Update app.py

app.py

@@ -39,7 +39,7 @@ API_URL1 = "https://api-inference.huggingface.co/models/Michael-Vptn/text-summar
 headers1 = {"Authorization": "Bearer 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"}
 API_URL2 = "https://api-inference.huggingface.co/models/gpt2"
 headers2 = {"Authorization": "Bearer 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"}
-	
+#@st.cache
 def read_pdf(file):
 #     images=pdf2image.convert_from_path(file)
 #     # print(type(images))
@@ -93,76 +93,77 @@ def change_photo_state():
 message = st.text_input("Type your text here!")
 uploaded_photo = st.file_uploader("Upload your Images/PDF",type=['jpg','png','jpeg','pdf'], on_change=change_photo_state)
 camera_photo = st.camera_input("Capture a photo to summarize: ", on_change=change_photo_state)
-if st.session_state["photo"]=="done" or message:
-    if uploaded_photo and uploaded_photo.type=='application/pdf':
-        tet = read_pdf(uploaded_photo)
-        # with tempfile.NamedTemporaryFile(delete=False) as temp_file:
-        #     temp_file.write(uploaded_photo.read())
-        #     temp_file_path = temp_file.name
-            
-        # loader = PyPDFLoader(temp_file_path)
-        # if loader:
-        #     text.extend(loader.load())
-        #     os.remove(temp_file_path)
-        # text_splitter = CharacterTextSplitter(separator="\n", chunk_size=1000, chunk_overlap=100, length_function=len)
-        # text_chunks = text_splitter.split_documents(text)
-        values = st.slider('Select a approximate number of lines to see and summarize',value=[0, len(tet)//(7*100)])
-        text = tet[values[0]*7*10:values[1]*10*100] if values[0]!=len(tet)//(10*100) else tet[len(tet)//(10*100):]
-        #st.success(type(text_chunks))
-        if st.button("English Pdf Summarize"):
-            st.subheader("Selected text for summarize: ")
-            st.success(text)
-            st.subheader("Summarized Text: ")
-            engsum(text)
-    
-    elif uploaded_photo and uploaded_photo.type !='application/pdf':
-        text=None
-        img = Image.open(uploaded_photo)
-        img = img.save("img.png")
-        img = cv2.imread("img.png")
-        st.text("Select the summarization type:")
-        if st.button("BENGALI"):
-            text =  pytesseract.image_to_string(img, lang="ben")
-            st.subheader("সারাংশ/সারমর্ম")
-            bansum(text)
-        if st.button("ENGLISH"): 
-            text=pytesseract.image_to_string(img)
-            st.subheader("Summarized Text")
-            engsum(text)
-        #st.success(text)
-    elif camera_photo:
-        text=None
-        img = Image.open(camera_photo)
-        img = img.save("img.png")
-        img = cv2.imread("img.png")
-        #text = pytesseract.image_to_string(img) if st.checkbox("Bangla") else pytesseract.image_to_string(img, lang="ben")
-        st.text("Select the summarization type:")
-        if st.button("Bangla"):
-            text =  pytesseract.image_to_string(img, lang="ben")
-            st.subheader("সারাংশ/সারমর্ম")
-            bansum(text)
-        if st.button("English"): 
-            text=pytesseract.image_to_string(img)
-            st.subheader("Summarized Text")
-            engsum(text)
-    else:
-        text=None
-        text = message
-        if st.button("Bangla"):
-            bansum(text)
-        if st.button("English"): 
-            engsum(text)  
-    # if st.button("English Text Generation"): 
-    #     def query(payload):
-    #     	response = requests.post(API_URL2, headers=headers2, json=payload)
-    #     	return response.json()
-            
-    #     out = query({
-    #     	"inputs": text,
-    #     })
-    #     if isinstance(out, list) and out[0].get("generated_text"):
-    #         text_output = out[0]["generated_text"]
-    #         st.success(text_output)
-    #         #text=text_output
+if st.checkbox("Summarization"):
+    if st.session_state["photo"]=="done" or message:
+        if uploaded_photo and uploaded_photo.type=='application/pdf':
+            tet = read_pdf(uploaded_photo)
+            # with tempfile.NamedTemporaryFile(delete=False) as temp_file:
+            #     temp_file.write(uploaded_photo.read())
+            #     temp_file_path = temp_file.name
+                
+            # loader = PyPDFLoader(temp_file_path)
+            # if loader:
+            #     text.extend(loader.load())
+            #     os.remove(temp_file_path)
+            # text_splitter = CharacterTextSplitter(separator="\n", chunk_size=1000, chunk_overlap=100, length_function=len)
+            # text_chunks = text_splitter.split_documents(text)
+            values = st.slider('Select a approximate number of lines to see and summarize',value=[0, len(tet)//(7*100)])
+            text = tet[values[0]*7*10:values[1]*10*100] if values[0]!=len(tet)//(10*100) else tet[len(tet)//(10*100):]
+            #st.success(type(text_chunks))
+            if st.button("English Pdf Summarize"):
+                st.subheader("Selected text for summarize: ")
+                st.success(text)
+                st.subheader("Summarized Text: ")
+                engsum(text)
+        
+        elif uploaded_photo and uploaded_photo.type !='application/pdf':
+            text=None
+            img = Image.open(uploaded_photo)
+            img = img.save("img.png")
+            img = cv2.imread("img.png")
+            st.text("Select the summarization type:")
+            if st.button("BENGALI"):
+                text =  pytesseract.image_to_string(img, lang="ben")
+                st.subheader("সারাংশ/সারমর্ম")
+                bansum(text)
+            if st.button("ENGLISH"): 
+                text=pytesseract.image_to_string(img)
+                st.subheader("Summarized Text")
+                engsum(text)
+            #st.success(text)
+        elif camera_photo:
+            text=None
+            img = Image.open(camera_photo)
+            img = img.save("img.png")
+            img = cv2.imread("img.png")
+            #text = pytesseract.image_to_string(img) if st.checkbox("Bangla") else pytesseract.image_to_string(img, lang="ben")
+            st.text("Select the summarization type:")
+            if st.button("Bangla"):
+                text =  pytesseract.image_to_string(img, lang="ben")
+                st.subheader("সারাংশ/সারমর্ম")
+                bansum(text)
+            if st.button("English"): 
+                text=pytesseract.image_to_string(img)
+                st.subheader("Summarized Text")
+                engsum(text)
+        else:
+            text=None
+            text = message
+            if st.button("Bangla"):
+                bansum(text)
+            if st.button("English"): 
+                engsum(text)  
+        # if st.button("English Text Generation"): 
+        #     def query(payload):
+        #     	response = requests.post(API_URL2, headers=headers2, json=payload)
+        #     	return response.json()
+                
+        #     out = query({
+        #     	"inputs": text,
+        #     })
+        #     if isinstance(out, list) and out[0].get("generated_text"):
+        #         text_output = out[0]["generated_text"]
+        #         st.success(text_output)
+        #         #text=text_output