Update backend.py

backend.py

@@ -1,214 +1,216 @@
-# ==========================================
-# FinTalk - Backend (llama-cpp + GPT summary)
-# ==========================================
-
-import os
-import re
-from typing import Dict
-from dotenv import load_dotenv
-from llama_cpp import Llama
-from openai import OpenAI
-import edge_tts
-import asyncio
-import reportlab
-
-load_dotenv()  
-
-# -----------------------------------------------------
-# 1) MODEL YOLU (GGUF) - KENDİ YOLUNU YAZ
-# -----------------------------------------------------
-model_path = r"C:/Users/cagri/.lmstudio/models/QuantFactory/Llama-3-8B-Instruct-Finance-RAG-GGUF/Llama-3-8B-Instruct-Finance-RAG.Q4_K_S.gguf"
-# -----------------------------------------------------
-# 2) LLAMA-CPP MODEL YÜKLEME
-#    n_ctx=4096 yeterli; 8192 istersen RAM artar. Uyarı görürsen önemsemeyebilirsin.
-# -----------------------------------------------------
-llm = Llama(
-    model_path=model_path,
-    n_ctx=4096,
-    n_threads=6,   
-    n_batch=512,
-    verbose=False
-)
-
-# -----------------------------------------------------
-# 3) OPENAI (Özetleme)
-# -----------------------------------------------------
-OPENAI_API_KEY = os.getenv("API_KEY")
-if not OPENAI_API_KEY:
-    raise RuntimeError("API_KEY bulunamadı. Lütfen .env veya sistem değişkenlerine ekleyin.")
-client = OpenAI(api_key=OPENAI_API_KEY)
-SUMMARY_MODEL = os.getenv("SUMMARY_MODEL", "gpt-4o-mini")
-
-# -----------------------------------------------------
-# 4) PERSONA PROMPTLAR (sade)
-# -----------------------------------------------------
-SYSTEM_MODERATOR = (
-    "You are Selin, the moderator of an economics roundtable. "
-    "Be neutral, brief, and structured. Guide the flow without giving opinions."
-)
-
-SYSTEM_BULLISH = (
-   """You are Bullish Investor, an optimistic economist who focuses on growth, market confidence, and positive catalysts.
-    Be analytical and persuasive. Mention at least two concrete macro or market factors that support your optimism 
-    (e.g., improved investor sentiment, fiscal stimulus, or sector resilience). 
-    Respond in 2–3 detailed paragraphs and conclude with one confident takeaway."""
-)
-
-SYSTEM_BEARISH = (
-    "You are Bearish Economist, a cautious macroeconomist who highlights downside risks "
-    "(inflation persistence, liquidity stress, policy uncertainty). Be analytical; end with one cautionary insight."
-)
-
-# -----------------------------------------------------
-# 5) YARDIMCI: Post-process (meta notları, personayı ifşa eden satırları temizle)
-# -----------------------------------------------------
-_META_PATTERNS = [
-    r"(?i)\bnote:\b.*",                 # "Note:" ile başlayan meta
-    r"(?i)\bi am (selin|bullish|bearish).*$",  # "I am ..." persona ifşaları
-    r"(?i)\bthis response was written\b.*",
-    r"(?i)\bplease review\b.*",
-    r"(?i)\bclarity and readability\b.*",
-]
-def _clean(text: str) -> str:
-    cleaned = text.strip()
-    for pat in _META_PATTERNS:
-        cleaned = re.sub(pat, "", cleaned, flags=re.MULTILINE)
-    # aşırı boşlukları toparla
-    cleaned = re.sub(r"\n{3,}", "\n\n", cleaned).strip()
-    return cleaned
-
-# -----------------------------------------------------
-# 6) TEK PERSONA CEVABI (chat completion + context reset)
-# -----------------------------------------------------
-def generate_as(system_prompt: str, user_text: str, max_tokens: int = 480, temperature: float = 0.7) -> str:
-    """
-    Her çağrıda temiz context: create_chat_completion kullanıyoruz.
-    """
-    # olası KV cache etkisini azaltmak için reset
-    llm.reset()
-    out = llm.create_chat_completion(
-        messages=[
-            {"role": "system", "content": system_prompt},
-            {"role": "user", "content": user_text}
-        ],
-        max_tokens=max_tokens,
-        temperature=temperature,
-        top_p=0.9,
-        repeat_penalty=1.1,
-    )
-    text = out["choices"][0]["message"]["content"]
-    return _clean(text)
-
-# -----------------------------------------------------
-# 7) TARTIŞMA AKIŞI
-# -----------------------------------------------------
-def fintalk_discussion(news_text: str) -> Dict[str, str]:
-    print("🧩 FinTalk simulation started...\n")
-
-    # Ortak mesaj zinciri (tek context)
-    messages = []
-
-    # 1️⃣ Selin başlatıyor
-    selin_intro = generate_as(SYSTEM_MODERATOR, f"Open the discussion about: {news_text}.")
