Update app.py

app.py

@@ -1,183 +1,183 @@
-import os
-import json
-import streamlit as st
-from PIL import Image, UnidentifiedImageError, ExifTags
-import requests
-from io import BytesIO
-import wikipedia
-from easygoogletranslate import EasyGoogleTranslate
-from BharatCaptioner import identify_landmark
-from groq import Groq
-import hashlib
-
-# Initialize EasyGoogleTranslate
-translator = EasyGoogleTranslate(source_language="en", target_language="hi", timeout=10)
-
-# Load configuration for Groq API key
-working_dir = os.path.dirname(os.path.abspath(__file__))
-config_data = json.load(open(f"{working_dir}/config.json"))
-GROQ_API_KEY = config_data["GROQ_API_KEY"]
-os.environ["GROQ_API_KEY"] = GROQ_API_KEY
-
-client = Groq()
-
-# Title of the Streamlit app
-st.title("BharatCaptioner with Conversational Chatbot")
-st.write(
-    "A tool to identify/describe Indian Landmarks in Indic Languages and chat about the image."
-)
-
-# Sidebar details
-st.sidebar.title("Developed by Harshal and Harsh Pandey")
-st.sidebar.write(
-    "**For the Model that I trained**: [Mail me here](mailto:harshal19052003@gmail.com)"
-)
-st.sidebar.write(
-    "**For the Code**: [GitHub Repo](https://github.com/justharshal2023/BharatCaptioner)"
-)
-st.sidebar.write(
-    "**Connect with me**: [LinkedIn](https://www.linkedin.com/in/harshal-123a90250/)"
-)
-
-# Image upload or URL input
-uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
-url = st.text_input("Or enter a valid image URL...")
-
-image = None
-error_message = None
-landmark = None
-summary = None
-caption = None
-
-
-# Function to correct image orientation
-def correct_image_orientation(img):
-    try:
-        for orientation in ExifTags.TAGS.keys():
-            if ExifTags.TAGS[orientation] == "Orientation":
-                break
-        exif = img._getexif()
-        if exif is not None:
-            orientation = exif[orientation]
-            if orientation == 3:
-                img = img.rotate(180, expand=True)
-            elif orientation == 6:
-                img = img.rotate(270, expand=True)
-            elif orientation == 8:
-                img = img.rotate(90, expand=True)
-    except (AttributeError, KeyError, IndexError):
-        pass
-    return img
-
-
-# Function to get a unique hash for the image
-def get_image_hash(image):
-    img_bytes = image.tobytes()
-    return hashlib.md5(img_bytes).hexdigest()
-
-
-# Check if new image or URL is uploaded and reset the chat history
-def reset_chat_if_new_image():
-    if "last_uploaded_hash" not in st.session_state:
-        st.session_state["last_uploaded_hash"] = None
-
-    # Process the new image or URL
-    if uploaded_file:
-        image = Image.open(uploaded_file)
-        image = correct_image_orientation(image)
-        new_image_hash = get_image_hash(image)
-    elif url:
-        try:
-            response = requests.get(url)
-            response.raise_for_status()
-            image = Image.open(BytesIO(response.content))
-            image = correct_image_orientation(image)
-            new_image_hash = get_image_hash(image)
-        except (requests.exceptions.RequestException, UnidentifiedImageError):
-            image = None
-            new_image_hash = None
-            error_message = (
-                "Error: The provided URL is invalid or the image could not be loaded."
