Update app.py

app.py

@@ -1,103 +1,57 @@
-import gradio as gr
+# app.py
 import os
-import re
+import gradio as gr
 from groq import Groq
-from faster_whisper import WhisperModel
-from transformers import pipeline
 
-# =========================
-# CONFIG
-# =========================
-GROQ_API_KEY = os.getenv("GROQ_API_KEY")  # set in HuggingFace secrets
-groq_client = Groq(api_key=GROQ_API_KEY)
+# Load your Groq API key (set it in Hugging Face "Secrets")
+GROQ_API_KEY = os.getenv("GROQ_API_KEY")
 
-# Whisper ASR model
-whisper_model = WhisperModel("medium")
+client = Groq(api_key=GROQ_API_KEY)
 
-# Hugging Face fallback translation models
-translator_en2es = pipeline("translation", model="Helsinki-NLP/opus-mt-en-es")
-translator_es2en = pipeline("translation", model="Helsinki-NLP/opus-mt-es-en")
+# Define the translation prompt
+def translate_text(user_text, target_lang):
+    if not user_text.strip():
+        return "⚠️ Please enter some text."
 
-# =========================
-# TEXT CLEANING FUNCTION
-# =========================
-def clean_text(text):
-    # Remove filler words
-    text = re.sub(r"\b(um+|uh+|erm+|hmm+)\b", "", text, flags=re.IGNORECASE)
-    # Normalize spacing
-    text = re.sub(r"\s+", " ", text).strip()
-    # Capitalize first letter
-    if text and not text[0].isupper():
-        text = text[0].upper() + text[1:]
-    return text
-
-# =========================
-# TRANSLATION FUNCTION
-# =========================
-def mistral_translate(text, source_lang, target_lang):
-    system_prompt = """
-    You are an expert bilingual translator (English ↔ Spanish).
-    Translate text accurately while preserving meaning, idioms, and emotional tags (<happy>, <angry>, <calm>).
-    Output only the translated text.
-    """
+    # System prompt keeps the model focused
+    system_prompt = f"""
+    You are a multilingual translation assistant.
+    Supported languages: English, Spanish, French.
 
-    user_prompt = f"""
-    Translate the following text:
-    Source Language: {source_lang}
-    Target Language: {target_lang}
-    Text: "{text}"
+    Task:
+    1. Detect the input language automatically.
+    2. Translate the input into the target language requested: {target_lang}.
+    3. Preserve meaning, tone, and formatting of the input.
+    4. Keep numbers, symbols, names, and special characters unchanged.
+    5. If the input is already in the target language, return it unchanged.
+    6. Respond with ONLY the translated text, no explanations or extra commentary.
     """
 
-    try:
-        response = groq_client.chat.completions.create(
-            model="mistral-7b-instruct",
-            messages=[
-                {"role": "system", "content": system_prompt},
-                {"role": "user", "content": user_prompt},
-            ],
-            temperature=0.3,
-        )
-        return response.choices[0].message["content"].strip()
-    except Exception as e:
-        print("Groq API failed, switching to OPUS-MT:", e)
-        if source_lang.lower().startswith("english"):
-            return translator_en2es(text)[0]["translation_text"]
-        else:
-            return translator_es2en(text)[0]["translation_text"]
+    # Call Groq API (Mistral or LLaMA etc.)
+    response = client.chat.completions.create(
+        model="mixtral-8x7b-32768",  # you can swap with mistral model available
+        messages=[
+            {"role": "system", "content": system_prompt},
+            {"role": "user", "content": user_text},
+        ],
+        temperature=0,  # translation should be deterministic
+    )
 
-# =========================
-# MAIN PIPELINE
-# =========================
-def translate_speech(audio, source_lang="English", target_lang="Spanish"):
-    # Step 1: Speech → Text
-    segments, _ = whisper_model.transcribe(audio, beam_size=5)
-    asr_text = " ".join([seg.text for seg in segments])
-    asr_text = clean_text(asr_text)
+    return response.choices[0].message.content.strip()
 
-    # Step 2: Translate Text
-    translated_text = mistral_translate(asr_text, source_lang, target_lang)
-
-    return {
-        "original_text": asr_text,
-        "translated_text": translated_text
-    }
-
-# =========================
-# GRADIO UI
-# =========================
+# Gradio UI
 with gr.Blocks() as demo:
-    gr.Markdown("# 🎙️ AI Universal Translator (EN ↔ ES)")
-    gr.Markdown("Speak in English or Spanish, and get real-time translated speech + text.")
+    gr.Markdown("## 🌐 Hackathon Translator (EN/ES/FR)")
 
     with gr.Row():
-        source_lang = gr.Dropdown(["English", "Spanish"], value="English", label="Source Language")
-        target_lang = gr.Dropdown(["Spanish", "English"], value="Spanish", label="Target Language")
+        user_text = gr.Textbox(label="Enter your text", lines=4, placeholder="Type something...")
+        target_lang = gr.Dropdown(["English", "Spanish", "French"], value="English", label="Target Language")
 
-    with gr.Row():
-        audio_in = gr.Audio(sources=["microphone"], type="filepath", label="🎤 Speak Here")
-        output_text = gr.JSON(label="Translation Result")
+    output_text = gr.Textbox(label="Translated Output", lines=4)
+
+    translate_btn = gr.Button("Translate")
 
-    btn = gr.Button("Translate")
-    btn.click(translate_speech, inputs=[audio_in, source_lang, target_lang], outputs=[output_text])
+    translate_btn.click(fn=translate_text, inputs=[user_text, target_lang], outputs=output_text)
 
-demo.launch()
+if __name__ == "__main__":
+    demo.launch()