Create app.py

app.py

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+import gradio as gr
+import google.generativeai as genai
+import os
+import json
+import pandas as pd
+import tempfile
+from pdf2image import convert_from_path
+from pptx import Presentation
+from pptx.util import Inches, Pt
+from pptx.dml.color import RGBColor
+from huggingface_hub import HfApi, hf_hub_download
+from dotenv import load_dotenv
+
+# --- 設定與常數 ---
+load_dotenv()
+PROF_SAVE_FILE = "saved_professors.json"
+COMP_SAVE_FILE = "saved_companies.json"
+HF_TOKEN = os.getenv("HF_TOKEN")
+DATASET_REPO_ID = os.getenv("DATASET_REPO_ID")
+
+# ==========================================
+# 🧠 核心服務層 (The Logic / Chef)
+# ==========================================
+class UnifiedService:
+    def __init__(self, api_key_input=None):
+        self.api_key = self._get_api_key(api_key_input)
+        if self.api_key:
+            genai.configure(api_key=self.api_key)
+            # 使用支援 Google Search 的模型
+            self.model_name = "gemini-2.0-flash-exp" 
+
+    def _get_api_key(self, user_key):
+        if user_key and user_key.strip(): return user_key.strip()
+        system_key = os.getenv("GEMINI_API_KEY")
+        if system_key: return system_key
+        return None # 允許初始化時無 Key，但在使用功能時會噴錯
+
+    def _check_key(self):
+        if not self.api_key: raise ValueError("請先輸入 API Key 或設定系統環境變數")
+
+    # --- 1. PDF 轉 PPTX ---
+    def analyze_pdf_to_pptx(self, pdf_file, progress):
+        self._check_key()
+        model = genai.GenerativeModel(self.model_name)
+        prs = Presentation()
+        prs.slide_width = Inches(16); prs.slide_height = Inches(9)
+        
+        progress(0.1, desc="轉檔中...")
+        images = convert_from_path(pdf_file)
+        
+        for i, img in enumerate(images):
+            progress(0.1 + (0.8 * (i / len(images))), desc=f"分析第 {i+1} 頁...")
+            slide = prs.slides.add_slide(prs.slide_layouts[6])
+            
+            prompt = "Detect all text blocks. Return JSON: [{'text':..., 'box_2d':[ymin,xmin,ymax,xmax] (0-1000), 'font_size':int, 'is_bold':bool, 'color':hex}]"
+            try:
+                response = model.generate_content([prompt, img], generation_config={"response_mime_type": "application/json"})
+                blocks = json.loads(response.text)
+                for b in blocks:
+                    box = b.get("box_2d", [0,0,0,0])
+                    left, top = Inches((box[1]/1000)*16), Inches((box[0]/1000)*9)
+                    width, height = Inches(((box[3]-box[1])/1000)*16), Inches(((box[2]-box[0])/1000)*9)
+                    tx = slide.shapes.add_textbox(left, top, width, height)
+                    p = tx.text_frame.paragraphs[0]
+                    p.text = b.get("text",""); p.font.size = Pt(b.get("font_size", 12)); p.font.bold = b.get("is_bold", False)
+                    try: p.font.color.rgb = RGBColor.from_string(b.get("color", "#000000").replace("#",""))
+                    except: pass
+            except Exception as e: print(f"Page {i} err: {e}")
+            
+        out = tempfile.mktemp(suffix=".pptx")
+        prs.save(out)
+        return out, "✅ 轉換完成"
+
+    # --- 2. 圖片去字 ---
+    def remove_text(self, image):
+        self._check_key()
+        model = genai.GenerativeModel(self.model_name)
+        prompt = "Remove all text from this image, fill background naturally. Return image only."
+        resp = model.generate_content([prompt, image]) # V1 SDK 通常回傳 multipart，這裡簡化處理
+        # 注意: Gemini V1 SDK 在 Python 直接回傳 image 比較 tricky，若失敗建議檢查 SDK 版本
+        # 這裡假設環境支援直接回圖，若否則需用 requests 操作 REST API
+        try:
+            return resp.parts[0].image 
+        except:
+            return image # Fallback
+
+    # --- 3. 搜尋 (教授/公司) 共用邏輯 ---
+    def _search_with_google(self, query, prompt_template):
+        self._check_key()
+        # 這裡使用 Google Search Tool 設定
+        tools = [{"google_search": {}}]
+        model = genai.GenerativeModel(self.model_name, tools=tools)
+        
+        # Step 1: Search
+        resp1 = model.generate_content(prompt_template.format(query=query))
+        
+        # Step 2: Extract JSON (Pure Text Model)
+        model_extract = genai.GenerativeModel(self.model_name) # No tools for extraction
+        extract_prompt = f"Extract structured data from this text into JSON array: {resp1.text}"
+        resp2 = model_extract.generate_content(extract_prompt, generation_config={"response_mime_type": "application/json"})
+        try: return json.loads(resp2.text)
+        except: return []
+
+    def search_professors(self, query):
+        p = "Find 10 prominent professors in Taiwan for '{query}'. Return name, university, department."
