Update app.py

app.py

@@ -1,187 +1,43 @@
-import streamlit as st
-# import wikipedia
-from streamlit_lottie import st_lottie 
-import regex as re
-from streamlit_js_eval import streamlit_js_eval
-# from common.utils import *
-from data_collector import *
-# from langchain_community.llms import Ollama
-
-# from langchain_community.llms import Ollama
-import pandas as pd
-import json
-st.set_page_config(layout="wide")
-screen_width = streamlit_js_eval(label="screen.width",js_expressions='screen.width')
-screen_height = streamlit_js_eval(label="screen.height",js_expressions='screen.height')
-
-
-condition_capture = st.session_state
-if 'schema' not in condition_capture:
-    condition_capture['schema'] = {}
-if 'prompt' not in condition_capture:
-    condition_capture['prompt'] = ""
-if  "count" not in condition_capture:
-    condition_capture['count'] = 0
-if "prev_schema" not in condition_capture:
-    condition_capture['prev_schema'] = {}
-if "textual_value" not in condition_capture:
-    condition_capture['textual_value'] = {}
-textual_value=None
-
-
-
-schema=condition_capture['schema']
-
-
-
-
-column1,column2 = st.columns(2)
-with column2:
-
-
-
-    if len(condition_capture['schema'])!=0 and len(condition_capture['textual_value'])==0:
-        # condition_capture['prev_schema'] = condition_capture['schema']
-        condition_capture['textual_value']=relevent_value(str(condition_capture['schema']).lower(),50)
-    if len(condition_capture['schema'])!=0:
-        html_page = condition_capture['textual_value'][1]
-        textual_value = condition_capture['textual_value'][0]
-        st.write("<br>",unsafe_allow_html=True)
-        
-        with st.container(border=True,height=int(screen_height/2.3)):
-            st.header("Wikipedia insights")
-            updated_schema = st.button("Start processing")
-            selector=st.empty()
-            write =st.empty()
-            start_page= selector.select_slider("Select a range of color wavelength",options=[i for i in html_page],key="start_page")
-            write.write(html_page[start_page],unsafe_allow_html=True)
-            
-            
-            
-            
-                
-# )
-            
-            
-                    
-
-
-        
-
-    
-with column1:
-    
-    if str(schema)!=str({}):
-        tabs = st.tabs(["Schema","Data Generation"])
-        with tabs[0]:
-            if str(schema)!=str({}):
-
-                schema_column1,schema_column2 = st.columns(2)
-                with schema_column1:
-                        edited_df = st.data_editor([str(i) for index,i in enumerate(schema)],hide_index=True,use_container_width=True,num_rows='dynamic',height=int(screen_height/3))
-                        
-                with schema_column2:
-                    number = st.number_input("Number of rows",min_value=1,max_value=1000,value=10)
-                    if number!=condition_capture['count'] and updated_schema:
-                        condition_capture['count'] = number
-                    
-                    
-                    with open("/home/user/app/animation/edit_file.json") as animate:
-                        url_json=json.load(animate)
-                    st_lottie(url_json,height = int(screen_height/3))
-                   
-
-        with tabs[1]:
-            with open("/home/user/app/animation/no data animation.json") as animate:
-                url_json=json.load(animate)
-            dataframe=st.empty()
-            
-            if condition_capture['count']==0:
-                st_lottie(url_json,height = int(screen_height/3))
-                
-            else:
-                smart_append=[]
-                if condition_capture['prev_schema'] != condition_capture['schema']:
-                    condition_capture['prev_schema'] = condition_capture['schema']
-                    condition_capture['current_append']={}
-                    
-                    for text_indexing,store in enumerate(actual_value(textual_value,schema)):
-                        dummy_value =dictionary_formatting(store)
-                        for keys in dummy_value:
-                            while len(dummy_value[keys])>=2:
-                                dummy_value[keys].pop(0)
-                        dummy_value = dictionary_formatting(dummy_value)
-
-                        if dummy_value != None:
-                            
-                            
-                            smart_append.append(dummy_value)
-                            print(dummy_value)
-                            for keys in dummy_value:
-                                if keys not in condition_capture['current_append']:
-                                    condition_capture['current_append'][str(keys)]=[]
-                                condition_capture['current_append'][str(keys)].append(str([i for i in dummy_value[keys]]))
