Initial deploy

app.py

@@ -0,0 +1,226 @@
+import gradio as gr
+import torch
+import re
+from transformers import (
+    AutoTokenizer,
+    AutoModelForSequenceClassification,
+    MarianMTModel,
+    MarianTokenizer,
+)
+import numpy as np
+
+# ─────────────────────────────────────────────
+# MODEL PATHS
+# ─────────────────────────────────────────────
+FINBERT_PATH = "./models/finbert-finetuned"
+TRANSLATE_MODEL = "Helsinki-NLP/opus-mt-tr-en"
+
+# ─────────────────────────────────────────────
+# LOAD MODELS (cached after first run)
+# ─────────────────────────────────────────────
+print("Loading FinBERT model...")
+try:
+    finbert_tokenizer = AutoTokenizer.from_pretrained(FINBERT_PATH)
+    finbert_model = AutoModelForSequenceClassification.from_pretrained(FINBERT_PATH)
+    finbert_model.eval()
+    FINBERT_LABELS = list(finbert_model.config.id2label.values())
+except Exception as e:
+    print(f"[WARN] Could not load local FinBERT, falling back to ProsusAI/finbert: {e}")
+    finbert_tokenizer = AutoTokenizer.from_pretrained("ProsusAI/finbert")
+    finbert_model = AutoModelForSequenceClassification.from_pretrained("ProsusAI/finbert")
+    finbert_model.eval()
+    FINBERT_LABELS = ["positive", "negative", "neutral"]
+
+print("Loading translation model...")
+tr_tokenizer = MarianTokenizer.from_pretrained(TRANSLATE_MODEL)
+tr_model = MarianMTModel.from_pretrained(TRANSLATE_MODEL)
+tr_model.eval()
+print("All models loaded.")
+
+# ─────────────────────────────────────────────
+# FINANCIAL KEYWORDS (EN)
+# ─────────────────────────────────────────────
+FINANCIAL_KEYWORDS = [
+    "revenue", "profit", "loss", "earnings", "growth", "decline", "risk",
+    "investment", "market", "stock", "bond", "interest", "rate", "inflation",
+    "debt", "equity", "dividend", "volatility", "forecast", "outlook",
+    "recession", "expansion", "gdp", "cash", "flow", "asset", "liability",
+    "bankruptcy", "merger", "acquisition", "ipo", "shares", "fund",
+]
+
+# ─────────────────────────────────────────────
+# HELPERS
+# ─────────────────────────────────────────────
+
+def detect_language(text: str) -> str:
+    """Simple heuristic: Turkish-specific characters → 'tr', else 'en'."""
+    tr_chars = set("çğıöşüÇĞİÖŞÜ")
+    if any(c in tr_chars for c in text):
+        return "tr"
+    turkish_words = {"ve", "bir", "bu", "ile", "için", "da", "de", "den", "nin",
+                     "nın", "nun", "nün", "ın", "in", "un", "ün", "yı", "yi",
+                     "yu", "yü", "ta", "te", "tan", "ten"}
+    words = set(text.lower().split())
+    if len(words & turkish_words) >= 2:
+        return "tr"
+    return "en"
+
+
+def translate_tr_to_en(text: str) -> str:
+    inputs = tr_tokenizer([text], return_tensors="pt", padding=True, truncation=True, max_length=512)
+    with torch.no_grad():
+        translated = tr_model.generate(**inputs)
+    return tr_tokenizer.decode(translated[0], skip_special_tokens=True)
+
+
+def extract_keywords(text: str) -> list[str]:
+    words = re.findall(r'\b\w+\b', text.lower())
+    found = [w for w in words if w in FINANCIAL_KEYWORDS]
+    return list(dict.fromkeys(found))  # deduplicate, preserve order
+
+
+def get_risk_level(label: str, confidence: float) -> str:
+    label = label.lower()
+    if label == "negative":
+        if confidence >= 0.80:
+            return "🔴 HIGH RISK"
+        elif confidence >= 0.55:
+            return "🟠 MEDIUM RISK"
+        else:
+            return "🟡 LOW-MEDIUM RISK"
+    elif label == "positive":
+        if confidence >= 0.80:
+            return "🟢 LOW RISK"
+        else:
+            return "🟡 LOW-MEDIUM RISK"
+    else:
+        return "🟡 NEUTRAL / MONITOR"
+
+
+def run_finbert(text: str):
+    inputs = finbert_tokenizer(text, return_tensors="pt", truncation=True,
+                               max_length=512, padding=True)
+    with torch.no_grad():
+        outputs = finbert_model(**inputs)
+    probs = torch.softmax(outputs.logits, dim=-1).squeeze().numpy()
+    idx = int(np.argmax(probs))
+    label = FINBERT_LABELS[idx]
+    confidence = float(probs[idx])
+    return label, confidence, probs
+
+
+# ─────────────────────────────────────────────
+# MAIN PREDICT FUNCTION
+# ─────────────────────────────────────────────
+
+def analyze(text: str):
+    if not text or not text.strip():
+        return "⚠️ Please enter some text.", "", "", "", ""
+
+    lang = detect_language(text)
