Upload app.py

app.py

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+from transformers import pipeline
+from dotenv import find_dotenv, load_dotenv
+from langchain import PromptTemplate, LLMChain, HuggingFaceHub
+import streamlit as st
+import requests
+import os
+
+
+load_dotenv(find_dotenv())
+HUGGINGFACE_API = os.getenv("HUGGINGFACE_API")
+
+
+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
+
+
+
+def generate_story(scenario, length):
+  template = """
+  You are  story teller, generate a short story in {length} words\n
+  CONTEXT:{scenario}\n
+  STORY:
+  """
+
+  prompt = PromptTemplate(template=template, input_variables=["scenario","length"])
+  llm = LLMChain(llm=HuggingFaceHub(huggingfacehub_api_token=HUGGINGFACE_API, repo_id="mistralai/Mixtral-8x7B-Instruct-v0.1"), prompt=prompt, verbose=True)
+  story = llm.predict(scenario=scenario, length=length)
+  print(story)
+  return story
+
+
+
+# def text2speech(message):
+#     API_URL = "https://api-inference.huggingface.co/models/microsoft/speecht5_tts"
+#     headers = {"Authorization": f"Bearer {HUGGINGFACE_API}"}
+#     payloads = {
+#         "inputs": message
+#     }
+#     response = requests.post(API_URL,headers=headers,json=payloads)
+#     with open('audio.wav', 'wb') as file:
+#       file.write(response.content)
+
+
+
+def main():
+    st.set_page_config(page_title="Image Storyteller")
+
+    st.header("Image to Audio")
+    uploaded_file = st.file_uploader("Choose an Image", type="jpg")
+
+    length = st.number_input("Length")
+    scenario = "" 
+    successful_processing = False  
+
+    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.name, caption="Uploaded Image", use_column_width=True)
+
+        try:
+            scenario = image2text(uploaded_file.name)
+            successful_processing = True
+        except Exception as e:
+            st.error(f"Error processing the image: {e}")
+
+        if successful_processing:
+            story = generate_story(scenario, length)
+            # text2speech(story)
+
+            with st.expander("scenario"):
+                st.write(scenario)
+            with st.expander("generated story"):
+                st.write(story)
+            # st.audio('audio.wav')
+
+if __name__ == '__main__':
+    main()