Update app.py

app.py

@@ -1,10 +1,10 @@
 import gradio as gr
 from langchain_community.document_loaders import YoutubeLoader
 from langchain.text_splitter import TokenTextSplitter
-from groq import Groq
+import anthropic
 import os
 
-client = Groq(
+client = anthropic.Anthropic(
     api_key=os.environ.get("api_key"),
 )
 max_textboxes = 5
@@ -29,38 +29,38 @@ def process_youtube_url(url, language):
         output_textboxes += ["" for _ in range(max_textboxes - len(chunks))]
         yield *output_textboxes, [], text, token_count,""
         
-        completion = client.chat.completions.create(
-            model="llama3-70b-8192",
+        with client.messages.stream(
             messages=[
-                {
-                    "role": "system",
-                    "content": "lang:日本語 あなたは日本語話者の解説系Youtuberです。"
-                },
                 {
                     "role": "assistant",
-                    "content": "わたしは日本語話者の解説系Youtuberです。"
+                    "content": [
+                        {
+                            "type": "text",
+                            "text": "わたしは日本語話者の解説系Youtuberです。"
+                        }
+                    ]
                 },
                 {
                     "role": "user",
-                    "content": f"lang:日本語　日本語で次のtranscriptを解説して。長くなってもいいよ\n\n## trascript \n```{text}```"
-                },
-                {
-                    "role": "assistant",
-                    "content": f"この動画は、"
-                },
+                    "content": [
+                        {
+                            "type": "text",
+                            "text": f"lang:日本語　日本語で次のtranscriptを解説して。長くなってもいいよ\n\n## trascript \n```{text}```"
+                        }
+                    ]
+                }
             ],
+            system="lang:日本語 あなたは日本語話者の解説系Youtuberです。",
+            model="claude-3-haiku-20240307",
+            max_tokens=4096,
             temperature=0.7,
-            max_tokens=1024,
-            top_p=1,
-            stream=True,
-            stop=None,
-        )
-        summirizedtext = ""
-        for chunk in completion:
-            summirizedtext += chunk.choices[0].delta.content or ""
-            # print(chunk.choices[0].delta.content or "", end="")
+        ) as stream:
+            summirizedtext = ""
+            for text in stream.text_stream:
+                summirizedtext += text
+                # print(text, end="")
         
-            yield *output_textboxes, [], text, token_count,summirizedtext
+                yield *output_textboxes, [], text, token_count, summirizedtext
     
     except Exception as e:
         error_msg = str(e)