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README.md

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----
-title: Image Text Storyteller
-emoji: 🏢
-colorFrom: green
-colorTo: yellow
-sdk: streamlit
-sdk_version: 1.28.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+
+
+
+https://github.com/arjunprakash027/Story_Teller/assets/72484657/1c756dd8-9889-420d-a30e-13dbe137a2f0
+
+
+# Story Teller
+
+Story Teller is a Streamlit application that generates a story based on an input image. It utilizes the Hugging Face Transformers library and the Salesforce BLIP Image Captioning model.
+
+## Table of Contents
+
+- [Installation](#installation)
+- [Usage](#usage)
+- [API Token](#api-token)
+- [Models](#models)
+- [Running the Application](#running-the-application)
+- [Contributing](#contributing)
+
+## Installation
+
+To install the necessary dependencies, run the following command:
+
+```shell
+pip install -r requirements.txt
+```
+
+Make sure you have the required dependencies specified in the `requirements.txt` file.
+
+## Usage
+
+To use the application, follow the steps below:
+
+1. Run the Streamlit application by executing the following command:
+
+   ```shell
+   streamlit run app.py
+   ```
+
+2. Access the application through the provided URL in the console.
+
+3. The application interface will appear with the title "Story Teller" and an instruction to "Upload an image and get a story".
+
+4. Click on the "Upload your file here..." button to select an image file (supported formats: PNG, JPEG, JPG).
+
+5. Once the image is uploaded, it will be displayed on the page.
+
+6. The application will process the uploaded image using the Salesforce BLIP Image Captioning model and generate a textual description of the image.
+
+7. The generated text will then be passed to the Hugging Face API to generate a story based on the text.
+
+8. The application will display the generated story on the page.
+
+9. If any errors occur during the process, an error message will be shown on the page, and you can try again.
+
+## API Token
+
+The application requires an API token from Hugging Face to access the story generation model. To obtain an API token, follow these steps:
+
+1. Sign up or log in to your Hugging Face account at [https://huggingface.co/](https://huggingface.co/).
+
+2. Once logged in, go to your account settings and navigate to the "API token" section.
+
+3. Generate a new API token, copy it, and replace the `"your api key"` placeholder in the `Models` class of `text_model.py` with your actual API token.
+
+## Models
+
+The `Models` class in `text_model.py` encapsulates the functionality of the application. It contains the following methods:
+
+- `__init__()`:
+    - Initializes the class and sets the API token and model ID.
+
+- `img2text(url)`:
+    - Takes an image URL as input and uses the Salesforce BLIP Image Captioning model to convert the image into text. It returns the generated text.
+
+- `story(payload)`:
+    - Takes a payload as input, which contains the generated text, and sends a request to the Hugging Face API to generate a story based on the text. It returns the generated story.
+
+- `chain(payload, num=0)`:
+    - This method acts as a recursive function that generates a chain of stories. It takes a payload as input, which initially contains the generated text. It recursively calls the `story()` method and updates the payload until the desired number of stories (50 in this case) is generated. The progress bar is also updated accordingly.
+
+## Running the Application
+If you are curious and want to just try the backend models
+execute the following command:
+
+```shell
+python text_model.py
+```
+
+Make sure you have the required dependencies installed, as mentioned in the installation section.
+
+## Contributing
+
+Contributions to the Story Teller application are welcome! If you find any issues or have suggestions for improvements, please feel free to open an issue or submit a pull request.

app.py

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+import streamlit as st
+from text_model import Models
+import os
+
+st.title("Story Teller")
+model = Models()
+st.write("Upload an image and get a story")
+uploaded_file = st.file_uploader("Upload your file here...",type=['png','jpeg','jpg'])
+
+try:
+    if uploaded_file is not None:
+        st.image(uploaded_file)
+        with open(os.path.join("images",uploaded_file.name),"wb") as f: 
+            f.write(uploaded_file.getbuffer()) 
+        response = model.chain(model.img2text(f'images/{uploaded_file.name}'))
+        st.write(response)
+except Exception as e:
+    print(e)
+    st.write("error occured! please try again")

text_model.py

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+from dotenv import find_dotenv, load_dotenv
+from transformers import pipeline 
+import requests
+import json
+import logging
+import streamlit as st
+requests.verify = False
+
+load_dotenv(find_dotenv()) #access huggingface token
+log = logging.getLogger("text_model.py")
+logging.basicConfig(level=logging.INFO,format='%(asctime)s - %(message)s', datefmt='%d-%b-%y %H:%M:%S')
+
+class Models:
+    def __init__(self):
+        self.api_token = "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"
+        self.model_id = "openai-gpt" 
+    def img2text(self,url):
+        image_to_text = pipeline("image-to-text",model="Salesforce/blip-image-captioning-base")
+        text = image_to_text(url)
+        log.info("image to text model running")
+        return text[0]['generated_text']
+
+    def story(self,payload):
+        payload  = json.dumps(payload)
+        headers = {"Authorization": f"Bearer {self.api_token}"}
+        url = f"https://api-inference.huggingface.co/models/{self.model_id}"
+        response = requests.post(url, headers=headers, data=payload)
+        log.info("story model running")
+        return response.json()[0]['generated_text']
+    
+    status_bar = st.progress(0,text="progress will be shown here")
+    def chain(self,payload,num=0):
+        if num == 50:
+            self.status_bar.progress(100,text="story generated!!")
+            return payload
+        
+        response = self.story(payload)
+        percent = int(((num+1)/50) * 100)
+        self.status_bar.progress(percent,text="generating story for you!!")
+        return self.chain(response,num+1)
+
+
+    
+
+    
+
+if __name__ == "__main__":
+
+    model = Models()
+    response = model.chain(model.img2text("D:\image-story-teller\Story_Teller\image.jpg"))
+    print(response)