-    messages.append(f"Selin: {selin_intro}")
-    print("Moderator Intro:\n", selin_intro, "\n")
-
-    # 2️⃣ Bullish konuşuyor
-    bullish_view = generate_as(
-        SYSTEM_BULLISH,
-        f"The moderator introduced the topic: {news_text}. Respond with your opening bullish perspective."
-    )
-    messages.append(f"Bullish Investor: {bullish_view}")
-    print("Bullish Investor:\n", bullish_view, "\n")
-
-    # 3️⃣ Bearish karşılık veriyor
-    bearish_view = generate_as(
-        SYSTEM_BEARISH,
-        f"The moderator introduced the topic: {news_text}. "
-        f"The bullish economist said: {bullish_view}\n"
-        "Now respond with your cautious analysis."
-    )
-    messages.append(f"Bearish Economist: {bearish_view}")
-    print("Bearish Economist:\n", bearish_view, "\n")
-
-    # 4️⃣ Selin toparlıyor (konuya referans ver)
-    selin_wrap = generate_as(
-        SYSTEM_MODERATOR,
-        f"Based on the debate about {news_text}, summarize their main differences and close the panel politely."
-    )
-    messages.append(f"Selin: {selin_wrap}")
-    print("Moderator Wrap-up:\n", selin_wrap, "\n")
-
-    # 5️⃣ GPT özetleme
-    debate_text = "\n".join(messages)
-    summary_prompt = (
-        "Summarize this debate between a bullish and a bearish economist in 5 bullet points. "
-        "Keep it grounded in the topic and add a balanced conclusion.\n\n"
-        f"{debate_text}"
-    )
-
-    summary_resp = client.chat.completions.create(
-        model=SUMMARY_MODEL,
-        messages=[
-            {"role": "system", "content": "You are an expert economic summarizer."},
-            {"role": "user", "content": summary_prompt}
-        ]
-    )
-    final_summary = summary_resp.choices[0].message.content.strip()
-    print("📊 GPT Summary:\n", final_summary)
-
-    return {
-        "moderator_intro": selin_intro,
-        "bullish_view": bullish_view,
-        "bearish_view": bearish_view,
-        "moderator_wrap": selin_wrap,
-        "summary": final_summary
-    }
-
-def export_to_pdf(result: dict, filename="FinTalk_Report.pdf"):
-    from reportlab.lib.pagesizes import A4
-    from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer
-    from reportlab.lib.styles import getSampleStyleSheet
-
-    styles = getSampleStyleSheet()
-    doc = SimpleDocTemplate(filename, pagesize=A4)
-    story = []
-
-    def add(title, text):
-        story.append(Paragraph(f"<b>{title}</b>", styles["Heading3"]))
-        story.append(Paragraph(text.replace("\n", "<br/>"), styles["BodyText"]))
-        story.append(Spacer(1, 12))
-
-    add("Topic", result.get("topic", "—"))
-    add("Moderator Intro", result["moderator_intro"])
-    add("Bullish Investor", result["bullish_view"])
-    add("Bearish Economist", result["bearish_view"])
-    add("Moderator Wrap-up", result["moderator_wrap"])
-    add("GPT-4 Summary", result["summary"])
-
-    story.append(Paragraph("<i>Generated by FinTalk – AI Economic Roundtable</i>", styles["Normal"]))
-    doc.build(story)
-
-async def generate_tts_files(result):
-    voices = {
-        "moderator_intro": "en-US-AriaNeural",
-        "bullish_view": "en-US-GuyNeural",
-        "bearish_view": "en-GB-RyanNeural",
-        "moderator_wrap": "en-US-AriaNeural"
-    }
-    for key, voice in voices.items():
-        filename = f"{key}.mp3"
-        text = result[key]
-        await edge_tts.Communicate(text, voice=voice, rate="+0%").save(filename)
-        print(f"✅ {filename} oluşturuldu")
-
-# -----------------------------------------------------
-# 8) Hızlı Test
-# -----------------------------------------------------
-if __name__ == "__main__":
-    topic = input("What's discussion topic ?\n>")
-    result = fintalk_discussion(topic)
-    result["topic"] = topic
-    export_to_pdf(result)
-    asyncio.run(generate_tts_files(result))
-    
+# ==========================================
+# FinTalk - Backend (llama-cpp + GPT summary)
+# ==========================================
+
+import os
+import re
+from typing import Dict
+from llama_cpp import Llama
+from openai import OpenAI
+import edge_tts
