Update app.py

app.py

@@ -4,57 +4,138 @@ 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")
+# 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}]
+from google.cloud import storage
+from google.oauth2 import service_account
+import json
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+# upload image to google cloud storage
+def upload_image_to_gcs_blob(image):
 
-    messages.append({"role": "user", "content": message})
+    google_creds = os.environ.get("GOOGLE_APPLICATION_CREDENTIALS_JSON")
+
+    creds_json = json.loads(google_creds)
+    credentials = service_account.Credentials.from_service_account_info(creds_json)
+
+    # 现在您可以使用这些凭证对Google Cloud服务进行认证
+    storage_client = storage.Client(credentials=credentials, project=creds_json['project_id'])
+
+    bucket_name=os.environ.get('bucket_name')
+    bucket = storage_client.bucket(bucket_name)
+    
+    destination_blob_name = os.path.basename(image)
+    blob = bucket.blob(destination_blob_name)
+
+    blob.upload_from_filename(image)
+
+    public_url = blob.public_url
+    
+    return public_url
+
+
+
+# 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
+
+def get_completion(message,history,system_message,max_tokens,temperature):
+    # base64_image = encode_image(image)
+    if message["text"].strip() == "" and not message["files"]:
+        gr.Error("Please input a query and optionally image(s).")
+    
+    if message["text"].strip() == "" and message["files"]:
+        gr.Error("Please input a text query along the image(s).")
+    
+    text = message['text']
+    content = [
+        {"type": "text", "text": text},
+    ]
+    if message['files']:
+        image = message['files'][0]
+        image_url = upload_image_to_gcs_blob(image)
+        content_image = {
+            "type": "image_url",
+            "image_url": {
+                "url": image_url,
+            },}
+    content.append(content_image)
+
+    init_message = [{"role": "system", "content": system_message}]
+    
+    history_openai_format = []
+    for human, assistant in history:
+        history_openai_format.append({"role": "user", "content": human })
+        history_openai_format.append({"role": "assistant", "content":assistant})
+    history_openai_format.append({"role": "user", "content": content})
+    
+    
+    # 请求头部信息
+    openai_api_key = os.environ.get('openai_api_key')
+    headers = {
+      'Authorization': f'Bearer {openai_api_key}'
+    }
+    
+    # 请求体信息
+    data = {
+        'model': 'gpt-4o',  # 可以根据需要更换其他模型
+        'messages': init_message + history_openai_format[-5:], #system message + 最近的2次對話 + 最新一條消息
+        'temperature': temperature,  # 可以根据需要调整
+        'max_tokens':max_tokens，
+        # 'stream':True，
+    }
+    
+    response = requests.post('https://burn.hair/v1/chat/completions', headers=headers, json=data)
+    
+    # 解析响应内容
+    response_data = response.json()
+    response_content = response_data['choices'][0]['message']['content']
+    usage = response_data['usage']
+    
+    return response_content
 
-    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,
+    get_completion,
+    multimodal=True，
     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)",
-        ),
     ],
 )