Create app.py

app.py

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+!pip install gradio transformers requests
+
+import gradio as gr
+from transformers import pipeline
+import requests
+import time  # For simulating intermediate steps
+
+# Load the sentiment analysis model
+classifier = pipeline('sentiment-analysis', model='krishnamishra8848/movie_sentiment_analysis')
+
+# Language detection function
+def detect_language(text):
+    detect_url = "https://google-translator9.p.rapidapi.com/v2/detect"
+    detect_payload = {"q": text}
+    headers = {
+        "x-rapidapi-key": "ef532cb7b6msh96f36c918327aacp171ce5jsn42c4de22fe5d",
+        "x-rapidapi-host": "google-translator9.p.rapidapi.com",
+        "Content-Type": "application/json"
+    }
+
+    response = requests.post(detect_url, json=detect_payload, headers=headers)
+    if response.status_code == 200:
+        detections = response.json().get('data', {}).get('detections', [[]])[0]
+        if detections:
+            return detections[0].get('language')
+    return None
+
+# Translation function
+def translate_text(text, source_language, target_language="en"):
+    translate_url = "https://google-translator9.p.rapidapi.com/v2"
+    translate_payload = {
+        "q": text,
+        "source": source_language,
+        "target": target_language,
+        "format": "text"
+    }
+    headers = {
+        "x-rapidapi-key": "ef532cb7b6msh96f36c918327aacp171ce5jsn42c4de22fe5d",
+        "x-rapidapi-host": "google-translator9.p.rapidapi.com",
+        "Content-Type": "application/json"
+    }
+
+    response = requests.post(translate_url, json=translate_payload, headers=headers)
+    if response.status_code == 200:
+        translations = response.json().get('data', {}).get('translations', [{}])
+        if translations:
+            return translations[0].get('translatedText')
+    return None
+
+# Main function for Gradio
+def analyze_sentiment_with_steps(text):
+    # Step 1: Detecting Language
+    status = "Detecting Language..."
+    yield status
+    detected_language = detect_language(text)
+    if not detected_language:
+        yield "Error: Could not detect the language."
+        return
+
+    status = f"Language Detected: {detected_language.upper()}"
+    yield status
+
+    # Step 2: Translating if necessary
+    if detected_language != "en":
+        status += "\nTranslating text to English..."
+        yield status
+        text = translate_text(text, detected_language)
+        if not text:
+            yield "Error: Could not translate the input text."
+            return
+
+    # Step 3: Sending to model
+    status += "\nSending to Model..."
+    yield status
+    time.sleep(1)  # Simulate processing time for better user experience
+
+    # Step 4: Sentiment analysis
+    result = classifier(text)
+    label_mapping = {"LABEL_0": "negative", "LABEL_1": "positive"}
+    sentiment = label_mapping[result[0]['label']]
+    confidence = result[0]['score']
+    status += f"\nPrediction: {sentiment.capitalize()} (Confidence: {confidence:.2f})"
+    yield status
+
+# Gradio interface
+interface = gr.Interface(
+    fn=analyze_sentiment_with_steps,
+    inputs=gr.Textbox(
+        label="Enter Movie Review",
+        placeholder="Type your review in any language...",
+        lines=3
+    ),
+    outputs=gr.Textbox(label="Prediction Steps"),
+    live=True,
+    title="Multilingual Movie Sentiment Analysis",
+    description=(
+        "This app analyzes movie reviews written in any language. "
+        "It detects the language, translates it to English (if required), "
+        "and predicts the sentiment (positive/negative)."
+    )
+)
+
+# Launch Gradio app
+interface.launch(share=True)