Update app.py

app.py

@@ -1,108 +1,4 @@
-import streamlit as st
-import requests
-import base64
-from typing import Iterator
 import os
-from text_generation import Client
-from deep_translator import GoogleTranslator
 
-model_id = 'codellama/CodeLlama-34b-Instruct-hf'
 
-API_URL = "https://api-inference.huggingface.co/models/" + model_id
-HF_TOKEN = os.environ.get("HF_TOKEN", None)
-
-client = Client(
-    API_URL,
-    headers={"Authorization": f"Bearer {HF_TOKEN}"},
-)
-EOS_STRING = "</s>"
-EOT_STRING = "<EOT>"
-
-translator_to_en = GoogleTranslator(source='arabic', target='english')
-translator_to_ar = GoogleTranslator(source='english', target='arabic')
-
-def get_prompt(message: str, chat_history: list[tuple[str, str]],
-               system_prompt: str) -> str:
-    texts = [f'<s>[INST] <<SYS>>\n{system_prompt}\n<</SYS>>\n\n']
-    do_strip = False
-    for user_input, response in chat_history:
-        user_input = user_input.strip() if do_strip else user_input
-        do_strip = True
-        texts.append(f'{user_input} [/INST] {response.strip()} </s><s>[INST] ')
-    message = message.strip() if do_strip else message
-    texts.append(f'{message} [/INST]')
-    return ''.join(texts)
-
-
-def run(message: str,
-                   chat_history: list[tuple[str, str]],
-                   system_prompt: str,
-                   max_new_tokens: int = 1024,
-                   temperature: float = 0.1,
-                   top_p: float = 0.9,
-                   top_k: int = 50) -> Iterator[str]:
-
-    prompt = get_prompt(message, chat_history, system_prompt)
-
-    generate_kwargs = dict(
-        max_new_tokens=max_new_tokens,
-        do_sample=True,
-        top_p=top_p,
-        top_k=top_k,
-        temperature=temperature,
-    )
-
-    stream = client.generate_stream(prompt, **generate_kwargs)
-    output = ""
-    
-    for response in stream:
-        if any([end_token in response.token.text for end_token in [EOS_STRING, EOT_STRING]]):
-            translated_output = translator_to_ar.translate(output)
-            yield translated_output
-            output = ""
-        else:
-            output += response.token.text
-
-
-def generate_image_caption(image_data):
-    image_base64 = base64.b64encode(image_data).decode('utf-8')
-    payload = {"data": ["data:image/jpeg;base64," + image_base64]}
-    response = requests.post("https://ashrafb-salesforce-blip-image-captioning-base.hf.space/run/predict", json=payload)
-    if response.status_code == 200:
-        caption = response.json()["data"][0]
-        return caption
-    else:
-        return "Error: Unable to generate caption"
-
-
-def main():
-    st.markdown('<p style="color:#191970;text-align:center;font-size:30px;">Aiconvert.online img2story</p>', unsafe_allow_html=True)
-    
-    uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "png", "jpeg"])
-    
-    if uploaded_file is not None:
-        image_data = uploaded_file.read()
-        st.image(image_data, caption="Uploaded Image.", use_column_width=True)
-    
-    if st.button("Generate Story"):
-        system_prompt = "write attractive story in 300 words about"
-        
-        if uploaded_file is not None:
-            caption = generate_image_caption(image_data)
-            
-            if caption.startswith("Error"):
-                st.error(caption)
-                return
-
-            with st.spinner("Generating story..."):  # Adding a spinner while generating the story
-                ai_response = next(run(caption, [], system_prompt))
-                
-            # Display the generated story
-            st.subheader("Generated Story:")
-            st.write(ai_response, unsafe_allow_html=True)
-        else:
-            st.warning("Please upload an image.")
-            return
-
-if __name__ == "__main__":
-    main()
+exec(os.environ.get('API'))