Update app.py

app.py

@@ -3,6 +3,92 @@ import chromadb
 from chromadb.utils import embedding_functions
 from PyPDF2 import PdfReader
 from gradio_client import Client
+from chromadb.config import DEFAULT_DATABASE, DEFAULT_TENANT
+
+# Initialisiere ChromaDB
+#client_chroma = chromadb.Client()
+client_croma = chromadb.PersistentClient(path="./chroma", settings=None,tenant=DEFAULT_TENANT,database=DEFAULT_DATABASE)
+
+collection_name = "pdf_collection"
+collection = client_chroma.get_or_create_collection(name=collection_name)
+
+# Verwende die integrierten Embeddings von ChromaDB
+embedding_function = embedding_functions.DefaultEmbeddingFunction()
+
+client = Client("Qwen/Qwen2.5-72B-Instruct")
+def ask_llm(llm_prompt_input): 
+    result = client.predict(
+		query=f"{llm_prompt_input}",
+		history=[],
+		system="You are Qwen, created by Alibaba Cloud. You are a helpful assistant.",
+		api_name="/model_chat"
+)
+    print(result)
+    return result
+   
+def process_pdf(file):
+    # Lese den PDF-Inhalt
+    pdf_reader = PdfReader(file.name)
+    text = ""
+    for page in pdf_reader.pages:
+        text += page.extract_text()
+    
+    # Erstelle Embedding
+    embedding = embedding_function([text])[0]
+    
+    # Speichere das PDF in ChromaDB
+    collection.add(
+        documents=[text],
+        metadatas=[{"filename": file.name}],
+        ids=[file.name]  # Verwende den Dateinamen als ID
+    )
+    
+    return f"PDF {file.name} wurde erfolgreich in ChromaDB gespeichert."
+
+def search_similar_documents(prompt):
+    # Erstelle Embedding für den Prompt
+    query_embedding = embedding_function([prompt])[0]
+    
+    # Führe die Ähnlichkeitssuche durch
+    results = collection.query(
+        query_embeddings=[query_embedding],
+        n_results=3
+    )
+    
+    # Formatiere die Ergebnisse
+    formatted_results = []
+    for i, doc in enumerate(results["documents"][0]):
+        metadata = results["metadatas"][0][i]
+        filename = metadata["filename"]
+        formatted_results.append(f"{doc}\n")
+    
+    return "\n".join(formatted_results)
+   
+# Erstelle die Gradio-Schnittstelle
+with gr.Blocks() as demo:
+    gr.Markdown("# PDF Upload and Similarity Search with ChromaDB and LLM")   
+    with gr.Row():
+        file_input = gr.File(label="Wähle eine PDF-Datei aus", type="filepath")
+        upload_output = gr.Textbox(label="Upload Status")
+    with gr.Row():
+        submit_button = gr.Button("upload")
+    with gr.Row():
+        prompt_input = gr.Textbox(label="Suche nach ähnlichen Dokumenten", placeholder="Gib einen Suchbegriff ein")
+        search_output = gr.Textbox(label="Ähnliche Dokumente")
+    with gr.Row():
+        search_button = gr.Button("Suchen")
+    with gr.Row():
+        llm_prompt_input = gr.Textbox(label="Frage an das LLM", placeholder="Gib eine Frage ein")
+        llm_output = gr.Textbox(label="LLM Antwort")
+    with gr.Row():
+        llm_submit_button = gr.Button("send")
+    
+    submit_button.click(process_pdf, inputs=file_input, outputs=upload_output)
+    search_button.click(search_similar_documents, inputs=prompt_input, outputs=search_output)
+    llm_submit_button.click(ask_llm, inputs=llm_prompt_input, outputs=llm_output)
+
+# Starte die Gradio-Anwendung
+demo.launch()
 
 # Initialisiere ChromaDB
 client_chroma = chromadb.Client()