإجابة", lines=5)
+        ask_btn = gr.Button("🔍 إرسال السؤال")
+        ask_btn.click(fn=answer_question, inputs=question, outputs=answer)
+
+demo.launch()