-            )
-            st.error(error_message)
-    else:
-        image = None
-        new_image_hash = None
-
-    # If the image is new, reset the chat and session state
-    if new_image_hash and new_image_hash != st.session_state["last_uploaded_hash"]:
-        st.session_state.clear()
-        st.session_state["last_uploaded_hash"] = new_image_hash
-        st.experimental_rerun()
-
-    return image
-
-
-# Call the reset function to check for new images or URL
-image = reset_chat_if_new_image()
-
-# If an image is provided
-if image is not None:
-    # Resize image for processing
-    image = image.resize((256, 256))
-
-    # Identify the landmark using BharatCaptioner
-    landmark, prob = identify_landmark(image)
-    summary = wikipedia.summary(landmark, sentences=3)  # Shortened summary
-    st.write(f"**Landmark Identified:** {landmark} (Confidence: {prob:.2f})")
-
-    # Display image and landmark name in the sidebar
-    with st.sidebar:
-        st.image(image, caption="Current Image", use_column_width=True)
-        st.write(f"**Landmark:** {landmark}")
-
-    # Chatbot functionality
-    st.write("### Chat with the Chatbot about the Image")
-    caption = f"The landmark in the image is {landmark}. {summary}"
-
-    # Initialize chat history in session state if not present
-    if "chat_history" not in st.session_state:
-        st.session_state["chat_history"] = []
-
-    # Chatbot introduction message with bold text for landmark and question
-    if not st.session_state.get("chatbot_started"):
-        chatbot_intro = f"Hello! I see the image is of **{landmark}**. {summary} **Would you like to know more** about this landmark?"
-        st.session_state["chat_history"].append(
-            {"role": "assistant", "content": chatbot_intro}
-        )
-        st.session_state["chatbot_started"] = True
-
-    # Display chat history
-    for message in st.session_state.chat_history:
-        with st.chat_message(message["role"]):
-            st.markdown(message["content"])
-
-    # User input
-    user_prompt = st.chat_input("Ask the Chatbot about the image...")
-
-    if user_prompt:
-        st.chat_message("user").markdown(user_prompt)
-        st.session_state.chat_history.append({"role": "user", "content": user_prompt})
-
-        # Send the user's message to the LLaMA chatbot
-        messages = [
-            {
-                "role": "system",
-                "content": "You are a helpful image conversational assistant. "
-                + f"The caption of the image is: {caption}",
-            },
-            *st.session_state.chat_history,
-        ]
-
-        response = client.chat.completions.create(
-            model="llama-3.1-8b-instant", messages=messages
-        )
-
-        assistant_response = response.choices[0].message.content
-        st.session_state.chat_history.append(
-            {"role": "assistant", "content": assistant_response}
-        )
-
-        # Display chatbot response
-        with st.chat_message("assistant"):
-            st.markdown(assistant_response)
+import os
+import json
+import streamlit as st
+from PIL import Image, UnidentifiedImageError, ExifTags
+import requests
+from io import BytesIO
+import wikipedia
+from easygoogletranslate import EasyGoogleTranslate
+from BharatCaptioner import identify_landmark
+from groq import Groq
+import hashlib
+
+# Initialize EasyGoogleTranslate
+translator = EasyGoogleTranslate(source_language="en", target_language="hi", timeout=10)
+
+# Load configuration for Groq API key
+working_dir = os.path.dirname(os.path.abspath(__file__))
+config_data = json.load(open(f"{working_dir}/config.json"))
+GROQ_API_KEY = config_data["GROQ_API_KEY"]
+os.environ["GROQ_API_KEY"] = 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
+
+client = Groq()
+
+# Title of the Streamlit app
+st.title("BharatCaptioner with Conversational Chatbot")
+st.write(
+    "A tool to identify/describe Indian Landmarks in Indic Languages and chat about the image."
+)
+
+# Sidebar details
+st.sidebar.title("Developed by Harshal and Harsh Pandey")
+st.sidebar.write(
+    "**For the Model that I trained**: [Mail me here](mailto:harshal19052003@gmail.com)"
+)
+st.sidebar.write(
+    "**For the Code**: [GitHub Repo](https://github.com/justharshal2023/BharatCaptioner)"
+)
+st.sidebar.write(
+    "**Connect with me**: [LinkedIn](https://www.linkedin.com/in/harshal-123a90250/)"
+)
+
+# Image upload or URL input
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+url = st.text_input("Or enter a valid image URL...")
