Update app.py

app.py

@@ -82,123 +82,123 @@ def bansum(text):
     if isinstance(out, list) and out[0].get("summary_text"):
        text_output = out[0]["summary_text"]
        st.success(text_output) 
-
+    
+@st.cache
+def save(l):
+    return l
 #@st.cache
 def main():
+    camera_photo=None
     import streamlit as st
-    import time
-    if st.checkbox("Summarize"):
-        if "photo" not in st.session_state:
-            st.session_state["photo"]="not done"
-        def change_photo_state():
-            st.session_state["photo"]="done"
+    if "photo" not in st.session_state:
+        st.session_state["photo"]="not done"
+    def change_photo_state():
+        st.session_state["photo"]="done"
+    with st.container():
         c1, c2, c3 = st.columns([2,2,1])
-        with st.container():
-            message = c1.text_input("Type your text here!")
-            uploaded_photo=None
-            camera_photo=None
-            if c2.button("CaptureImage"):
-                camera_photo = c2.camera_input("Capture a photo to summarize: ", on_change=change_photo_state)
-            if c2.button("Stop camera"):
+        message = c1.text_input("Type your text here!")
+        if st.button("CaptureImage"):
+            camera_photo = c2.camera_input("Capture a photo to summarize: ", on_change=change_photo_state)
+            if st.button("Stop camera"):
                 CaptureImage =False
-            uploaded_photo = c3.file_uploader("Upload your Images/PDF",type=['jpg','png','jpeg','pdf'], 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.checkbox("Conversate"):         
-        Summarize=False
-        with st.container():
-            from streamlit_chat import message as st_message
-            from transformers import BlenderbotTokenizer
-            from transformers import BlenderbotForConditionalGeneration
-            st.title("Chatbot!!!")
             
-            @st.experimental_singleton
-            def get_models():
-                # it may be necessary for other frameworks to cache the model
-                # seems pytorch keeps an internal state of the conversation
-                model_name = "facebook/blenderbot-400M-distill"
-                tokenizer = BlenderbotTokenizer.from_pretrained(model_name)
-                model = BlenderbotForConditionalGeneration.from_pretrained(model_name)
-                return tokenizer, model
-            if "history" not in st.session_state:
-                st.session_state.history = []
-            def generate_answer():
-                tokenizer, model = get_models()
-                user_message = st.session_state.input_text
-                inputs = tokenizer(st.session_state.input_text, return_tensors="pt")
-                result = model.generate(**inputs)
-                message_bot = tokenizer.decode(
-                    result[0], skip_special_tokens=True
-                )  # .replace("<s>", "").replace("</s>", "")
-                st.session_state.history.append({"message": user_message, "is_user": True})
-                st.session_state.history.append({"message": message_bot, "is_user": False})
-            from copyreg import clear_extension_cache
-            for chat in st.session_state.history:
-                st_message(**chat)
-            st.text_input("Talk to the bot", key="input_text", on_change=generate_answer)
-            if st.button("Refresh/New Chat"):
-               st.session_state.history = []
+        uploaded_photo = save(c3.file_uploader("Upload your Images/PDF",type=['jpg','png','jpeg','pdf'], 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:")
+                c4, c5 = st.columns([1,1])
+                if c4.button("BENGALI"):
+                    text =  pytesseract.image_to_string(img, lang="ben")
+                    st.subheader("সারাংশ/সারমর্ম")
+                    bansum(text)
+                if c5.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:")
+                c6, c7 = st.columns([1,1])
+                if c6.button("Bangla"):
+                    text =  pytesseract.image_to_string(img, lang="ben")
+                    st.subheader("সারাংশ/সারমর্ম")
+                    bansum(text)
+                if c7.button("English"): 
+                    text=pytesseract.image_to_string(img)
+                    st.subheader("Summarized Text")
+                    engsum(text)
+            else:
+                text=None
+                text = message
+                c8, c9 = st.columns([1,1])
+                if c8.button("Bangla"):
+                    bansum(text)
+                if c9.button("English"): 
+                    engsum(text) 
+             
+    with st.container():
+        from streamlit_chat import message as st_message
+        from transformers import BlenderbotTokenizer
+        from transformers import BlenderbotForConditionalGeneration
+        st.title("Chatbot!!!")
+        
+        @st.experimental_singleton
+        def get_models():
+            # it may be necessary for other frameworks to cache the model
+            # seems pytorch keeps an internal state of the conversation
+            model_name = "facebook/blenderbot-400M-distill"
+            tokenizer = BlenderbotTokenizer.from_pretrained(model_name)
+            model = BlenderbotForConditionalGeneration.from_pretrained(model_name)
+            return tokenizer, model
+        if "history" not in st.session_state:
+            st.session_state.history = []
+        st.title("Hello bot: ")
+        def generate_answer():
+            tokenizer, model = get_models()
+            user_message = st.session_state.input_text
+            inputs = tokenizer(st.session_state.input_text, return_tensors="pt")
+            result = model.generate(**inputs)
+            message_bot = tokenizer.decode(
+                result[0], skip_special_tokens=True
+            )  # .replace("<s>", "").replace("</s>", "")
+            st.session_state.history.append({"message": user_message, "is_user": True})
+            st.session_state.history.append({"message": message_bot, "is_user": False})
+        st.text_input("Talk to the bot", key="input_text", on_change=generate_answer)
+        from copyreg import clear_extension_cache
+        for chat in st.session_state.history:
+            st_message(**chat)
+        if st.button("Refresh/New Chat"):
+           st.session_state.history = []
             
-
-
 if __name__ == "__main__":
     main()