-                            dataframe.dataframe(condition_capture['current_append'])
-                            
-
-                            if len(condition_capture['current_append'][[i for i in condition_capture['current_append']][-1]])>=condition_capture['count']:
-                                break
-
-                            # print(dummy_value)
-                            # if smart_check(dummy_value)!=True:
-                            #     smart_value=verification(dummy_value)
-                            # if statement(condition_capture['schema'],smart_value):
-                            #     st.dataframe(smart_value)
-                condition_capture['current_append']={}
-                if len(smart_append)==0:
-                    
-                    ranger=len(condition_capture['current_append'][[i for i in condition_capture['current_append']][0]])
-                    for indexing in range(ranger):
-                        working_dict = {}
-                        for j in condition_capture['current_append']:
-                        
-                            working_dict[j]=condition_capture['current_append'][j][indexing][0]
-                        smart_append.append(working_dict)
-                smart_movement =   sorting(smart_append)
-                
-                for keys in smart_movement:
-                    value=eval(keys)
-                    for keys in value:
-                        if keys not in condition_capture['current_append']:
-                            condition_capture['current_append'][str(keys)]=[]
-                        condition_capture['current_append'][str(keys)].append([str(i) for i in value[keys]])
-                dataframe.dataframe(condition_capture['current_append'])
-                for indexing,j in enumerate(smart_movement):
-                    try:
-                        # Convert string to dictionary
-                        dummy_value = eval(j)
-                        
-                        # Process dictionary values
-                        for key in dummy_value:
-                            while len(dummy_value[key]) >= 2:
-                                dummy_value[key].pop(0)
-                        
-                        # Format dictionary
-                        formatted = dictionary_formatting(dummy_value)
-                        print(formatted)
-                        # Verify and store result
-                        verification_result = verification(formatted) if formatted else None
-                        for j in verification_result:
-                            if j in condition_capture['current_append']:
-                                condition_capture['current_append'][j][indexing]=[str(i) for i in verification_result[j]]
-                        dataframe.dataframe(condition_capture['current_append'])
-                        
-                    except:
-                        pass
-
-
-  
-prompt = st.text_input(label="Please use prompt to generate data",value=condition_capture['prompt'])
-if prompt != str(condition_capture['prompt']):
-    
-    condition_capture['prompt'] = prompt
-    schema = schema_generator(prompt)
-    condition_capture['schema'] = schema
-    condition_capture['current_append']={}
-    
-    st.rerun()
+import gradio as gr
+import torch
+from transformers import pipeline
+
+# Model ID for Llama 3 8B instruct (replace with the exact model you want)
+MODEL_ID = "meta-llama/Llama-3-8b-Instruct-hf"
+
+# Load the text-generation pipeline with device_map="auto" to use GPU if available
+generator = pipeline(
+    "text-generation",
+    model=MODEL_ID,
+    torch_dtype=torch.float16,
+    device_map="auto",
+)
+
+def generate_response(prompt, max_length=512, temperature=0.7):
+    # Format prompt for Llama 3 instruct style
+    formatted_prompt = f"<s>[INST] {prompt} [/INST]"
+    output = generator(
+        formatted_prompt,
+        max_length=max_length,
+        temperature=temperature,
+        do_sample=True,
+        top_p=0.95,
+        num_return_sequences=1,
+    )
+    generated_text = output[0]["generated_text"]
+    # Extract the response after the [/INST] token
+    response = generated_text.split("[/INST]")[-1].strip()
+    return response
+
+with gr.Blocks() as demo:
+    gr.Markdown("# Chat with Llama 3 (8B Instruct)")
+    with gr.Row():
+        with gr.Column():
+            user_input = gr.Textbox(lines=3, placeholder="Type your message here...", label="Your Message")
+            submit_btn = gr.Button("Submit")
+        with gr.Column():
+            output = gr.Textbox(lines=10, label="Llama 3 Response")
+    submit_btn.click(fn=generate_response, inputs=user_input, outputs=output)
+
+if __name__ == "__main__":
+    demo.launch()