Update app.py

app.py

@@ -1,60 +1,58 @@
-import faiss
-from sentence_transformers import SentenceTransformer
-from langchain_core.prompts import ChatPromptTemplate
-from langchain_groq import ChatGroq
-import os
-import gradio as gr
-import numpy as np
-import pickle
-
-model = SentenceTransformer("all-MiniLM-L6-v2")
-index = faiss.read_index("solar_vectors.index")
-with open("chunks.pkl", "rb") as f:
-    chunks= pickle.load(f)
-
-os.environ["GROQ_API_KEY"]= "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"
-llm = ChatGroq(model="mixtral-8x7b-32768",temperature=0)
-
-def retrieve_relevant_text(query, top_k=1):
-    query_embedding = model.encode([query])
-    distances, indices = index.search(np.array(query_embedding), top_k)
-    return [chunks[i] for i in indices[0]]
-
-def generate_response(user_query):
-    retrieved_text = retrieve_relevant_text(user_query, top_k=4)
-    system_message = "You are an intelligent assistant that provides accurate, helpful information about solar energy based on the information provided(if not, answer according to your knowledge)."
-    prompt_template = ChatPromptTemplate.from_messages([
-        ("system", system_message),
-        ("human", f"Use the following information to answer: {retrieved_text} \n\nUser Query: {user_query}")
-    ])
-
-    chain = prompt_template | llm
-    response = chain.invoke({"text": user_query})
-    return response.content
-
-def gradio_chatbot(user_input):
-    response = generate_response(user_input)
-    return response
-
-
-
-with gr.Blocks() as demo:
-    gr.Markdown("# 🌞 SolarAI 🌞")
-
-    with gr.Row():
-        user_input = gr.Textbox(
-            placeholder="Ask me anything about solar energy...",
-            lines=2,
-            interactive=True
-        )
-
-    with gr.Row():
-        output_box = gr.Textbox(
-            lines=12,
-            interactive=True,
-            label="Chatbot Response"
-        )
-
-    submit_btn = gr.Button("Ask")
-    submit_btn.click(fn=gradio_chatbot, inputs=user_input, outputs=output_box)
+import faiss
+from sentence_transformers import SentenceTransformer
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_groq import ChatGroq
+import gradio as gr
+import numpy as np
+import pickle
+
+model = SentenceTransformer("all-MiniLM-L6-v2")
+index = faiss.read_index("solar_vectors.index")
+with open("chunks.pkl", "rb") as f:
+    chunks= pickle.load(f)
+
+llm = ChatGroq(model="mixtral-8x7b-32768",temperature=0.2)
+
+def retrieve_relevant_text(query, top_k=1):
+    query_embedding = model.encode([query])
+    distances, indices = index.search(np.array(query_embedding), top_k)
+    return [chunks[i] for i in indices[0]]
+
+def generate_response(user_query):
+    retrieved_text = retrieve_relevant_text(user_query, top_k=4)
+    system_message = "You are an intelligent assistant that provides accurate, helpful information about solar energy based on the information provided(if not, answer according to your knowledge)."
+    prompt_template = ChatPromptTemplate.from_messages([
+        ("system", system_message),
+        ("human", f"Use the following information to answer: {retrieved_text} \n\nUser Query: {user_query}")
+    ])
+
+    chain = prompt_template | llm
+    response = chain.invoke({"text": user_query})
+    return response.content
+
+def gradio_chatbot(user_input):
+    response = generate_response(user_input)
+    return response
+
+
+
+with gr.Blocks() as demo:
+    gr.Markdown("# 🌞 SolarAI 🌞")
+
+    with gr.Row():
+        user_input = gr.Textbox(
+            placeholder="Ask me anything about solar energy...",
+            lines=2,
+            interactive=True
+        )
+
+    with gr.Row():
+        output_box = gr.Textbox(
+            lines=12,
+            interactive=True,
+            label="Chatbot Response"
+        )
+
+    submit_btn = gr.Button("Ask")
+    submit_btn.click(fn=gradio_chatbot, inputs=user_input, outputs=output_box)
 demo.launch()