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Dockerfile.txt

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+FROM python:3.10-slim
+
+ENV PYTHONUNBUFFERED=1
+
+RUN apt-get update && apt-get install -y \
+    ffmpeg \
+    libsndfile1 \
+    git \
+    && rm -rf /var/lib/apt/lists/*
+
+WORKDIR /app
+
+COPY requirements.txt /app/
+
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY . /app
+
+EXPOSE 7860
+
+CMD ["gunicorn", "-b", "0.0.0.0:7860", "app:app"]

app.py

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+import os
+import json
+from flask import Flask, request, render_template_string
+from textblob import TextBlob
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torchvision import transforms
+from PIL import Image
+import numpy as np
+import librosa
+
+# ----------------------------------------------------------
+# PATHS
+# ----------------------------------------------------------
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+UPLOAD_FOLDER = os.path.join(BASE_DIR, "uploads")
+os.makedirs(UPLOAD_FOLDER, exist_ok=True)
+
+app = Flask(__name__)
+
+# ----------------------------------------------------------
+# TEXT SENTIMENT
+# ----------------------------------------------------------
+def predict_text_sentiment(text: str):
+    if not text or not text.strip():
+        return None, None
+
+    polarity = TextBlob(text).sentiment.polarity
+
+    if polarity > 0.1:
+        arr = [0.1, 0.1, 0.8]
+    elif polarity < -0.1:
+        arr = [0.8, 0.1, 0.1]
+    else:
+        arr = [0.2, 0.7, 0.1]
+
+    return arr, max(arr)
+
+
+# ----------------------------------------------------------
+# FINAL AUDIO SENTIMENT (Librosa-based - NO TF, NO TRANSFORMERS)
+# ----------------------------------------------------------
+def predict_audio_sentiment(file_path):
+    if not file_path:
+        return None, None
+
+    try:
+        # Load audio
+        y, sr = librosa.load(file_path, sr=16000)
+
+        # Extract intensity & pitch
+        energy = float(np.mean(np.abs(y)))
+
+        pitch, _ = librosa.piptrack(y=y, sr=sr)
+        pitch_vals = pitch[pitch > 0]
+        pitch_mean = float(np.mean(pitch_vals)) if pitch_vals.size > 0 else 0
+
+        tempo, _ = librosa.beat.beat_track(y=y, sr=sr)
+
+        # Simple rule-based emotions
+        if energy < 0.02 and pitch_mean < 120:
+            arr = [0.8, 0.15, 0.05]  # negative
+        elif pitch_mean > 180 and energy > 0.05:
+            arr = [0.7, 0.2, 0.1]   # angry -> negative
+        elif tempo > 120 or pitch_mean > 160:
+            arr = [0.1, 0.1, 0.8]   # happy -> positive
+        else:
+            arr = [0.1, 0.8, 0.1]   # neutral
+
+        return arr, max(arr)
+
+    except Exception as e:
+        print("❌ AUDIO ERROR:", e)
+        return None, None
+
+
+# ----------------------------------------------------------
+# IMAGE SENTIMENT (your trained CNN)
+# ----------------------------------------------------------
+NUM_CLASSES = 7
+IMG_LABELS = ["angry", "disgust", "fear", "happy", "neutral", "sad", "surprise"]
+
+class MediumEmotionCNN(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.layer1 = nn.Sequential(
+            nn.Conv2d(1, 32, 3, padding=1),
+            nn.ReLU(),
+            nn.BatchNorm2d(32),
+            nn.MaxPool2d(2)
+        )
+        self.layer2 = nn.Sequential(
+            nn.Conv2d(32, 64, 3, padding=1),
+            nn.ReLU(),
+            nn.BatchNorm2d(64),
+            nn.MaxPool2d(2)
+        )
+        self.layer3 = nn.Sequential(
+            nn.Conv2d(64, 128, 3, padding=1),
+            nn.ReLU(),
+            nn.BatchNorm2d(128),
+            nn.MaxPool2d(2)
+        )
+        self.fc1 = nn.Linear(128 * 6 * 6, 256)
+        self.dropout = nn.Dropout(0.4)
+        self.fc2 = nn.Linear(256, NUM_CLASSES)
+
+    def forward(self, x):
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x = x.reshape(x.size(0), -1)
