Update app.py

app.py

@@ -1,135 +1,137 @@
-import gradio as gr
-import pymongo
-import certifi
-from llama_index.core import VectorStoreIndex
-from llama_index.embeddings.huggingface import HuggingFaceEmbedding
-from llama_index.llms.groq import Groq
-from llama_index.vector_stores.mongodb import MongoDBAtlasVectorSearch
-from llama_index.core.prompts import PromptTemplate
-from dotenv import load_dotenv
-import os
-import base64
-import markdown as md
-
-# Load environment variables
-load_dotenv()
-
-# --- MongoDB Config ---
-# ATLAS_CONNECTION_STRING = "mongodb+srv://sarath:sarath@itihasa.wkacol3.mongodb.net/?retryWrites=true&w=majority&appName=Itihasa"
-ATLAS_CONNECTION_STRING = os.getenv("ATLAS_CONNECTION_STRING")
-DB_NAME = "RAG"
-COLLECTION_NAME = "ramayana"
-VECTOR_INDEX_NAME = "ramayana_vector_index"
-
-# --- Embedding Model ---
-embed_model = HuggingFaceEmbedding(model_name="intfloat/multilingual-e5-base")
-
-# --- Prompt Template ---
-ramayana_qa_template = PromptTemplate(
-    """You are an expert on the Valmiki Ramayana and a guide who always inspires people with the great Itihasa like the Ramayana.
-
-    Below is text from the epic, including shlokas and their explanations:
-    ---------------------
-    {context_str}
-    ---------------------
-
-    Using only this information, answer the following query.
-
-    Query: {query_str}
-
-    Answer:
-     - Intro or general description to ```Query```
-     - Related shloka/shlokas followed by its explanation
-     - Overview of ```Query```
-    """
-)
-
-# --- Connect to MongoDB once at startup ---
-def get_vector_index_once():
-    mongo_client = pymongo.MongoClient(
-        ATLAS_CONNECTION_STRING,
-        tlsCAFile=certifi.where(),
-        tlsAllowInvalidCertificates=False,
-        connectTimeoutMS=30000,
-        serverSelectionTimeoutMS=30000,
-    )
-    mongo_client.server_info()
-    print("✅ Connected to MongoDB Atlas.")
-
-    vector_store = MongoDBAtlasVectorSearch(
-        mongo_client,
-        db_name=DB_NAME,
-        collection_name=COLLECTION_NAME,
-        vector_index_name=VECTOR_INDEX_NAME,
-    )
-    return VectorStoreIndex.from_vector_store(vector_store, embed_model=embed_model)
-
-# Connect once
-vector_index = get_vector_index_once()
-
-# --- Respond Function (uses API key from state) ---
-def chat_with_groq(message, history, groq_key):
-    llm = Groq(model="llama-3.1-8b-instant", api_key=groq_key)
-
-    query_engine = vector_index.as_query_engine(
-        llm=llm,
-        text_qa_template=ramayana_qa_template,
-        similarity_top_k=5,
-        verbose=True,
-    )
-
-    response = query_engine.query(message)
-    return str(response)
-
-def encode_image(image_path):
-    with open(image_path, "rb") as image_file:
-        return base64.b64encode(image_file.read()).decode('utf-8')
-
-# Encode the images
-github_logo_encoded = encode_image("Images/github-logo.png")
-linkedin_logo_encoded = encode_image("Images/linkedin-logo.png")
-website_logo_encoded = encode_image("Images/ai-logo.png")
-
-# --- Gradio UI ---
-with gr.Blocks(theme=gr.themes.Soft(font=[gr.themes.GoogleFont("Roboto Mono")]), css='footer {visibility: hidden}') as demo:
-    with gr.Tabs():
-        with gr.TabItem("Intro"):
-            gr.Markdown(md.description)
-
-        with gr.TabItem("GPT"):
-            with gr.Column(visible=True) as accordion_container:
-                with gr.Accordion("How to get Groq API KEY", open=False):
-                    gr.Markdown(md.groq_api_key)
-
-            groq_key_box = gr.Textbox(
-                    label="Enter Groq API Key",
-                    type="password",
-                    placeholder="Paste your Groq API key here..."
