Update app.py

app.py

@@ -1,43 +1,30 @@
 import streamlit as st
 from transformers import pipeline
 from PIL import Image
-import requests
-
-# Hardcoded OpenAI API Key
-OPENAI_API_KEY = "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"
 
 # Load OCR model for extracting text from images
 @st.cache_resource
 def load_ocr_model():
     return pipeline("image-to-text", model="Salesforce/blip-image-captioning-base")
 
-# Function to interact with ChatGPT API
-def chat_with_gpt(prompt):
-    url = "https://api.openai.com/v1/chat/completions"
-    headers = {
-        "Authorization": f"Bearer {OPENAI_API_KEY}",
-        "Content-Type": "application/json",
-    }
-    data = {
-        "model": "gpt-3.5-turbo",  # Adjust the model if needed
-        "messages": [{"role": "user", "content": prompt}],
-        "max_tokens": 1000,
-        "temperature": 0.7,
-    }
-    response = requests.post(url, headers=headers, json=data)
-    if response.status_code == 200:
-        return response.json()["choices"][0]["message"]["content"].strip()
-    else:
-        st.error(f"Error from OpenAI: {response.status_code} - {response.text}")
-        return None
+# Load text-generation model
+@st.cache_resource
+def load_text_model():
+    return pipeline("text-generation", model="EleutherAI/gpt-neo-1.3B")
+
+# Function to process text with a language model
+def process_with_llm(prompt):
+    llm_model = load_text_model()
+    response = llm_model(prompt, max_length=500, do_sample=True, temperature=0.7)
+    return response[0]["generated_text"]
 
 # Streamlit App
 def main():
-    st.title("Image-to-Text with ChatGPT")
+    st.title("Image-to-Text with Open-Source Language Models")
     st.markdown(
         """
-        **Upload an image**, extract text using a state-of-the-art OCR model, 
-        and get explanations or solutions using ChatGPT.
+        **Upload an image**, extract text using an open-source OCR model, 
+        and get explanations or text completions using a GPT-style open-source model.
         """
     )
 
@@ -59,9 +46,9 @@ def main():
                 st.write("### Extracted Text:")
                 st.write(f"`{extracted_text}`")  # Display the extracted text in a readable format
 
-                # Send text to ChatGPT
-                st.write("### ChatGPT Explanation:")
-                explanation = chat_with_gpt(extracted_text)
+                # Process extracted text with LLM
+                st.write("### Explanation/Completion:")
+                explanation = process_with_llm(extracted_text)
                 if explanation:
                     st.write(explanation)
             else: