Update app.py

app.py

@@ -34,17 +34,10 @@ os.environ['HUGGINGFACEHUB_API_TOKEN'] = os.getenv("HF_TOKEN")
 # Initialize embedding model
 embedding_model = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
 
-#Create pipeline
-pipe = pipeline("text-generation", model="TinyLlama/TinyLlama-1.1B-Chat-v1.0")
-
-#Build LLM
-llm = HuggingFacePipeline(pipeline=pipe)
-# Wrap in pipeline
-#pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, max_length=512)
-#llm = HuggingFacePipeline(pipeline=pipe)
 
 # Store the QA chain globally (across UI events)
 qa_chain = None
+qa_chain1 = None
 
 repo_id="manabb/nrl"
 
@@ -53,17 +46,17 @@ repo_id="manabb/nrl"
 
 # Initialize embedding model
 #embedding_model = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
-
+#=============================================google/flan-t5-small
 # Load HF model (lightweight for CPU)
-#model_name = "google/flan-t5-small"
-#tokenizer = AutoTokenizer.from_pretrained(model_name)
-#model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+model_name = "google/flan-t5-small"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
 
 # Wrap in pipeline
-#pipe = pipeline("text2text-generation", model=model, tokenizer=tokenizer, max_length=512)
-#llm = HuggingFacePipeline(pipeline=pipe)
+pipe1 = pipeline("text2text-generation", model=model, tokenizer=tokenizer, max_length=512)
+llm1 = HuggingFacePipeline(pipeline=pipe1)
 
-#======
+#=============================================TinyLlama/TinyLlama-1.1B-Chat-v1.0
 # Create optimized pipeline for TinyLlama
 pipe = pipeline(
     "text-generation",
@@ -82,7 +75,7 @@ pipe = pipeline(
 
 # Build LangChain LLM wrapper
 llm = HuggingFacePipeline(pipeline=pipe)
-#=====
+#=============================================
 
 def create_faiss_index(repo_id, file, embedding_model="sentence-transformers/all-MiniLM-L6-v2"):
     """Create FAISS index from PDF and upload to HF dataset repo"""
@@ -324,38 +317,61 @@ def generate_qa_chain(repo_id, embedding_model="sentence-transformers/all-MiniLM
 def bePrepare():
     global qa_chain
     qa_chain = generate_qa_chain("manabb/nrl",llm=llm)
+    return "I am ready, ask me questions with model tiny Lama."
+
+def bePrepare1():
+    global qa_chain1
+    qa_chain1 = generate_qa_chain("manabb/nrl",llm=llm1)
+    return "I am ready, ask me questions with model google flan-t5."
 
 def ask_question(query):
     if not qa_chain:
-        return "❌ Please clik the button to get the udated resources first."
+        return "❌ Please clik the button to get the udated resources with tiny Lama."
     response = qa_chain.invoke({"query": query})
     return response["result"]
 
+def ask_question1(query):
+    if not qa_chain1:
+        return "❌ Please clik the button to get the udated resources google flan-t5."
+    response1 = qa_chain1.invoke({"query": query})
+    return response1["result"]
 #====================
 # Gradio UI
 with gr.Blocks() as demo:
     gr.Markdown("## 🧠 For use of NRL procurement department Only")
-
     with gr.Row():
-      Index_processing_output=gr.Textbox(label="📁 Status", interactive=False)
-      Index_processing_btn = gr.Button("🔄 Clik to get the udated resources")
-
-    with gr.Row():
-        query_input = gr.Textbox(label="❓ This is for NRL commercial procurement deptd. Your Question pls")
-        query_btn = gr.Button("🧠 Get Answer")
-
-    answer_output = gr.Textbox(label="✅ Answer", lines=10)
-
-    output_msg = gr.Textbox(label="📁 Authorization Message", interactive=False)
+        # LEFT COLUMN: Document Management
+        with gr.Column(scale=1):
+            gr.Markdown("## 🧠 Using TinyLama Model")
+            with gr.Row():
+                Index_processing_output=gr.Textbox(label="📁 Status for tiny lama", interactive=False)
+            with gr.Row():
+                Index_processing_btn = gr.Button("🔄 Clik to get the udated resources with tiny Lama")
+                Index_processing_btn.click(bePrepare, inputs=None, outputs=Index_processing_output)
+            with gr.Row():
+                query_input = gr.Textbox(label="❓ Your Question pls")
+                query_btn = gr.Button("🧠 Get Answer")
+                answer_output = gr.Textbox(label="✅ Answer", lines=4)
+                query_btn.click(ask_question, inputs=query_input, outputs=answer_output)
+        # RIGHT COLUMN: Document Management
+        with gr.Column(scale=2):
+            gr.Markdown("## 🧠 Using google flan-t5")
+            Index_processing_outpu1t=gr.Textbox(label="📁 Status for google flan-t5", interactive=False)
+            Index_processing_btn1 = gr.Button("🔄 Clik to get the udated resources with google flan-t5")
+            Index_processing_btn1.click(bePrepare1, inputs=None, outputs=Index_processing_output1)
+            query_input1 = gr.Textbox(label="❓ Your Question pls")
+            query_btn1 = gr.Button("🧠 Get Answer")
+            answer_output1 = gr.Textbox(label="✅ Answer", lines=4)
+            query_btn1.click(ask_question1, inputs=query_input1, outputs=answer_output1)    
+    
     with gr.Row():
+        gr.Markdown("## 🧠 Using google flan-t5")
+        output_msg = gr.Textbox(label="📁 Authorization Message", interactive=False)
         file_input = gr.File(label="📄 Upload .pdf File by only authorized user", type="filepath")
         upload_btn = gr.Button("🔄 Process Doc")
-        manab1="Write the password to upload new Circular Doc."
-        authorized_user=gr.Textbox(label=manab1)
+        authorized_user=gr.Textbox(label="Write the password to upload new Circular Doc.")
         upload_btn.click(upload_and_prepare, inputs=[file_input,authorized_user], outputs=output_msg)
 
-    query_btn.click(ask_question, inputs=query_input, outputs=answer_output)
-    Index_processing_btn.click(bePrepare, inputs=None, outputs=Index_processing_output)
 
 
 # For local dev use: demo.launch()