initial commit yat - yet another translater

app.py

@@ -0,0 +1,131 @@
+import requests
+
+from fastapi import FastAPI
+
+app = FastAPI()
+
+class HuggingFaceAPI:
+    def __init__(self, token):
+        self.token = token
+
+    def send_request(self, url, method, body):
+        headers = {
+            "Authorization": f"Bearer {self.token}",
+            "Content-Type": "application/json"
+        }
+
+        if method == "GET":
+            response = requests.get(url, headers=headers)
+        elif method == "POST":
+            response = requests.post(url, headers=headers, json=body)
+        else:
+            raise ValueError(f"Unsupported HTTP method: {method}")
+
+        response.raise_for_status()
+        return response.json()
+
+    def text_translation(self, text, target_language):
+        source_language = self.language_detection(text) 
+        url = "https://api-inference.huggingface.co/models/Helsinki-NLP/opus-mt-"+source_language+"-"+target_language
+        method = "POST"
+        body = {
+            "inputs": text
+        }
+        return self.send_request(url, method, body)
+
+
+    def text_translation(self, text, source_language, target_language):
+      #return ""
+        url = "https://api-inference.huggingface.co/models/Helsinki-NLP/opus-mt-"+source_language+"-"+target_language
+        method = "POST"
+        body = {
+            "inputs": text
+        }
+        return self.send_request(url, method, body)
+
+    def language_detection(self, text):
+        url = "https://api-inference.huggingface.co/models/papluca/xlm-roberta-base-language-detection"
+        method = "POST"
+        body = {
+            "inputs": text
+        }
+        return self.send_request(url, method, body)
+
+# ... existing API endpoints ...
+
+@app.post("/hf-inference/language_detection")
+async def language_detection_api(text: str):
+    language_detection_response = api.language_detection(text)
+    return language_detection_response
+
+@app.post("/hf-inference/text_translation")
+async def text_translation_api(text: str, source_language:str, target_language: str):
+    text_translation_response = api.text_translation(text, source_language, target_language)
+    return text_translation_response
+
+@app.post("/hf-inference/text_translation")
+async def text_translation_api(text: str, target_language: str):
+    text_translation_response = api.text_translation(text, target_language)
+    return text_translation_response
+
+### ENd of Fast API endpoints
+
+api = HuggingFaceAPI( {api_hf_key} )
+
+# Define the function to be called when inputs are provided
+def hf_inference_translate(prompt="Wie kann ich Ihnen helfen?",  target_language="en"):
+    print(prompt)
+    # Call the respective API methods 
+    # Get the input language
+    chat_response_languagedetected = api.language_detection(text)
+    print(chat_response_languagedetected[0][0])
+    # Translate based on input prompt, detected language and chosen target language
+    text_translation_response = api.text_translation(prompt, chat_response_languagedetected[0][0]['label'], target_language) 
+    print(text_translation_response) 
+    # Extract the labels and scores from the result
+    label_scores = {entry['label']: entry['score'] for entry in chat_response_languagedetected[0][:3]}
+    print(label_scores)
+    # Return the API responses #
+    return  text_translation_response[0]['translation_text'],label_scores
+
+text = "Hallo, ich bin Christof. Wie geht es dir?"
+#text = "Меня зовут Вольфганг и я живу в Берлине"
+translation_response = hf_inference_translate(text, "en")
+print(translation_response)
+
+
+
+import gradio as gr
+import requests
+ 
+
+iface = gr.Interface(
+    fn=hf_inference_translate,
+    inputs=[
+        gr.inputs.Textbox(label="Input", lines=5, placeholder="Enter text to translate"),
+        gr.inputs.Dropdown(["en", "fr", "de", "es", "ch", "ru"], label="Select target language")
+    ],
+    outputs=[
+        gr.outputs.Textbox(label="Translated text"),
+        gr.outputs.Label(label="Detected languages", num_top_classes=3)
+    ],
+    title="Translation Interface",
+    description="Type something in any language below and then click Run to see the output in the chosen target language.",
+    examples=[["Wie geht es Dir?", "fr"], ["Do you need help?", "de"]],
+    article="## Text Examples",
+    article_description="Use examples",
+    #live=True,
+    debug=True
+)
+
+ 
+
+# Create a Gradio interface
+#queue
+iface.queue(concurrency_count=3)
+# Run the Gradio interface
+#iface.launch(share=True)
+iface.launch(share=True, debug=True)
+ 
+
+