+
+image = None
+error_message = None
+landmark = None
+summary = None
+caption = None
+
+
+# Function to correct image orientation
+def correct_image_orientation(img):
+    try:
+        for orientation in ExifTags.TAGS.keys():
+            if ExifTags.TAGS[orientation] == "Orientation":
+                break
+        exif = img._getexif()
+        if exif is not None:
+            orientation = exif[orientation]
+            if orientation == 3:
+                img = img.rotate(180, expand=True)
+            elif orientation == 6:
+                img = img.rotate(270, expand=True)
+            elif orientation == 8:
+                img = img.rotate(90, expand=True)
+    except (AttributeError, KeyError, IndexError):
+        pass
+    return img
+
+
+# Function to get a unique hash for the image
+def get_image_hash(image):
+    img_bytes = image.tobytes()
+    return hashlib.md5(img_bytes).hexdigest()
+
+
+# Check if new image or URL is uploaded and reset the chat history
+def reset_chat_if_new_image():
+    if "last_uploaded_hash" not in st.session_state:
+        st.session_state["last_uploaded_hash"] = None
+
+    # Process the new image or URL
+    if uploaded_file:
+        image = Image.open(uploaded_file)
+        image = correct_image_orientation(image)
+        new_image_hash = get_image_hash(image)
+    elif url:
+        try:
+            response = requests.get(url)
+            response.raise_for_status()
+            image = Image.open(BytesIO(response.content))
+            image = correct_image_orientation(image)
+            new_image_hash = get_image_hash(image)
+        except (requests.exceptions.RequestException, UnidentifiedImageError):
+            image = None
+            new_image_hash = None
+            error_message = (
+                "Error: The provided URL is invalid or the image could not be loaded."
+            )
+            st.error(error_message)
+    else:
+        image = None
+        new_image_hash = None
+
+    # If the image is new, reset the chat and session state
+    if new_image_hash and new_image_hash != st.session_state["last_uploaded_hash"]:
+        st.session_state.clear()
+        st.session_state["last_uploaded_hash"] = new_image_hash
+        st.experimental_rerun()
+
+    return image
+
+
+# Call the reset function to check for new images or URL
+image = reset_chat_if_new_image()
+
+# If an image is provided
+if image is not None:
+    # Resize image for processing
+    image = image.resize((256, 256))
+
+    # Identify the landmark using BharatCaptioner
+    landmark, prob = identify_landmark(image)
+    summary = wikipedia.summary(landmark, sentences=3)  # Shortened summary
+    st.write(f"**Landmark Identified:** {landmark} (Confidence: {prob:.2f})")
+
+    # Display image and landmark name in the sidebar
+    with st.sidebar:
+        st.image(image, caption="Current Image", use_column_width=True)
+        st.write(f"**Landmark:** {landmark}")
+
+    # Chatbot functionality
+    st.write("### Chat with the Chatbot about the Image")
+    caption = f"The landmark in the image is {landmark}. {summary}"
+
+    # Initialize chat history in session state if not present
+    if "chat_history" not in st.session_state:
+        st.session_state["chat_history"] = []
+
+    # Chatbot introduction message with bold text for landmark and question
+    if not st.session_state.get("chatbot_started"):
+        chatbot_intro = f"Hello! I see the image is of **{landmark}**. {summary} **Would you like to know more** about this landmark?"
+        st.session_state["chat_history"].append(
+            {"role": "assistant", "content": chatbot_intro}
+        )
+        st.session_state["chatbot_started"] = True
+
+    # Display chat history
+    for message in st.session_state.chat_history:
+        with st.chat_message(message["role"]):
+            st.markdown(message["content"])
+
+    # User input
+    user_prompt = st.chat_input("Ask the Chatbot about the image...")
+
+    if user_prompt:
+        st.chat_message("user").markdown(user_prompt)
+        st.session_state.chat_history.append({"role": "user", "content": user_prompt})
+
+        # Send the user's message to the LLaMA chatbot
+        messages = [
+            {
+                "role": "system",
+                "content": "You are a helpful image conversational assistant. "
+                + f"The caption of the image is: {caption}",
+            },
+            *st.session_state.chat_history,
+        ]
+
+        response = client.chat.completions.create(
+            model="llama-3.1-8b-instant", messages=messages
+        )
+
+        assistant_response = response.choices[0].message.content
+        st.session_state.chat_history.append(
+            {"role": "assistant", "content": assistant_response}
+        )
+
+        # Display chatbot response
+        with st.chat_message("assistant"):
+            st.markdown(assistant_response)