+        return self._search_with_google(query, p)
+
+    def search_companies(self, query):
+        p = "Find 5-10 Taiwanese companies for '{query}'. Return name, industry."
+        return self._search_with_google(query, p)
+
+    def get_details(self, data, role):
+        self._check_key()
+        tools = [{"google_search": {}}]
+        model = genai.GenerativeModel(self.model_name, tools=tools)
+        prompt = f"Act as {role}. Investigate: {json.dumps(data)}. Report in Traditional Chinese Markdown."
+        resp = model.generate_content(prompt)
+        
+        # 處理來源引用 (V1 SDK)
+        sources = []
+        if hasattr(resp.candidates[0], 'grounding_metadata'):
+            chunks = resp.candidates[0].grounding_metadata.grounding_chunks
+            for c in chunks:
+                if c.web: sources.append({"title": c.web.title, "uri": c.web.uri})
+        
+        # 去重
+        unique_sources = list({v['uri']:v for v in sources}.values())
+        return {"text": resp.text, "sources": unique_sources}
+
+    def chat(self, hist, msg, context, role):
+        self._check_key()
+        model = genai.GenerativeModel(self.model_name)
+        chat = model.start_chat(history=[
+            {"role": "user" if h[0] else "model", "parts": [h[0] or h[1]]} for h in hist
+        ])
+        full_msg = f"Context: {context}\nInstruction: {role}\nUser: {msg}"
+        resp = chat.send_message(full_msg)
+        return resp.text
+
+# ==========================================
+# 💾 資料存取層 (Persistence)
+# ==========================================
+def load_data(filename):
+    if HF_TOKEN and DATASET_REPO_ID:
+        try: hf_hub_download(repo_id=DATASET_REPO_ID, filename=filename, repo_type="dataset", token=HF_TOKEN, local_dir=".")
+        except: pass
+    if os.path.exists(filename):
+        try: 
+            with open(filename, 'r', encoding='utf-8') as f: return json.load(f)
+        except: pass
+    return []
+
+def save_data(data, filename):
+    with open(filename, 'w', encoding='utf-8') as f: json.dump(data, f, ensure_ascii=False, indent=2)
+    if HF_TOKEN and DATASET_REPO_ID:
+        try:
+            api = HfApi(token=HF_TOKEN)
+            api.upload_file(path_or_fileobj=filename, path_in_repo=filename, repo_id=DATASET_REPO_ID, repo_type="dataset", commit_message="Sync")
+        except: pass
+
+# ==========================================
+# 🖥️ 介面邏輯 (UI Helpers)
+# ==========================================
+def format_df(data_list, cols):
+    if not data_list: return pd.DataFrame(columns=cols)
+    res = []
+    for d in data_list:
+        icon = {'match':'✅','good':'✅','risk':'⚠️'}.get(d.get('status'),'')
+        res.append([f"{icon} {d.get('name')}", d.get('university') or d.get('industry'), ", ".join(d.get('tags',[]))])
+    return pd.DataFrame(res, columns=cols)
+
+# ==========================================
+# 🚀 主程式 (Gradio)
+# ==========================================
+def main_app():
+    # 初始化
+    prof_data = load_data(PROF_SAVE_FILE)
+    comp_data = load_data(COMP_SAVE_FILE)
+
+    with gr.Blocks(title="Prof.404 x PPT.404 Ultimate", theme=gr.themes.Soft()) as demo:
+        
+        # 全域 Key
+        with gr.Accordion("🔑 系統設定 (API Key)", open=False):
+            api_key = gr.Textbox(label="Google Gemini API Key", type="password", placeholder="若未填寫則使用系統預設")
+
+        gr.Markdown(
+            """
+            <div align="center">
+            <h1>🚀 Prof.404 Ultimate: 產學導航 & 文件工具站</h1>
+            <h3>整合文件視覺處理 (PPT/Img) 與 產學資源導航 (Prof/Com) 的全方位平台</h3>
+            </div>
+            """
+        )
+
+        with gr.Tabs():
+            
+            # --- Tab 1: 工具箱 ---
+            with gr.Tab("🛠️ 文件工具箱 (PPT.404)"):
+                with gr.Row():
+                    with gr.Column():
+                        gr.Markdown("### 📄 PDF 轉 PPTX (含排版還原)")
+                        pdf_file = gr.File(label="上傳 PDF")
+                        pdf_btn = gr.Button("開始轉換", variant="primary")
+                        ppt_out = gr.File(label="下載 PPTX")
+                        pdf_msg = gr.Textbox(label="狀態", interactive=False)
+                        
+                        pdf_btn.click(
+                            lambda f, k: UnifiedService(k).analyze_pdf_to_pptx(f, gr.Progress()),
+                            inputs=[pdf_file, api_key], outputs=[ppt_out, pdf_msg]
+                        )
+
+                    with gr.Column():
+                        gr.Markdown("### 🎨 圖片智慧去字")
+                        img_in = gr.Image(type="pil", label="原圖")
+                        img_btn = gr.Button("一鍵去除", variant="primary")
+                        img_out = gr.Image(label="結果")
+                        