+        x = F.relu(self.fc1(x))
+        x = self.dropout(x)
+        return self.fc2(x)
+
+# Load model
+IMG_MODEL_PATH = os.path.join(BASE_DIR, "emotion_cnn.pth")
+image_device = "cuda" if torch.cuda.is_available() else "cpu"
+
+image_model = MediumEmotionCNN().to(image_device)
+IMAGE_MODEL_OK = False
+
+try:
+    image_model.load_state_dict(torch.load(IMG_MODEL_PATH, map_location=image_device))
+    image_model.eval()
+    IMAGE_MODEL_OK = True
+    print("🟢 Image CNN loaded successfully!")
+except Exception as e:
+    print("❌ Image model failed:", e)
+
+img_transform = transforms.Compose([
+    transforms.Grayscale(1),
+    transforms.Resize((48, 48)),
+    transforms.ToTensor(),
+    transforms.Normalize((0.5,), (0.5,))
+])
+
+def predict_image_sentiment(path):
+    if not (IMAGE_MODEL_OK and path and os.path.exists(path)):
+        return None, None
+
+    try:
+        img = Image.open(path).convert("RGB")
+        x = img_transform(img).unsqueeze(0).to(image_device)
+
+        with torch.no_grad():
+            logits = image_model(x)
+            probs7 = torch.softmax(logits, dim=1)[0].cpu().numpy()
+
+        idx = {l: i for i, l in enumerate(IMG_LABELS)}
+        pos = float(probs7[idx["happy"]] + probs7[idx["surprise"]])
+        neu = float(probs7[idx["neutral"]])
+        neg = float(probs7[idx["angry"]] + probs7[idx["disgust"]] +
+                    probs7[idx["fear"]] + probs7[idx["sad"]])
+
+        return [neg, neu, pos], max([neg, neu, pos])
+
+    except Exception as e:
+        print("❌ Image error:", e)
+        return None, None
+
+
+# ----------------------------------------------------------
+# FUSION
+# ----------------------------------------------------------
+def fuse_sentiments(*items):
+    probs = [arr for arr, conf in items if arr]
+    if not probs:
+        return None
+
+    avg = torch.tensor(probs).mean(dim=0).tolist()
+    sent = ["negative", "neutral", "positive"][int(np.argmax(avg))]
+    emoji = {"negative": "😡", "neutral": "😐", "positive": "😊"}[sent]
+
+    return {"sentiment": sent, "emoji": emoji, "probs": avg}
+
+
+# ----------------------------------------------------------
+# HTML (unchanged)
+# ----------------------------------------------------------
+HTML = """
+<!doctype html>
+<html><head>
+<meta charset="utf-8" />
+
+<title>🎭 Multimodal Sentiment Analyzer</title>
+
+<style>
+body{
+    margin:0;
+    font-family:Poppins, sans-serif;
+    background: linear-gradient(135deg, #161616, #1f0033, #33001a);
+    background-size: 200% 200%;
+    animation: gradientShift 8s ease infinite;
+    color:#f5f5f5;
+}
+
+@keyframes gradientShift {
+    0% { background-position: 0% 50%; }
+    50% { background-position: 100% 50%; }
+    100% { background-position: 0% 50%; }
+}
+
+.wrap{
+    max-width:900px;
+    margin:40px auto;
+    padding:20px;
+}
+
+.card{
+    background:rgba(255,255,255,0.07);
+    backdrop-filter: blur(12px);
+    border-radius:16px;
+    padding:28px;
+    box-shadow:0 0 18px rgba(0,0,0,0.5);
+    margin-top:22px;
+    border:1px solid rgba(255,255,255,0.15);
+}
+
+h1{
+    text-align:center;
+    font-size:36px;
+    font-weight:700;
+    color:#ffca5a;
+    margin-bottom:10px;
+    text-shadow:0 0 12px rgba(255, 204, 102,0.4);
+}
+
+input,textarea{
+    width:100%;
+    padding:14px;
+    border-radius:12px;
+    background:rgba(255,255,255,0.15);
+    border:1px solid rgba(255,255,255,0.25);
+    color:#fff;
+    margin-top:8px;
+    margin-bottom:18px;
+    outline:none;
+    resize:none;
+    box-sizing: border-box;
+}
+
+.btn{
+    width:100%;
+    padding:16px;
+    border-radius:12px;
+    background:linear-gradient(90deg,#ff9933,#ff5500);
+    border:0;
+    font-weight:bold;
+    color:white;
+    margin-top:6px;
+    cursor:pointer;
+    box-shadow:0 0 12px rgba(255,153,51,0.5);
+    transition: transform .2s ease;
+}
+
+.btn:hover{
+    transform:scale(1.04);
