Upload app.py

app.py

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+
+from transformers import pipeline
+from langchain import PromptTemplate, LLMChain
+from langchain.llms import GooglePalm
+
+import requests
+import os
+import streamlit as st
+
+
+os.environ["GOOGLE_API_KEY"] = "AIzaSyD29fEos3V6S2L-AGSQgNu03GqZEIgJads"
+os.environ ["HUGGINGFACEHUB_API_TOKEN"] = "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"
+
+llm = GooglePalm(temperature = 0.7)
+
+
+#image to text
+
+def image2text(url):
+    image_to_text = pipeline("image-to-text", model = "Salesforce/blip-image-captioning-large")
+
+    text = image_to_text(
+        url)[0]['generated_text']
+    
+    print(text)
+    return(text)
+
+#story teller
+def generate_story(scenario):
+    template = """"
+    You are a story teller;
+    you can generate a creative fun story based on a sample narrative, the story should not be more than 100 words;
+
+    CONTEXT: {scenario}
+    STORY: 
+    """
+
+    prompt = PromptTemplate(template = template, 
+        input_variables = ['scenario']
+    )
+    story_llm = LLMChain(llm=llm, prompt = prompt, verbose = True)
+
+    story = story_llm.predict(scenario = scenario)
+
+    print(story)
+    return(story)
+
+#text to speech
+
+def text2speech(message):
+     API_URL = "https://api-inference.huggingface.co/models/espnet/kan-bayashi_ljspeech_vits"
+     headers = {"Authorization": "Bearer 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"}
+     payloads = {
+          "inputs":message
+     }
+     response = requests.post(API_URL, headers = headers, json= payloads)
+     with open("audio.flac", "wb") as file:
+         file.write(response.content)
+
+
+
+def main():
+    st.set_page_config(page_title="Your Image to Audio Story", page_icon="🦜")
+    st.header("Turn Your Image to Audio Story")
+    uploaded_file = st.file_uploader("Select an Image...")
+
+    if uploaded_file is not None:
+        print(uploaded_file)
+        bytes_data = uploaded_file.getvalue()
+        with open(uploaded_file.name, 'wb') as file:
+            file.write(bytes_data)
+        st.image(uploaded_file, caption="Uploaded Image",
+                 use_column_width= True)
+        
+        scenario = image2text(uploaded_file.name)
+        st.subheader("Image Details:")
+        st.write(scenario)
+
+        story = generate_story(scenario)
+        st.subheader("Story:")
+        st.write(story)
+
+        text2speech(story)
+        st.subheader("Generated Audio:")
+        st.audio("audio.flac", format="audio/flac")
+
+        # Add a download link for the audio
+        st.subheader("Download Audio:")
+        with open("audio.flac", "rb") as audio_file:
+            st.download_button(label="Download Audio", data=audio_file, file_name="generated_audio.flac", mime="audio/flac")
+
+
+if __name__ == "__main__":
+    main()