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README.md

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 ---
 title: Gemini PDF
-emoji: 🏃
+emoji: 🏢
 colorFrom: gray
-colorTo: pink
-sdk: streamlit
-sdk_version: 1.40.1
+colorTo: yellow
+sdk: gradio
+sdk_version: 5.4.0
 app_file: app.py
 pinned: false
+license: other
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import os
+import time
+import gradio as gr
+import google.generativeai as genai
+
+# 設定 Gemini API KEY
+def configure_api_key(api_key):
+    genai.configure(api_key=api_key)
+    print("API Key 已配置成功")
+
+# 上傳 PDF 並等待處理完成
+def upload_and_process_pdf(file_path, mime_type="application/pdf"):
+    print("上傳 PDF 中...")
+    pdf_file = genai.upload_file(file_path, mime_type=mime_type)
+    print(f"PDF '{pdf_file.display_name}' 上傳成功，URI 為: {pdf_file.uri}")
+    
+    # 等待 PDF 處理完成
+    while pdf_file.state.name == "PROCESSING":
+        print("等待 PDF 處理中...")
+        time.sleep(10)
+        pdf_file = genai.get_file(pdf_file.name)
+
+    if pdf_file.state.name == "FAILED":
+        raise ValueError("PDF 處理失敗。")
+    
+    print(f"PDF 處理完成: {pdf_file.uri}")
+    return pdf_file
+
+# 使用 PDF 的 URI 來生成描述
+def generate_pdf_summary(api_key, pdf_file_path, prompt="仔細讀檔，彙整重點。繁體中文"):
+    configure_api_key(api_key)
+
+    # 上傳並處理 PDF
+    try:
+        pdf_file = upload_and_process_pdf(pdf_file_path)
+    except Exception as e:
+        return f"PDF 上傳或處理失敗：{e}", None
+
+    # 設定模型
+    model = genai.GenerativeModel(model_name="models/gemini-1.5-pro-002")
+
+    # 發送 LLM 推理請求
+    try:
+        print("Making LLM inference request...")
+        response = model.generate_content(
+            [prompt, pdf_file],
+            request_options={"timeout": 600}
+        )
+        # 保存模型回應到文件
+        output_file_path = "/tmp/model_response.txt"
+        with open(output_file_path, "w") as f:
+            f.write(response.text)
+        return response.text, output_file_path
+    except Exception as e:
+        return f"與模型對話時發生錯誤：{e}", None
+
+# Gradio 介面
+with gr.Blocks() as demo:
+    gr.Markdown("### 上傳 PDF 並生成摘要（Gemini）")
+
+    # API Key 輸入
+    api_key_input = gr.Textbox(
+        label="輸入 API Key",
+        placeholder="請輸入您的 Gemini API Key",
+        type="password"
+    )
+
+    # PDF 上傳
+    pdf_input = gr.File(
+        label="上傳 PDF",
+        type="filepath",
+    )
+
+    # 描述提示輸入
+    prompt = gr.Textbox(
+        label="摘要提示",
+        placeholder="默認為 '仔細讀檔，彙整重點。繁體中文'",
+        lines=5
+    )
+
+    # 提交按鈕
+    submit_button = gr.Button("提交")
+
+    # 輸出文字框和下載文件按鈕
+    output_text = gr.Textbox(
+        label="模型回應",
+        placeholder="模型的回應將出現在這裡",
+        lines=10
+    )
+    download_button = gr.File(
+        label="下載模型回應",
+        interactive=False
+    )
+
+    # 設置提交按鈕的函數調用
+    def handle_generate_summary(api_key, pdf_file, prompt):
+        text_response, file_path = generate_pdf_summary(api_key, pdf_file, prompt)
+        return text_response, file_path
+
+    # 提交按鈕的處理
+    submit_button.click(
+        handle_generate_summary,
+        inputs=[api_key_input, pdf_input, prompt],
+        outputs=[output_text, download_button]
+    )
+
+# 啟動 Gradio 介面
+demo.launch()

gitattributes

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+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
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+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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requirements.txt

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+gradio==3.1.4  # 根據您的需求使用最新的相容版本
+google-generativeai  # 調整為正確的套件名稱，若有不同
+httpx==0.23.0  # 指定相容版本以防止依賴衝突
+httpcore==0.15.0  # 指定相容版本以防止依賴衝突