Update app.py

app.py

@@ -1,34 +1,79 @@
 
-import streamlit as st
-import time
+# import streamlit as st
+# import time
 
-def main():
-    # Title
-    st.title("Streamlit Functions Demo")
+# def main():
+#     # Title
+#     st.title("Streamlit Functions Demo")
+
+#     # Write
+#     st.write("This demo showcases various Streamlit functions.")
+
+#     # File Uploader
+#     uploaded_file = st.file_uploader("Choose an image file")
+#     if uploaded_file is not None:
+#         # Display the uploaded image
+#         st.image(uploaded_file, caption="Uploaded Image", use_column_width=True)
+
+#     # Spinner
+#     if st.button("Process Image"):
+#         with st.spinner('Processing...'):
+#             time.sleep(2)  # Simulate a long computation
+#         st.success('Processing complete!')
+
+#     # Audio
+#     st.write("Here is an audio sample:")
+#     audio_file = open('path/to/your/audio.mp3', 'rb')
+#     st.audio(audio_file.read(), format='audio/mp3')
 
-    # Write
-    st.write("This demo showcases various Streamlit functions.")
+#     # Button
+#     if st.button('Click me'):
+#         st.write('Button clicked!')
 
-    # File Uploader
-    uploaded_file = st.file_uploader("Choose an image file")
-    if uploaded_file is not None:
-        # Display the uploaded image
-        st.image(uploaded_file, caption="Uploaded Image", use_column_width=True)
+# if __name__ == "__main__":
+#     main()
 
-    # Spinner
-    if st.button("Process Image"):
-        with st.spinner('Processing...'):
-            time.sleep(2)  # Simulate a long computation
-        st.success('Processing complete!')
+import streamlit as st
+from transformers import pipeline
+
+# Function to generate image caption
+def generate_image_caption(image_path):
+    img2caption = pipeline("image-to-text", model="Salesforce/blip-image-captioning-large")
+    result = img2caption(image_path)
+    return result[0]['generated_text']
+
+# Function to generate story from text
+def text2story(text):
+    text2story = pipeline("text-generation", model="pranavpsv/genre-story-generator-v2")
+    generated_story = text2story(text)
+    return generated_story[0]['generated_text']
 
-    # Audio
-    st.write("Here is an audio sample:")
-    audio_file = open('path/to/your/audio.mp3', 'rb')
-    st.audio(audio_file.read(), format='audio/mp3')
+# Main function for the Streamlit app
+def main():
+    st.title("Image to Story Generator")
+
+    # File uploader for image
+    uploaded_file = st.file_uploader("Choose an image file", type=["jpg", "jpeg", "png"])
+    
+    if uploaded_file is not None:
+        # Display the uploaded image
+        st.image(uploaded_file, caption="Uploaded Image", use_column_width=True)
+        
+        # Generate caption button
+        if st.button("Generate Caption"):
+            with st.spinner('Generating caption...'):
+                caption = generate_image_caption(uploaded_file)
+                st.write("Generated Caption:", caption)
+                
+                # Generate story button
+                if st.button("Generate Story"):
+                    with st.spinner('Generating story...'):
+                        generated_story = text2story(caption)
+                        st.write("Generated Story:", generated_story)
 
-    # Button
-    if st.button('Click me'):
-        st.write('Button clicked!')
+    # Audio generation placeholder (to be implemented)
+    st.write("Audio generation feature coming soon...")
 
 if __name__ == "__main__":
-    main()
+    main()
+