+import asyncio
+import reportlab
+from huggingface_hub import hf_hub_download
+  
+
+# -----------------------------------------------------
+# 1) MODEL YOLU (GGUF) - KENDİ YOLUNU YAZ
+# -----------------------------------------------------
+MODEL_REPO = "QuantFactory/Llama-3-8B-Instruct-Finance-RAG-GGUF"
+MODEL_FILE = "Llama-3-8B-Instruct-Finance-RAG.Q4_K_S.gguf"  
+
+model_path = hf_hub_download(repo_id=MODEL_REPO, filename=MODEL_FILE)
+
+# -----------------------------------------------------
+# 2) LLAMA-CPP MODEL YÜKLEME
+# -----------------------------------------------------
+llm = Llama(
+    model_path=model_path,
+    n_ctx=4096,
+    n_threads=6,   
+    n_batch=512,
+    verbose=False
+)
+
+# -----------------------------------------------------
+# 3) OPENAI (Özetleme)
+# -----------------------------------------------------
+OPENAI_API_KEY = os.getenv("API_KEY")
+if not OPENAI_API_KEY:
+    raise RuntimeError("API_KEY bulunamadı. Lütfen .env veya sistem değişkenlerine ekleyin.")
+client = OpenAI(api_key=OPENAI_API_KEY)
+SUMMARY_MODEL = os.getenv("SUMMARY_MODEL", "gpt-4o-mini")
+
+# -----------------------------------------------------
+# 4) PERSONA PROMPTLAR (sade)
+# -----------------------------------------------------
+SYSTEM_MODERATOR = (
+    "You are Selin, the moderator of an economics roundtable. "
+    "Be neutral, brief, and structured. Guide the flow without giving opinions."
+)
+
+SYSTEM_BULLISH = (
+   """You are Bullish Investor, an optimistic economist who focuses on growth, market confidence, and positive catalysts.
+    Be analytical and persuasive. Mention at least two concrete macro or market factors that support your optimism 
+    (e.g., improved investor sentiment, fiscal stimulus, or sector resilience). 
+    Respond in 2–3 detailed paragraphs and conclude with one confident takeaway."""
+)
+
+SYSTEM_BEARISH = (
+    "You are Bearish Economist, a cautious macroeconomist who highlights downside risks "
+    "(inflation persistence, liquidity stress, policy uncertainty). Be analytical; end with one cautionary insight."
+)
+
+# -----------------------------------------------------
+# 5) YARDIMCI: Post-process (meta notları, personayı ifşa eden satırları temizle)
+# -----------------------------------------------------
+_META_PATTERNS = [
+    r"(?i)\bnote:\b.*",                 # "Note:" ile başlayan meta
+    r"(?i)\bi am (selin|bullish|bearish).*$",  # "I am ..." persona ifşaları
+    r"(?i)\bthis response was written\b.*",
+    r"(?i)\bplease review\b.*",
+    r"(?i)\bclarity and readability\b.*",
+]
+def _clean(text: str) -> str:
+    cleaned = text.strip()
+    for pat in _META_PATTERNS:
+        cleaned = re.sub(pat, "", cleaned, flags=re.MULTILINE)
+    # aşırı boşlukları toparla
+    cleaned = re.sub(r"\n{3,}", "\n\n", cleaned).strip()
+    return cleaned
+
+# -----------------------------------------------------
+# 6) TEK PERSONA CEVABI (chat completion + context reset)
+# -----------------------------------------------------
+def generate_as(system_prompt: str, user_text: str, max_tokens: int = 480, temperature: float = 0.7) -> str:
+    """
+    Her çağrıda temiz context: create_chat_completion kullanıyoruz.
+    """
+    # olası KV cache etkisini azaltmak için reset
+    llm.reset()
+    out = llm.create_chat_completion(
+        messages=[
+            {"role": "system", "content": system_prompt},
+            {"role": "user", "content": user_text}
+        ],
+        max_tokens=max_tokens,
+        temperature=temperature,
+        top_p=0.9,
+        repeat_penalty=1.1,
+    )
+    text = out["choices"][0]["message"]["content"]
+    return _clean(text)
+
+# -----------------------------------------------------
+# 7) TARTIŞMA AKIŞI
+# -----------------------------------------------------
+def fintalk_discussion(news_text: str) -> Dict[str, str]:
+    print("🧩 FinTalk simulation started...\n")
+
+    # Ortak mesaj zinciri (tek context)
+    messages = []
+
+    # 1️⃣ Selin başlatıyor
+    selin_intro = generate_as(SYSTEM_MODERATOR, f"Open the discussion about: {news_text}.")
