Create app.py

app.py

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+import gradio as gr
+import requests
+import json
+
+# Hugging Face API details
+API_URL = "https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.2"
+HF_TOKEN = "YOUR_HUGGINGFACE_API_TOKEN"  # put your HF token here
+headers = {"Authorization": f"Bearer {HF_TOKEN}"}
+
+# Prompt template
+PROMPT_TEMPLATE = """
+You are an AI translation assistant for a real-time universal translator.
+Your tasks:
+1. Take the input text in either English or Spanish.
+2. Remove filler words like "um", "uh", "ehhh", "pues", "like", "you know", and stretched words ("soooo", "pizzaaaa").
+3. Correct punctuation and casing.
+4. Translate the cleaned text into the target language (English ↔ Spanish).
+5. Do not change the emotion tag.
+
+Return only JSON in this exact format:
+
+{
+  "cleaned_text": "<cleaned input text in original language>",
+  "translated_text": "<translation in target language>",
+  "emotion": "<given emotion>"
+}
+
+Input:
+{text}
+Source language: {source_lang}
+Target language: {target_lang}
+Emotion: {emotion}
+"""
+
+# Function to call Hugging Face API
+def query_hf(payload):
+    response = requests.post(API_URL, headers=headers, json=payload)
+    return response.json()
+
+# Core translation function
+def translate(text, source_lang, target_lang, emotion):
+    prompt = PROMPT_TEMPLATE.format(
+        text=text,
+        source_lang=source_lang,
+        target_lang=target_lang,
+        emotion=emotion
+    )
+    payload = {"inputs": prompt, "parameters": {"temperature": 0.2}}
+    output = query_hf(payload)
+
+    # Extract model output
+    try:
+        raw_text = output[0]["generated_text"]
+        parsed = json.loads(raw_text.strip())
+    except Exception:
+        # If model messes up, fallback with safe JSON
+        parsed = {
+            "cleaned_text": text,
+            "translated_text": f"[Translation error]",
+            "emotion": emotion
+        }
+    return parsed
+
+# Gradio UI
+def gradio_interface(text, source_lang, target_lang, emotion):
+    result = translate(text, source_lang, target_lang, emotion)
+    return json.dumps(result, indent=2, ensure_ascii=False)
+
+iface = gr.Interface(
+    fn=gradio_interface,
+    inputs=[
+        gr.Textbox(label="Input Text"),
+        gr.Radio(["en", "es"], label="Source Language"),
+        gr.Radio(["en", "es"], label="Target Language"),
+        gr.Radio(["happy", "sad", "angry", "calm", "excited"], label="Emotion")
+    ],
+    outputs=gr.Textbox(label="Output JSON"),
+    title="AI Universal Translator - Translation Module",
+    description="Cleans text, translates EN ↔ ES, and preserves emotions."
+)
+
+if __name__ == "__main__":
+    iface.launch()