Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
+from langdetect import detect
+import pandas as pd
+import os
+
+# -----------------------------
+# Load Models
+# -----------------------------
+# English sentiment model (CardiffNLP)
+en_model = pipeline("sentiment-analysis", model="cardiffnlp/twitter-roberta-base-sentiment")
+
+# Urdu / Roman Urdu fine-tuned model (replace with your Hugging Face repo ID)
+ur_model_name = "tahamueed23/roman-urdu-sentiment"  
+ur_tokenizer = AutoTokenizer.from_pretrained(ur_model_name)
+ur_model = AutoModelForSequenceClassification.from_pretrained(ur_model_name)
+ur_pipeline = pipeline("sentiment-analysis", model=ur_model, tokenizer=ur_tokenizer)
+
+# -----------------------------
+# CSV Setup
+# -----------------------------
+csv_file = "sentiment_logs.csv"
+if not os.path.exists(csv_file):
+    df = pd.DataFrame(columns=["Sentence", "Language", "Sentiment"])
+    df.to_csv(csv_file, index=False)
+
+# -----------------------------
+# Processing Function
+# -----------------------------
+def analyze_sentiment(sentence):
+    try:
+        # Detect language
+        lang = detect(sentence)
+
+        if lang == "en":
+            result = en_model(sentence)[0]
+        else:
+            # Treat anything non-English as Urdu/Roman Urdu
+            result = ur_pipeline(sentence)[0]
+
+        label = result["label"]
+        score = round(result["score"], 3)
+
+        # Save to CSV
+        new_row = pd.DataFrame([[sentence, lang, f"{label} ({score})"]],
+                               columns=["Sentence", "Language", "Sentiment"])
+        df = pd.read_csv(csv_file)
+        df = pd.concat([df, new_row], ignore_index=True)
+        df.to_csv(csv_file, index=False)
+
+        # Output
+        return f"**Detected Language:** {lang}\n**Sentiment:** {label}\n**Confidence:** {score}"
+
+    except Exception as e:
+        return f"⚠️ Error: {str(e)}"
+
+# -----------------------------
+# Gradio App
+# -----------------------------
+with gr.Blocks() as demo:
+    gr.Markdown("## 🌍 Multilingual Sentiment Analysis (English, Urdu, Roman Urdu)")
+    gr.Markdown("Enter a sentence in **English, Urdu, or Roman Urdu** to detect sentiment.")
+
+    input_text = gr.Textbox(label="Enter your sentence:")
+    output_text = gr.Markdown(label="Result")
+
+    btn = gr.Button("Analyze Sentiment")
+    btn.click(analyze_sentiment, inputs=input_text, outputs=output_text)
+
+demo.launch()