Upload app.py

app.py

@@ -32,7 +32,7 @@ def fetch_arxiv_summary(query, sort_by="relevance", sort_order="descending", max
                 summaries.append(f"Titel: {title.text.strip()}\nLink: {link.text.strip()}\nZusammenfassung: {summary.text.strip()}")
         return summaries if summaries else ["Keine relevanten Studien gefunden."]
     except Exception as e:
-        return [f"Fehler beim Abrufen der Studie: {str(e)}"]
+        return [f"Fehler beim Abrufen der Studie: {str(e),str(url)}"]
 
 # Chatbot-Logik mit arXiv-Integration
 def respond(
@@ -52,7 +52,7 @@ def respond(
     study_info = "\n".join(study_summaries)
 
     # Nachrichten vorbereiten
-    messages = [{"role": "system", "content": f"{system_message}\n"}]
+    messages = [{"role": "system", "content": f"{system_message} You are a highly capable assistant specializing in parsing and summarizing study abstracts. Your task is to analyze the provided study data, extract relevant information, and offer concise summaries. Always include the study's title and a direct link, ensuring clarity and accessibility.\n"}]
     for val in history:
         if val[0]:
             messages.append({"role": "user", "content": val[0]})
@@ -84,7 +84,7 @@ def create_intro_text():
 demo = gr.ChatInterface(
     respond,
     additional_inputs=[
-        gr.Textbox(value="You are a highly capable assistant specializing in parsing and summarizing study abstracts. Your task is to analyze the provided study data, extract relevant information, and offer concise summaries. Always include the study's title and a direct link, ensuring clarity and accessibility.", label="System message"),
+        gr.Textbox(value="You are helpful.", label="System message"),
         gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
         gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
         gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"),