+    original_text = text
+
+    if lang == "tr":
+        translated_text = translate_tr_to_en(text)
+        lang_info = f"🌐 Detected: **Turkish** → translated to English"
+    else:
+        translated_text = text
+        lang_info = "🌐 Detected: **English**"
+
+    label, confidence, all_probs = run_finbert(translated_text)
+    risk = get_risk_level(label, confidence)
+    keywords = extract_keywords(translated_text)
+
+    sentiment_emoji = {"positive": "📈", "negative": "📉", "neutral": "➡️"}
+    emoji = sentiment_emoji.get(label.lower(), "❓")
+
+    label_display = f"{emoji} {label.upper()}"
+    confidence_display = f"{confidence*100:.1f}%"
+    keywords_display = ", ".join(keywords) if keywords else "—"
+
+    # Build score breakdown
+    scores_md = "\n".join(
+        [f"- **{FINBERT_LABELS[i]}**: {all_probs[i]*100:.1f}%"
+         for i in range(len(FINBERT_LABELS))]
+    )
+
+    translation_note = (
+        f"\n\n**Translated text:** _{translated_text}_"
+        if lang == "tr" else ""
+    )
+
+    summary = (
+        f"{lang_info}{translation_note}\n\n"
+        f"### Score Breakdown\n{scores_md}"
+    )
+
+    return label_display, confidence_display, risk, keywords_display, summary
+
+
+# ─────────────────────────────────────────────
+# GRADIO UI
+# ─────────────────────────────────────────────
+
+with gr.Blocks(
+    title="Financial Sentiment Analysis API",
+    theme=gr.themes.Soft(primary_hue="blue"),
+    css="""
+    .result-box { border-radius: 8px; padding: 8px; }
+    footer { display: none !important; }
+    """,
+) as demo:
+
+    gr.Markdown(
+        """
+        # 📊 Financial Sentiment Analysis
+        ### Powered by FinBERT · Supports Turkish & English
+        Paste any financial news headline, earnings summary, or analyst comment.
+        """
+    )
+
+    with gr.Row():
+        with gr.Column(scale=2):
+            text_input = gr.Textbox(
+                label="📝 Input Text (Turkish or English)",
+                placeholder="e.g. 'Company reported record profits this quarter' or 'Şirket bu çeyrekte rekor kar açıkladı'",
+                lines=5,
+            )
+            submit_btn = gr.Button("🔍 Analyze Sentiment", variant="primary", size="lg")
+
+        with gr.Column(scale=1):
+            out_label = gr.Textbox(label="Sentiment Label", elem_classes="result-box")
+            out_confidence = gr.Textbox(label="Confidence Score", elem_classes="result-box")
+            out_risk = gr.Textbox(label="Risk Level", elem_classes="result-box")
+            out_keywords = gr.Textbox(label="Financial Keywords", elem_classes="result-box")
+
+    out_summary = gr.Markdown(label="Details")
+
+    submit_btn.click(
+        fn=analyze,
+        inputs=[text_input],
+        outputs=[out_label, out_confidence, out_risk, out_keywords, out_summary],
+    )
+
+    gr.Examples(
+        examples=[
+            ["The company reported a significant drop in quarterly earnings due to supply chain disruptions."],
+            ["Strong revenue growth and expanding margins signal a bullish outlook for investors."],
+            ["Şirketin hisse senetleri, beklentilerin üzerinde kar açıklamasının ardından yükseldi."],
+            ["Merkez bankası faiz oranlarını artırarak enflasyonla mücadele etmeye devam ediyor."],
+            ["Markets remained flat as investors awaited the Federal Reserve's rate decision."],
+        ],
+        inputs=text_input,
+        label="📌 Example Inputs",
+    )
+
+    gr.Markdown(
+        """
+        ---
+        **Model:** Fine-tuned FinBERT for financial sentiment classification  
+        **Translation:** Helsinki-NLP/opus-mt-tr-en for Turkish→English  
+        **Labels:** Positive · Negative · Neutral
+        """
+    )
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

@@ -1,25 +1,6 @@
-torch>=2.2.0
-transformers>=4.40.0
-datasets==2.16.0
-scikit-learn>=1.3.2
-pandas>=2.1.4
-numpy<2.0.0
-matplotlib>=3.8.2
-seaborn==0.13.0
-jupyter==1.0.0
-ipykernel==6.27.1
-fastapi==0.109.0
-uvicorn[standard]==0.27.0
-python-dotenv==1.0.0
-pydantic>=2.5.3
-accelerate==0.25.0
-langdetect==1.0.9
-sentencepiece
-streamlit==1.31.0
-plotly==5.18.0
-sentencepiece==0.1.99
-sacremoses==0.0.53
-feedparser==6.0.11
-schedule==1.2.1
-beautifulsoup4==4.12.3
-pytest==7.4.4
+gradio>=4.0.0
+torch>=2.0.0
+transformers>=4.35.0
+sentencepiece>=0.1.99
+sacremoses>=0.0.53
+numpy>=1.24.0