Upload app.py

app.py

@@ -27,64 +27,59 @@ def fetch_arxiv_summary(query, sort_by="relevance", sort_order="descending", max
                 summaries.append(f"Title: {title.text.strip()}\n Link: {link}\n Abstract: {summary.text.strip()}")
         return summaries if summaries else ["No Studies found"]
     except Exception as e:
-        return [f"Fehler beim Abrufen der Studie: {str(e)}"]
-
-# Chatbot-Logik mit arXiv-Integration
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    sort_by,
-    sort_order,
-    max_results,
-    query
-):
-    # Query generieren und Studien abrufen
+        return [f"Error: {str(e)}"]
+
+
+# Funktion, um die Ergebnisse zu generieren
+def search_and_generate(query, sort_by, sort_order, max_results, system_message, max_tokens, temperature, top_p):
+    # Studieninformationen abrufen
     study_summaries = fetch_arxiv_summary(query, sort_by, sort_order, max_results)
     study_info = "\n".join(study_summaries)
 
-    # Nachrichten vorbereiten
-    #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"}]
-    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. The data will be provided as a list of strings, where each string contains details about a study in the following format: 'Title: [Study Title]\\nLink: [URL]\\nSummary: [Study Abstract]'. Process each entry separately, ensuring accuracy and readability in your summaries.\n"}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
 
-    messages.append({"role": "user", "content": f"{message}\nUse this Kontext (Studies):\n{study_info}"})
+    # Anfrage an das Modell
+    messages = [{"role": "system", "content": system_message},
+                {"role": "user", "content": f"Studies:\n{study_info}"}]
 
-    # Antwort vom Modell generieren
     response = ""
     for message in client.chat_completion(
         messages,
-        stream=True
+        max_tokens=max_tokens,
+        stream=True,
+        temperature=temperature,
+        top_p=top_p,
     ):
         token = message.choices[0].delta.content
         response += token
-        yield response
 
-# Gradio-Interface mit zusätzlichen Eingaben
+    return f"Studies:\n{study_info}\n\nAnswer of the modell:\n{response}"
+
+
+# Gradio-Interface mit Eingabe- und Ausgabefeldern
+def create_intro_text():
+    return ("Enter your specific query in the field below, and the bot will provide you with studies including the title, link, and summary.")
 
 with gr.Blocks() as demo:
-    gr.Markdown("""Helloooooo
-                This chatbot uses AI to answer your questions and retrieve relevant studies from the arXiv database.
-Enter your specific query in the field below, and the bot will provide you with studies including the title, link, and summary.
-    """)
-    
-
-    chat_interface = gr.ChatInterface(
-        respond,
-        additional_inputs=[
-            gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-            gr.Dropdown(label="Sortby", choices=["relevance", "lastUpdatedDate", "submittedDate"], value="relevance"),
-            gr.Dropdown(label="Sort", choices=["ascending", "descending"], value="descending"),
-            gr.Slider(label="MaxEntries", minimum=1, maximum=50, value=20, step=1),
-            gr.Textbox(value="", label="Query", placeholder="Enter your specific search term.")
-
-        ],
+    gr.Markdown(create_intro_text())
+    query_input = gr.Textbox(value="", label="Query (arxiv API)", placeholder="Geben Sie Ihren spezifischen Suchbegriff ein.")
+    sort_by = gr.Dropdown(label="Sortby", choices=["relevance", "lastUpdatedDate", "submittedDate"], value="relevance")
+    sort_order = gr.Dropdown(label="Sort", choices=["ascending", "descending"], value="descending")
+    max_results = gr.Slider(label="Max Entries", minimum=1, maximum=50, value=20, step=1)
+    system_message = 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. The data will be provided as a list of strings, where each string contains details about a study in the following format: 'Title: [Study Title]\\nLink: [URL]\\nSummary: [Study Abstract]'. Process each entry separately, ensuring accuracy and readability in your summaries.", label="Systemnachricht")
+    max_tokens = gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max Token")
+    temperature = gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature")
+    top_p = gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)")
+
+    output_box = gr.Textbox(label="Results", placeholder="Result...", lines=10)
+    search_button = gr.Button("Search")
+
+    search_button.click(
+        fn=search_and_generate,
+        inputs=[query_input, sort_by, sort_order, max_results, system_message, max_tokens, temperature, top_p],
+        outputs=output_box
     )
 
+
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()
+