+                        img_btn.click(
+                            lambda i, k: UnifiedService(k).remove_text(i),
+                            inputs=[img_in, api_key], outputs=[img_out]
+                        )
+
+            # --- Tab 2: 找教授 ---
+            with gr.Tab("🎓 找教授 (Prof.404)"):
+                p_state = gr.State(prof_data)
+                p_current = gr.State(None) # 當前選中的教授
+                
+                with gr.Row():
+                    p_query = gr.Textbox(label="搜尋領域", scale=4)
+                    p_btn = gr.Button("搜尋", scale=1)
+                
+                with gr.Row():
+                    p_table = gr.Dataframe(headers=["姓名", "大學", "標籤"], interactive=False, scale=1)
+                    with gr.Column(scale=1, visible=False) as p_detail_col:
+                        p_md = gr.Markdown()
+                        p_chat = gr.Chatbot(height=300)
+                        p_msg = gr.Textbox(label="詢問關於此教授")
+                
+                # Logic Wrappers
+                def search_p(q, k, saved):
+                    svc = UnifiedService(k)
+                    res = svc.search_professors(q)
+                    return res, format_df(res, ["姓名","大學","標籤"])
+
+                def select_p(evt: gr.SelectData, res, k, saved):
+                    svc = UnifiedService(k)
+                    item = res[evt.index[0]]
+                    # 取得詳細資料
+                    det = svc.get_details(item, "Academic Consultant")
+                    item['details'] = det['text']
+                    # 簡易儲存邏輯 (為了Demo簡化，實際建議加上去重)
+                    saved.append(item) 
+                    save_data(saved, PROF_SAVE_FILE)
+                    
+                    display_text = det['text'] + "\n\n📚 來源:\n" + "\n".join([f"- {s['title']}" for s in det['sources']])
+                    return gr.update(visible=True), display_text, [], item, saved
+
+                def chat_p(hist, msg, item, k):
+                    svc = UnifiedService(k)
+                    reply = svc.chat(hist, msg, item.get('details'), "Academic Consultant")
+                    hist.append((msg, reply))
+                    return hist, ""
+
+                p_btn.click(search_p, [p_query, api_key, p_state], [p_state, p_table])
+                p_table.select(select_p, [p_state, api_key, p_state], [p_detail_col, p_md, p_chat, p_current, p_state])
+                p_msg.submit(chat_p, [p_chat, p_msg, p_current, api_key], [p_chat, p_msg])
+
+            # --- Tab 3: 找公司 ---
+            with gr.Tab("🏢 找公司 (Com.404)"):
+                c_state = gr.State(comp_data)
+                c_current = gr.State(None)
+                
+                with gr.Row():
+                    c_query = gr.Textbox(label="搜尋產業/公司", scale=4)
+                    c_btn = gr.Button("搜尋", scale=1)
+                
+                with gr.Row():
+                    c_table = gr.Dataframe(headers=["公司", "產業", "標籤"], interactive=False, scale=1)
+                    with gr.Column(scale=1, visible=False) as c_detail_col:
+                        c_md = gr.Markdown()
+                        c_chat = gr.Chatbot(height=300)
+                        c_msg = gr.Textbox(label="詢問關於此公司")
+
+                # Logic Wrappers (Similar structure)
+                def search_c(q, k, saved):
+                    svc = UnifiedService(k)
+                    res = svc.search_companies(q)
+                    return res, format_df(res, ["公司","產業","標籤"])
+
+                def select_c(evt: gr.SelectData, res, k, saved):
+                    svc = UnifiedService(k)
+                    item = res[evt.index[0]]
+                    det = svc.get_details(item, "Business Analyst")
+                    item['details'] = det['text']
+                    saved.append(item)
+                    save_data(saved, COMP_SAVE_FILE)
+                    
+                    display_text = det['text'] + "\n\n📚 來源:\n" + "\n".join([f"- {s['title']}" for s in det['sources']])
+                    return gr.update(visible=True), display_text, [], item, saved
+
+                def chat_c(hist, msg, item, k):
+                    svc = UnifiedService(k)
+                    reply = svc.chat(hist, msg, item.get('details'), "Business Analyst")
+                    hist.append((msg, reply))
+                    return hist, ""
+
+                c_btn.click(search_c, [c_query, api_key, c_state], [c_state, c_table])
+                c_table.select(select_c, [c_state, api_key, c_state], [c_detail_col, c_md, c_chat, c_current, c_state])
+                c_msg.submit(chat_c, [c_chat, c_msg, c_current, api_key], [c_chat, c_msg])
+
+    demo.queue().launch()
+
+if __name__ == "__main__":
+    main_app()