Update app.py

app.py

@@ -12,10 +12,7 @@ collection = client_chroma.get_or_create_collection(name=collection_name)
 # Verwende die integrierten Embeddings von ChromaDB
 embedding_function = embedding_functions.DefaultEmbeddingFunction()
 
-# Initialisiere das LLM
-client_llm = InferenceClient(
-    "mistralai/Mistral-7B-Instruct-v0.3"
-)
+
 #client_llm = Client("wasmdashai/meta-llama-Llama-3.2-3B-Instruct")
 
 def process_pdf(file):
@@ -57,12 +54,16 @@ def search_similar_documents(prompt):
     return "\n".join(formatted_results)
 
 def ask_llm(prompt):
+    #client = Client("KingNish/OpenGPT-4o")
     result = client_llm.predict(
         param_0=prompt,
-        api_name="/predict"
-    )
+        #user_prompt=prompt,
+		#user_prompt={"text":"","files":[]},
+		api_name="/chat"
+)
     return result
 
+
 # Erstelle die Gradio-Schnittstelle
 with gr.Blocks() as demo:
     gr.Markdown("# PDF Upload and Similarity Search with ChromaDB and LLM")