app.py added

.gitignore

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+.env

app.py

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+from dotenv import find_dotenv, load_dotenv
+from transformers import pipeline
+import os
+import requests
+import streamlit as st
+
+load_dotenv(find_dotenv())
+HUGGINGFACE_API_TOKEN = os.getenv("HUGGINGFACE_API_TOKEN")
+
+pipe = pipeline("image-to-text", model="Salesforce/blip-image-captioning-base")
+
+#img to text
+def img_to_text(url):
+    text = pipe(url)[0]["generated_text"]
+    print(text)
+    return text
+
+def text_to_speech(message):
+    API_URL = "https://api-inference.huggingface.co/models/espnet/kan-bayashi_ljspeech_vits"
+    headers = {"Authorization": f"Bearer {HUGGINGFACE_API_TOKEN}"}
+    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="Image to Text", page_icon="🎙️")
+
+    st.header("Image to Text")
+    # Image.
+    image = "narrator.jpeg"
+    left_co, cent_co, last_co = st.columns(3)
+    with cent_co:
+        st.image(image=image)
+    uploaded_file = st.file_uploader("Choose an image: ", type=["jpg", "jpeg", "png"])
+
+    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=img_to_text(uploaded_file.name)
+            text_to_speech(scenario)
+
+            with st.expander("scenatio"):
+                st.write(scenario)
+            
+            st.audio("audio.flac")
+
+
+if __name__== "__main__":
+    main()