Update app.py

app.py

@@ -1,4 +1,108 @@
+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 = os.environ.get("CODE", None)
 
-exec(os.environ.get('CODE'))
+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()