Update app.py

app.py

@@ -6,8 +6,10 @@ import os
 
 from langchain_community.document_loaders import UnstructuredImageLoader
 from langchain.prompts import PromptTemplate
-from langchain_huggingface import HuggingFaceEndpoint, ChatHuggingFace
+from langchain.chains import LLMChain
+from langchain_huggingface import HuggingFaceEndpoint
 
+# Set Hugging Face API keys
 os.environ["HUGGINGFACEHUB_API_KEY"] = os.getenv("HF")
 os.environ["HF_TOKEN"] = os.getenv("HF")
 
@@ -17,7 +19,6 @@ st.set_page_config(
     page_icon="💊"
 )
 
-# Sidebar
 st.sidebar.title("💊 MediAssist")
 st.sidebar.markdown("Analyze prescriptions with ease using AI")
 st.sidebar.markdown("---")
@@ -33,10 +34,8 @@ st.sidebar.markdown("""
     </a>
 </div>
 """, unsafe_allow_html=True)
-
 st.sidebar.markdown("---")
 
-# Main Title
 st.markdown("""
     <h1 style='text-align: center; color: #4A90E2;'>🧠 MediAssist</h1>
     <h3 style='text-align: center;'>Prescription Analyzer using AI and OCR</h3>
@@ -44,40 +43,48 @@ st.markdown("""
     <br>
 """, unsafe_allow_html=True)
 
-# Image Upload
 uploaded_file = st.file_uploader("📤 Upload Prescription Image (JPG/PNG)", type=["jpg", "jpeg", "png"])
 
- 
-dilated_path = orig_path.replace(".png", "_dilated.png")
-cv2.imwrite(dilated_path, dilated)
+if uploaded_file:
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".png") as temp_file:
+        temp_file.write(uploaded_file.read())
+        orig_path = temp_file.name
 
-# Load and extract text
-loader = UnstructuredImageLoader(dilated_path)
-documents = loader.load()
-extracted_text = "\n".join([doc.page_content for doc in documents])
+    # Step 1: Read and preprocess image
+    image = cv2.imread(orig_path)
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    _, binary_inv = cv2.threshold(gray, 128, 255, cv2.THRESH_BINARY_INV)
+    kernel = np.ones((3, 3), np.uint8)
+    dilated = cv2.dilate(binary_inv, kernel, iterations=1)
 
-# Define prompt
-template = """
-You are a helpful assistant. Here is a prescription text extracted from an image:
+    # Save preprocessed image for OCR
+    dilated_path = orig_path.replace(".png", "_dilated.png")
+    cv2.imwrite(dilated_path, dilated)
 
-{prescription_text}
+    loader = UnstructuredImageLoader(dilated_path)
+    documents = loader.load()
+    extracted_text = "\n".join([doc.page_content for doc in documents])
 
-Please summarize the key medicine names and instructions in bullet points.
-"""
-prompt = PromptTemplate(input_variables=["prescription_text"], template=template)
+    template = """
+    You are a helpful assistant. Here is a prescription text extracted from an image:
 
-model = HuggingFaceEndpoint(
-    repo_id="mistralai/Mistral-7B-Instruct-v0.3",
-    provider="novita",
-    temperature=0.6,
-    max_new_tokens=300,
-    task="conversational"
-)
+    {prescription_text}
 
-chain = LLMChain(llm=model, prompt=prompt)
+    Please summarize the key medicine names and instructions in bullet points.
+    """
+    prompt = PromptTemplate(input_variables=["prescription_text"], template=template)
 
-# Columns for layout
-col1, col2 = st.columns([1, 2])
+    model = HuggingFaceEndpoint(
+        repo_id="mistralai/Mistral-7B-Instruct-v0.3",
+        provider="novita",
+        temperature=0.6,
+        max_new_tokens=300,
+        task="conversational"
+    )
+    chain = LLMChain(llm=model, prompt=prompt)
+
+   
+    col1, col2 = st.columns([1, 2])
 
     with col1:
         st.image(dilated, caption="Preprocessed Prescription", channels="GRAY", use_container_width=True)
@@ -87,13 +94,21 @@ col1, col2 = st.columns([1, 2])
 
         st.markdown("### 📜 Extracted Text")
         st.code(extracted_text)
-    
+
         if st.button("🔍 Analyze Text"):
             with st.spinner("Analyzing..."):
                 response = chain.run(prescription_text=extracted_text)
-            # st.markdown("### LLM Output")
             st.success(response)
 
+    # Cleanup temp files
+    os.remove(orig_path)
+    os.remove(dilated_path)
+
+else:
+    st.image("https://cdn.dribbble.com/users/285475/screenshots/14711920/media/bd46dc2873f7099e4ef9fd53e6f7f1df.png", width=600)
+    st.markdown("<center><i>Upload a prescription image to begin analysis.</i></center>", unsafe_allow_html=True)
+
+
 
         # st.markdown("### 🌐 Translated Text")
         # st.code("पेरासिटामोल 500 मिलीग्राम\nभोजन के बाद दिन में दो बार 1 गोली लें", language='text')
@@ -104,10 +119,6 @@ col1, col2 = st.columns([1, 2])
         # st.markdown("### ⚠️ Possible Side Effects")
         # st.warning("- Nausea\n- Dizziness\n- Liver damage (on overdose)")
 
-    os.remove(temp_path)
-    os.remove(orig_path)
-    os.remove(dilated_path)
-
-else:
-    st.image("https://cdn.dribbble.com/users/285475/screenshots/14711920/media/bd46dc2873f7099e4ef9fd53e6f7f1df.png", width=600)
-    st.markdown("<center><i>Upload a prescription image to begin analysis.</i></center>", unsafe_allow_html=True)
+    # os.remove(temp_path)
+    # os.remove(orig_path)
+    # os.remove(dilated_path)