+    messages.append(f"Selin: {selin_intro}")
+    print("Moderator Intro:\n", selin_intro, "\n")
+
+    # 2️⃣ Bullish konuşuyor
+    bullish_view = generate_as(
+        SYSTEM_BULLISH,
+        f"The moderator introduced the topic: {news_text}. Respond with your opening bullish perspective."
+    )
+    messages.append(f"Bullish Investor: {bullish_view}")
+    print("Bullish Investor:\n", bullish_view, "\n")
+
+    # 3️⃣ Bearish karşılık veriyor
+    bearish_view = generate_as(
+        SYSTEM_BEARISH,
+        f"The moderator introduced the topic: {news_text}. "
+        f"The bullish economist said: {bullish_view}\n"
+        "Now respond with your cautious analysis."
+    )
+    messages.append(f"Bearish Economist: {bearish_view}")
+    print("Bearish Economist:\n", bearish_view, "\n")
+
+    # 4️⃣ Selin toparlıyor (konuya referans ver)
+    selin_wrap = generate_as(
+        SYSTEM_MODERATOR,
+        f"Based on the debate about {news_text}, summarize their main differences and close the panel politely."
+    )
+    messages.append(f"Selin: {selin_wrap}")
+    print("Moderator Wrap-up:\n", selin_wrap, "\n")
+
+    # 5️⃣ GPT özetleme
+    debate_text = "\n".join(messages)
+    summary_prompt = (
+        "Summarize this debate between a bullish and a bearish economist in 5 bullet points. "
+        "Keep it grounded in the topic and add a balanced conclusion.\n\n"
+        f"{debate_text}"
+    )
+
+    summary_resp = client.chat.completions.create(
+        model=SUMMARY_MODEL,
+        messages=[
+            {"role": "system", "content": "You are an expert economic summarizer."},
+            {"role": "user", "content": summary_prompt}
+        ]
+    )
+    final_summary = summary_resp.choices[0].message.content.strip()
+    print("📊 GPT Summary:\n", final_summary)
+
+    return {
+        "moderator_intro": selin_intro,
+        "bullish_view": bullish_view,
+        "bearish_view": bearish_view,
+        "moderator_wrap": selin_wrap,
+        "summary": final_summary
+    }
+
+def export_to_pdf(result: dict, filename="FinTalk_Report.pdf"):
+    from reportlab.lib.pagesizes import A4
+    from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer
+    from reportlab.lib.styles import getSampleStyleSheet
+
+    styles = getSampleStyleSheet()
+    doc = SimpleDocTemplate(filename, pagesize=A4)
+    story = []
+
+    def add(title, text):
+        story.append(Paragraph(f"<b>{title}</b>", styles["Heading3"]))
+        story.append(Paragraph(text.replace("\n", "<br/>"), styles["BodyText"]))
+        story.append(Spacer(1, 12))
+
+    add("Topic", result.get("topic", "—"))
+    add("Moderator Intro", result["moderator_intro"])
+    add("Bullish Investor", result["bullish_view"])
+    add("Bearish Economist", result["bearish_view"])
+    add("Moderator Wrap-up", result["moderator_wrap"])
+    add("GPT-4 Summary", result["summary"])
+
+    story.append(Paragraph("<i>Generated by FinTalk – AI Economic Roundtable</i>", styles["Normal"]))
+    doc.build(story)
+
+async def generate_tts_files(result):
+    voices = {
+        "moderator_intro": "en-US-AriaNeural",
+        "bullish_view": "en-US-GuyNeural",
+        "bearish_view": "en-GB-RyanNeural",
+        "moderator_wrap": "en-US-AriaNeural"
+    }
+    for key, voice in voices.items():
+        filename = f"{key}.mp3"
+        text = result[key]
+        await edge_tts.Communicate(text, voice=voice, rate="+0%").save(filename)
+        print(f"✅ {filename} oluşturuldu")
+
+# -----------------------------------------------------
+# 8) Hızlı Test
+# -----------------------------------------------------
+if __name__ == "__main__":
+    topic = input("What's discussion topic ?\n>")
+    result = fintalk_discussion(topic)
+    result["topic"] = topic
+    export_to_pdf(result)
+    asyncio.run(generate_tts_files(result))
+