Update pages/model.py

pages/model.py

@@ -0,0 +1,78 @@
+import streamlit as st
+import cv2
+import numpy as np
+import tempfile
+import os
+import easyocr
+
+from langchain.prompts import PromptTemplate
+from langchain.chains import LLMChain
+from langchain_huggingface import HuggingFaceEndpoint, ChatHuggingFace
+
+
+os.environ["HUGGINGFACEHUB_API_KEY"] = os.getenv("HF_TOKEN")
+
+st.set_page_config(page_title="🧠 MediAssist", layout="centered")
+
+st.markdown("<h1 style='text-align: center; color: #4A90E2;'>🧠 MediAssist</h1>", unsafe_allow_html=True)
+st.markdown("<h4 style='text-align: center;'>Upload a doctor's prescription and get detailed medicine analysis</h4><br>", unsafe_allow_html=True)
+
+uploaded_file = st.file_uploader("📤 Upload Prescription Image", type=["jpg", "jpeg", "png"])
+
+if uploaded_file:
+
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".png") as temp_file:
+        temp_file.write(uploaded_file.read())
+        img_path = temp_file.name
+
+    image = cv2.imread(img_path)
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    _, binary = cv2.threshold(gray, 130, 255, cv2.THRESH_BINARY_INV)
+    dilated = cv2.dilate(binary, np.ones((2, 2), np.uint8), iterations=1)
+
+    reader = easyocr.Reader(['en'], gpu=False)
+    extracted_text = "\n".join(reader.readtext(dilated, detail=0))
+
+    st.image(dilated, caption="🧾 Preprocessed Image", use_column_width=True)
+    st.markdown("### 📜 Extracted Text from Image")
+    st.code(extracted_text)
+
+    template = """
+    You're a medical assistant AI. Below is a doctor's handwritten prescription text:
+
+    {prescription_text}
+
+    Based on the text, please do the following:
+    1. Extract all medicine names (ignore other notes).
+    2. For each medicine, mention:
+       - When to take it (morning/night, before/after food)
+       - Dosage
+       - Possible side effects
+       - Any precautions or special instructions
+    Return results in clear bullet points.
+    """
+
+    prompt = PromptTemplate(input_variables=["prescription_text"], template=template)
+
+    llm = ChatHuggingFace(
+        llm=HuggingFaceEndpoint(
+            repo_id="mistralai/Mistral-7B-Instruct-v0.3",
+            provider="novita",
+            temperature=0.5,
+            max_new_tokens=500,
+            task="conversational"
+        )
+    )
+
+    chain = LLMChain(llm=llm, prompt=prompt)
+
+    if st.button("🔍 Analyze Prescription"):
+        with st.spinner("Analyzing the prescription..."):
+            response = chain.run(prescription_text=extracted_text)
+        st.markdown("### 💊 Medicine Summary")
+        st.success(response)
+
+    os.remove(img_path)
+
+else:
+    st.info("Upload a prescription image to start the analysis.")