+}
+
+.preview img,.preview audio{
+    margin-top:12px;
+    max-width:100%;
+    border-radius:12px;
+    box-shadow:0 0 14px rgba(255,153,51,0.4);
+}
+
+.result-emoji{
+    font-size:60px;
+    margin-bottom:10px;
+    animation: pop 0.7s ease;
+}
+
+@keyframes pop {
+    0%{transform:scale(0.2);}
+    100%{transform:scale(1);}
+}
+
+pre{
+    background:rgba(0,0,0,0.4);
+    padding:16px;
+    border-radius:12px;
+    color:#7fffd4;
+    overflow:auto;
+}
+
+label{
+    font-size:15px;
+    opacity:0.9;
+    margin-top:12px;
+    display:block;
+}
+</style>
+
+
+<script>
+function preview(input,id,type){
+    let file = input.files[0];
+    if(!file) return;
+    let url = URL.createObjectURL(file);
+
+    if(type==="img")
+        document.getElementById(id).innerHTML = `<img src="${url}">`;
+    else
+        document.getElementById(id).innerHTML = `<audio controls src="${url}"></audio>`;
+}
+</script>
+
+</head>
+<body>
+
+<div class="wrap">
+    <h1>🎯 Multimodal Sentiment Analyzer</h1>
+
+    <form method="POST" enctype="multipart/form-data" class="card">
+        <label>Enter Text:</label>
+        <textarea name="text" rows="4" placeholder="Write something..."></textarea>
+
+        <label>Upload Face Image:</label>
+        <input type="file" name="image" accept="image/*" onchange="preview(this,'imgprev','img')">
+        <div class="preview" id="imgprev"></div>
+
+        <label>Upload Audio:</label>
+        <input type="file" name="audio" accept="audio/*" onchange="preview(this,'audprev','aud')">
+        <div class="preview" id="audprev"></div>
+
+        <button class="btn">🚀 Analyze</button>
+    </form>
+
+    {% if result %}
+    <div class="card" style="text-align:center;">
+        <div class="result-emoji">{{ result['fused']['emoji'] }}</div>
+        <h2>{{ result['fused']['sentiment'] | capitalize }}</h2>
+    </div>
+
+    <div class="card">
+        <pre>{{ result_json }}</pre>
+    </div>
+    {% endif %}
+</div>
+
+</body></html>
+"""
+
+
+
+# ----------------------------------------------------------
+# ROUTE
+# ----------------------------------------------------------
+@app.route("/", methods=["GET", "POST"])
+def home():
+    result = None
+
+    if request.method == "POST":
+        text = request.form.get("text", "")
+
+        audio_file = request.files.get("audio")
+        image_file = request.files.get("image")
+
+        audio_path = None
+        img_path = None
+
+        if audio_file and audio_file.filename:
+            audio_path = os.path.join(UPLOAD_FOLDER, audio_file.filename)
+            audio_file.save(audio_path)
+
+        if image_file and image_file.filename:
+            img_path = os.path.join(UPLOAD_FOLDER, image_file.filename)
+            image_file.save(img_path)
+
+        t = predict_text_sentiment(text)
+        a = predict_audio_sentiment(audio_path)
+        i = predict_image_sentiment(img_path)
+
+        fused = fuse_sentiments(t, a, i)
+
+        result = {"text": t, "audio": a, "image": i, "fused": fused}
+        result_json = json.dumps(result, indent=2)
+
+        return render_template_string(HTML, result=result, result_json=result_json)
+
+    return render_template_string(HTML)
+
+
+# ----------------------------------------------------------
+if __name__ == "__main__":
+    port = int(os.environ.get("PORT", 5000))
+    app.run(host="0.0.0.0", port=int(os.environ.get("PORT", 7860)))

emotion_cnn.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d02ae0ed00d07354860b6967be52b23a2e6a6de764ffa7e035778504dab3cdbc
+size 5109183

requirements.txt

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+Flask==3.0.0
+gunicorn==21.2.0
+textblob==0.17.1
+transformers==4.45.1
+torch==2.9.0
+torchvision==0.24.0
+torchaudio==2.9.0
+Pillow==10.4.0
+librosa==0.10.1
+
+huggingface-hub
+accelerate
+soundfile

runtime.txt

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+python-3.10