Update app.py

app.py

@@ -9,17 +9,18 @@ from firecrawl import FirecrawlApp
 import gradio as gr
 import os
 
-# Initialize LLM and Tools
-
+# Using Ollama (only in Local)
+# -----------------------------
 # local_llm = 'llama3.1'
 # llama3 = ChatOllama(model=local_llm, temperature=1)
 # llama3_json = ChatOllama(model=local_llm, format='json', temperature=0)
 
-#
+# Hugging Face Initialization
 os.environ["HUGGINGFACEHUB_API_TOKEN"] = os.getenv('HF_KEY')
 os.environ["GOOGLE_CSE_ID"] = os.getenv('GOOGLE_CSE_ID')
 os.environ["GOOGLE_API_KEY"] = os.getenv('GOOGLE_API_KEY')
 
+# Llama Endpoint
 llm = HuggingFaceEndpoint(
     repo_id="meta-llama/Meta-Llama-3.1-8B-Instruct",
     task="text-generation",
@@ -27,13 +28,15 @@ llm = HuggingFaceEndpoint(
     do_sample=False,
     repetition_penalty=1.03,
     )
+
 llama3 = ChatHuggingFace(llm=llm, temperature = 1)
 llama3_json = ChatHuggingFace(llm=llm, format = 'json', temperature = 0)
 
+# Search & Scrape Initialization
 google_search = GoogleSearchAPIWrapper()
 firecrawl_app = FirecrawlApp(api_key=os.getenv('FIRECRAWL_KEY'))
 
-# Query Transformation
+# Keyword Generation
 query_prompt = PromptTemplate(
     template="""
     
@@ -55,8 +58,7 @@ query_prompt = PromptTemplate(
     """,
     input_variables=["question"],
 )
-
-# Chain
+# Keyword Chain
 query_chain = query_prompt | llama3_json | JsonOutputParser()
 
 # Google Search and Firecrawl Setup
@@ -69,7 +71,7 @@ def search_and_scrape(keyword):
         scraped_data.append(scrape_response)
     return scraped_data
 
-# Summarizer
+# Summarizer 
 summarize_prompt = PromptTemplate(
     template="""
     
@@ -89,11 +91,10 @@ summarize_prompt = PromptTemplate(
     """,
     input_variables=["question"],
 )
-
-# Chain
+# Summarizer Chain
 summarize_chain = summarize_prompt | llama3 | StrOutputParser()
 
-# Generation prompt
+# Fact Checker
 generate_prompt = PromptTemplate(
     template="""
     
@@ -123,31 +124,31 @@ generate_prompt = PromptTemplate(
     """,
     input_variables=["question", "context"],
 )
-
-# Chain
+# Fact Checker Chain
 generate_chain = generate_prompt | llama3_json | JsonOutputParser()
 
 # Full Flow Function
 def fact_check_flow(user_question):
-    # Step 2: Transform question into search query keyword
+    # Generate Keyword
     keyword = query_chain.invoke({"question": user_question})["query"]
     
-    # Step 3 & 4: Google search and scrape results
+    # Search & Scrape Results
     context_data = search_and_scrape(keyword)
-    
+
+    # Join Results
     final_markdown = []
     for results in context_data:
         final_markdown.append(results['markdown'])
 
     final_markdown = ' '.join(final_markdown)
 
+    # Summarize Results
     context = summarize_chain.invoke({"question": final_markdown})
 
-    # Step 5: Use scraped data as context and run generate chain
-
+    # Use Information + Context to Fact Check
     final_response = generate_chain.invoke({"question": user_question, "context": context})
 
-    # Process output
+    # Process Output
     verdict = final_response['system_verdict']
     explanation = final_response['explanation']
     
@@ -160,11 +161,6 @@ def fact_check_flow(user_question):
     
     return verdict_html + explanation_html
 
-# Example Use
-# user_question = "biden is not joining election in 2024"
-# result = fact_check_flow(user_question)
-# print(result)
-
 demo = gr.Interface(
     fn=fact_check_flow,
     inputs=gr.Textbox(label="Input any information you want to fact-check!"),