Update app.py

app.py

@@ -1,63 +1,34 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    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": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", 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)",
-        ),
-    ],
+import pandas as pd
+import requests
+from io import StringIO
+
+# Dataset'inizi Hugging Face Hub'dan indirin
+def download_file(url):
+    response = requests.get(url)
+    response.raise_for_status()
+    return StringIO(response.text)
+
+dataset_url = "https://huggingface.co/datasets/ta2cay/caycuma_info/resolve/main/caycuma_info.csv"
+data = pd.read_csv(download_file(dataset_url))
+
+# Soruya cevap veren fonksiyon
+def answer_question(question):
+    response = data[data["Soru"].str.contains(question, case=False, na=False)]
+    if not response.empty:
+        return response.iloc[0]["Cevap"]
+    else:
+        return "Bu soruya dair bir bilgi bulamadım."
+
+# Gradio arayüzü
+iface = gr.Interface(
+    fn=answer_question,
+    inputs=gr.Textbox(lines=2, placeholder="Sorunuzu buraya yazın...", label="Soru"),
+    outputs="text",
+    title="Çaycuma Bilgi Modeli",
+    description="Çaycuma ile ilgili sorularınızı sorun",
+    theme="default"
 )
 
-
 if __name__ == "__main__":
-    demo.launch()
+    iface.launch()