Update app.py

app.py

@@ -4,7 +4,7 @@ from langchain import PromptTemplate, LLMChain, OpenAI
 import requests
 import os
 import streamlit as st
-
+import json
 
 
 load_dotenv (find_dotenv())
@@ -57,6 +57,29 @@ def text2speech(message):
      with open('audio.wav', 'wb') as file:
          file.write(response.content)
 
+#translate
+def translatefr(Text2img2text):
+     API_URL = "https://api-inference.huggingface.co/models/sgugger/marian-finetuned-kde4-en-to-fr"
+     headers = {"Authorization": f"Bearer {HUGGINGFACEHUB_API_TOKEN}"}
+     payloads = {
+     "inputs": Text2img2text
+     }
+
+     response = requests.post(API_URL, headers=headers, json=payloads)
+     responsejson = response.content
+     # Votre donnée JSON encodée en bytes
+     data_bytes = responsejson
+
+     # Décoder les bytes en string
+     data_str = data_bytes.decode('utf-8')
+
+     # Analyser la chaîne JSON pour obtenir un objet Python
+     data = json.loads(data_str)
+
+     # Extraire le texte souhaité
+     text = data[0]['translation_text']
+     print(text)
+     return text
 
 #scenario = img2text("mmd.png")
 #story = generate_story(scenario)
@@ -82,13 +105,14 @@ def main():
         st.image(uploaded_file, caption='Uploaded Image.',
                  use_column_width=True)
         scenario = img2text(uploaded_file.name)
-        story = generate_story(scenario)
-       # en_fr_translator = pipeline("translation_en_to_fr")
-       # story_fr = en_fr_translator(story)[0]["translation_text"]
+        scenariofr = translatefr(scenario)
+        story = generate_story(scenariofr)
+        #en_fr_translator = pipeline("translation_en_to_fr")
+        #story_fr = en_fr_translator(story)[0]["translation_text"]
         text2speech(story)
 
         with st.expander("scenario"):
-            st.write(scenario)
+            st.write(scenariofr)
         with st.expander("story"):
             st.write(story)