-                )
-
-            start_btn = gr.Button("Start Chat")
-
-            groq_state = gr.State(value="")
-
-            # Chat container, initially hidden
-            with gr.Column(visible=False) as chatbot_container:
-                chatbot = gr.ChatInterface(
-                    fn=lambda message, history, groq_key: chat_with_groq(message, history, groq_key),
-                    additional_inputs=[groq_state],
-                    title="🕉️ RamayanaGPT",
-                    # description="Ask questions from the Valmiki Ramayana. Powered by RAG + MongoDB + LlamaIndex.",
-                )
-
-            # Show chat and hide inputs
-            def save_key_and_show_chat(key):
-                return key, gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=True)
-
-            start_btn.click(
-                fn=save_key_and_show_chat,
-                inputs=[groq_key_box],
-                outputs=[groq_state, groq_key_box, start_btn, accordion_container, chatbot_container]
-            )
-        gr.HTML(md.footer.format(github_logo_encoded, linkedin_logo_encoded, website_logo_encoded))
-
-if __name__ == "__main__":
-    demo.launch()
+import gradio as gr
+import pymongo
+import certifi
+from llama_index.core import VectorStoreIndex
+from llama_index.embeddings.huggingface import HuggingFaceEmbedding
+from llama_index.llms.groq import Groq
+from llama_index.vector_stores.mongodb import MongoDBAtlasVectorSearch
+from llama_index.core.prompts import PromptTemplate
+from dotenv import load_dotenv
+import os
+import base64
+import markdown as md
+
+# Load environment variables
+load_dotenv()
+
+# --- MongoDB Config ---
+ATLAS_CONNECTION_STRING = os.getenv("ATLAS_CONNECTION_STRING")
+DB_NAME = "RAG"
+COLLECTION_NAME = "ramayana"
+VECTOR_INDEX_NAME = "ramayana_vector_index"
+
+# --- Embedding Model ---
+embed_model = HuggingFaceEmbedding(model_name="intfloat/multilingual-e5-base")
+
+# --- Prompt Template ---
+ramayana_qa_template = PromptTemplate(
+    """You are an expert on the Valmiki Ramayana and a guide who always inspires people with the great Itihasa like the Ramayana.
+
+    Below is text from the epic, including shlokas and their explanations:
+    ---------------------
+    {context_str}
+    ---------------------
+
+    Using only this information, answer the following query.
+
+    Query: {query_str}
+
+    Answer:
+     - Intro or general description to ```Query```
+     - Related shloka/shlokas followed by its explanation
+     - Overview of ```Query```
+    """
+)
+
+# --- Connect to MongoDB once at startup ---
+def get_vector_index_once():
+    mongo_client = pymongo.MongoClient(
+        ATLAS_CONNECTION_STRING,
+        tlsCAFile=certifi.where(),
+        tlsAllowInvalidCertificates=False,
+        connectTimeoutMS=30000,
+        serverSelectionTimeoutMS=30000,
+    )
+    mongo_client.server_info()
+    print("✅ Connected to MongoDB Atlas.")
+
+    vector_store = MongoDBAtlasVectorSearch(
+        mongo_client,
+        db_name=DB_NAME,
+        collection_name=COLLECTION_NAME,
+        vector_index_name=VECTOR_INDEX_NAME,
+    )
+    return VectorStoreIndex.from_vector_store(vector_store, embed_model=embed_model)
+
+# Connect once
+vector_index = get_vector_index_once()
+
+# --- Respond Function (uses API key from state) ---
+def chat_with_groq(message, history, groq_key):
+    llm = Groq(model="llama-3.1-8b-instant", api_key=groq_key)
+
+    query_engine = vector_index.as_query_engine(
+        llm=llm,
+        text_qa_template=ramayana_qa_template,
+        similarity_top_k=5,
+        verbose=True,
+    )
+
+    response = query_engine.query(message)
+    return str(response)
+
+def encode_image(image_path):
+    with open(image_path, "rb") as image_file:
+        return base64.b64encode(image_file.read()).decode('utf-8')
+
+# Encode the images
+github_logo_encoded = encode_image("Images/github-logo.png")
+linkedin_logo_encoded = encode_image("Images/linkedin-logo.png")
+website_logo_encoded = encode_image("Images/ai-logo.png")
+
+# --- Gradio UI ---
+with gr.Blocks(theme=gr.themes.Soft(font=[gr.themes.GoogleFont("Roboto Mono")]), css='footer {visibility: hidden}') as demo:
+    with gr.Tabs():
+        with gr.TabItem("Intro"):
+            gr.Markdown(md.description)
+
+        with gr.TabItem("GPT"):
+            with gr.Column(visible=True) as accordion_container:
+                with gr.Accordion("How to get Groq API KEY", open=False):
+                    gr.Markdown(md.groq_api_key)
+
+            groq_key_box = gr.Textbox(
+                    label="Enter Groq API Key",
+                    type="password",
+                    placeholder="Paste your Groq API key here..."
+                )
+
+            start_btn = gr.Button("Start Chat")
+
+            groq_state = gr.State(value="")
+
+            # Chat container, initially hidden
+            with gr.Column(visible=False) as chatbot_container:
+                chatbot = gr.ChatInterface(
+                    fn=lambda message, history, groq_key: chat_with_groq(message, history, groq_key),
+                    additional_inputs=[groq_state],
+                    chatbot=gr.Chatbot(height=500),
+                    title="🕉️ RamayanaGPT",
+                    show_progress="full",
+                    fill_height=True,
+                    # description="Ask questions from the Valmiki Ramayana. Powered by RAG + MongoDB + LlamaIndex.",
+                )
+
+            # Show chat and hide inputs
+            def save_key_and_show_chat(key):
+                return key, gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=True)
+
+            start_btn.click(
+                fn=save_key_and_show_chat,
+                inputs=[groq_key_box],
+                outputs=[groq_state, groq_key_box, start_btn, accordion_container, chatbot_container]
+            )
+        gr.HTML(md.footer.format(github_logo_encoded, linkedin_logo_encoded, website_logo_encoded))
+
+if __name__ == "__main__":
+    demo.launch()