Create app.py

app.py

@@ -0,0 +1,74 @@
+import streamlit as st
+import requests
+from huggingface_hub import hf
+
+# Retrieve your API key from the Secrets Manager
+api_key = hf.secrets.get("testing")
+
+API_URL_SEMANTICS = "https://api-inference.huggingface.co/models/Salesforce/blip-image-captioning-large"
+API_URL_CAPTION = "https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.2"
+
+headers = {"Authorization": f"Bearer api_key"}
+st.set_page_config(page_title="Instagram Post Improvement")
+def generateSemantic(file):
+    response = requests.post(API_URL_SEMANTICS, headers=headers, data=file)
+    return response.json()[0]['generated_text']
+
+def generateCaption(payload):
+	response = requests.post(API_URL_CAPTION , headers=headers, json=payload)
+	return response.json()[0]['generated_text']
+
+st.title("Create an Eye-Catching Instagram Post 📸✨")
+st.write("""  🌟 This project utilizes two free pre-trained models from Hugging Face to enhance the engagement and attractiveness of your Instagram posts for your followers. It accomplishes this through two steps:
+
+1-🚀 Capturing the semantics of an image.
+
+2-🎀 Transforming the captured semantics into an appealing Instagram post.  """)
+st.sidebar.title('About app')
+st.sidebar.info(
+    "This is a Streamlit application created by Gasbaoui Mohammed el Amin.\n"
+    "It demonstrates how to interact with pre-trained model hagging face."
+)
+
+file=st.file_uploader("upload an image",type=["jpg","jpeg","png"])
+if file:
+  col1,col2=st.columns(2)
+  with col1:
+      st.image(file,use_column_width=True)
+  with col2:
+      
+      with st.spinner("Generating semantics..."):
+        outputSemantic=generateSemantic(file)
+        st.subheader("Output Semantic")
+        st.markdown(
+    """
+    <style>
+        /* Style for the container */
+        .fancy-text {
+            padding: 10px;
+            border-radius: 10px; /* Make edges curved */
+            box-shadow: 0px 0px 10px rgba(0, 0, 0, 0.1); /* Add box shadow */
+            border: 2px solid #ccc; /* Add a border */
+        }
+    </style>
+    """,
+        unsafe_allow_html=True
+)
+
+        # Now use the styled container for your text
+        st.markdown(f'<div class="fancy-text">{outputSemantic}</div>',
+        unsafe_allow_html=True)
+                
+      with st.spinner("Generating caption..."):
+        promptDictionary={
+             "inputs": f"convert the following image semantics '{outputSemantic}'  "
+             f"to an instagram caption make sure to add hashtags and emojis.,"
+             f"Answer: ",
+             
+        }
+        st.subheader("Caption")
+        outputCaption=generateCaption(promptDictionary)
+        result=outputCaption.split("Answer: ")[1]
+        st.markdown(f'<div class="fancy-text">{result}</div>',
+        unsafe_allow_html=True)
+