Upload 5 files

.gitignore

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+# Ignore Python cache and bytecode
+__pycache__/
+*.py[cod]
+*.so
+
+# Ignore virtual environment
+venv/
+
+# Ignore saved model files
+saved_model/
+*.safetensors
+*.pt
+*.bin
+*.ckpt
+
+# Ignore logs or runtime data
+*.log
+*.csv
+*.tmp
+*.DS_Store
+
+# Ignore Jupyter Notebook checkpoints
+.ipynb_checkpoints/
+
+# Ignore environment files if sensitive
+.env
+
+*.pyc
+*.pyo
+*.pyd

Dockerfile

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+# Use a slim Python image
+FROM python:3.10-slim
+
+# Set working directory inside the container
+WORKDIR /app
+
+# Copy the requirements file and install dependencies
+COPY requirements.txt .
+RUN pip install --upgrade pip && pip install -r requirements.txt
+
+# Copy all app files to the container
+COPY . .
+
+# Expose the port the app will run on (Hugging Face requires 7860)
+EXPOSE 7860
+
+# Run the Flask app
+CMD ["python", "app.py"]

app.py

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+from flask import Flask, render_template, request
+import pandas as pd
+from predictor import predict_sentiment
+from flask import Flask, render_template, request, redirect, url_for
+
+
+app = Flask(__name__)
+
+# 🔘 Single review input route
+@app.route("/")
+def root():
+    return redirect("/sentiment-review/single")
+@app.route("/sentiment-review/single", methods=["GET", "POST"])
+def single_review():
+    prediction = None
+    confidence = None
+    review = ""
+
+    if request.method == "POST":
+        review = request.form.get("review", "")
+        if review.strip():
+            prediction, confidence = predict_sentiment(review)
+
+    return render_template("index.html", prediction=prediction, confidence=confidence, review=review)
+
+# 📁 Batch upload route
+@app.route("/sentiment-review/batch", methods=["GET", "POST"])
+def batch_review():
+    if request.method == "POST":
+        if 'csvfile' not in request.files:
+            return render_template("batch.html", error="No file part found.")
+
+        file = request.files['csvfile']
+        if file.filename == "":
+            return render_template("batch.html", error="No selected file.")
+
+        if file and file.filename.endswith(".csv"):
+            try:
+                df = pd.read_csv(file, encoding='utf-8')
+                if "review" not in df.columns:
+                    return render_template("batch.html", error="CSV must have a 'review' column.")
+
+                results = []
+                for i, text in enumerate(df["review"].fillna("").tolist()):
+                    sentiment, confidence = predict_sentiment(text)
+                    print(f"🧠 Review {i+1}: {text[:40]}... → {sentiment} ({confidence})")
+                    results.append({
+                        "text": text,
+                        "sentiment": sentiment,
+                        "confidence": confidence
+                    })
+
+                return render_template("batch.html", results=results)
+
+            except Exception as e:
+                print("❌ CSV Processing error:", e)
+                return render_template("batch.html", error=f"Processing error: {str(e)}")
+
+        return render_template("batch.html", error="Invalid file format. Upload .csv only.")
+
+    return render_template("batch.html")
+if __name__ == "__main__":
+    app.run(host="0.0.0.0", port=7860, debug=False)

predictor.py

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+# predictor.py
+import torch
+from transformers import BertTokenizer, BertForSequenceClassification
+import re
+
+# ✅ Load model and tokenizer from Hugging Face Hub
+MODEL_NAME = "SreyaDvn/sentiment-model"
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+try:
+    print("🚀 Loading tokenizer and model from Hugging Face Hub...")
+    tokenizer = BertTokenizer.from_pretrained(MODEL_NAME)
+    model = BertForSequenceClassification.from_pretrained(MODEL_NAME)
+    model.to(device)
+    model.eval()
+    print("✅ Model & tokenizer loaded successfully from Hugging Face.")
+except Exception as e:
+    print("❌ Error loading model/tokenizer:", e)
+
+# 🔍 Text cleaner
+def clean_text(text):
+    text = str(text).lower()
+    text = re.sub(r"http\S+", "", text)
+    text = re.sub(r"@\w+", "", text)
+    text = re.sub(r"#\w+", "", text)
+    text = re.sub(r"[^\w\s]", "", text)
+    text = re.sub(r"\d+", "", text)
+    text = re.sub(r"\s+", " ", text).strip()
+    return text
+
+# 🔮 Sentiment prediction
+def predict_sentiment(text):
+    try:
+        cleaned = clean_text(text)
+        inputs = tokenizer(cleaned, return_tensors="pt", truncation=True, padding=True, max_length=128)
+        inputs = {k: v.to(device) for k, v in inputs.items()}
+        with torch.no_grad():
+            outputs = model(**inputs)
+        probs = torch.softmax(outputs.logits, dim=1).cpu().numpy()[0]
+        pred_class = probs.argmax()
+        sentiment = "Positive 👍🏻" if pred_class == 1 else "Negative 👎🏻"
+        confidence = f"{probs[pred_class] * 100:.2f}%"
+        return sentiment, confidence
+    except Exception as e:
+        print("❌ Prediction error:", e)
+        return "Error", "0.00%"