Create app.py

app.py

@@ -0,0 +1,192 @@
+import os, base64
+import requests, json
+import gradio as gr
+
+
+GREEN = '\033[1;32m'
+BLUE = '\033[1;34m'
+RESET = '\033[0m'
+URL = "https://ai1071.4dstaging.com/v1/"
+
+VALID_ANSWER, QUERY_FAIL, INVALID_ANSWER=0 , 1, 2
+
+VICTORIA_HARBOUR, MIC =0, 1 # VICTORIA_HARBOUR=海港飲食集團
+CUSTOMER = MIC
+
+MODES = [
+    {
+        "name": " ",
+        "query_mode_indx": 5,
+        "retrieval_temperature": 0.2, #EC19Jun2024
+        "path": r"E:\workspace\RAG_data\20240412_superQuery\db\EC_test_all\20240603_haigang_qa",
+        "sample_questions": [
+            "這裡可以book位嗎？", "可以book位嗎？", "Hi", "蟹", "魚", "會員", "訂枱"
+            "锡我?", "可唔可以幫我寫一張菜單？",
+            "可以加大長腳蟹嗎？","想查詢最新堂食優惠",
+            "有什麼優惠", "宴會菜單", "有長腳蟹?", "積分如何運作?", "點加入會員？",
+            "套餐可轉其他菜式嗎？", "網購限定優惠可以堂食嗎？", "當日海鮮供應情況?"
+        ],
+     
+    },{
+        "name": "MiC Modular Integrated Construction - HK (Beta)",
+        "query_mode_indx": 4,
+        "retrieval_temperature": 0.2, #EC19Jun2024
+        # "path": r"E:\workspace\RAG_data\20??????????????S",
+        "path": r"E:\workspace\RAG_data\20240412_superQuery\db\EC_test_all\20240619_mic_demo",
+        "sample_questions": [
+            "What is MIC?", "優惠措施", "Please introduce CIC", "Key Technologies of MIC",
+            "組裝合成建築法", "物料或產品規格", "MIC safety."
+        ],
+    }
+]
+
+
+questions=MODES[CUSTOMER]['sample_questions']
+
+def the_answer(response:dict): #extract answer from the response.
+    a=response['msg'].split('Answer(GPT4):')[1].split('References:')[0] #get answers 
+    a.strip() # remove all linefeeds
+    return a
+
+def the_references(response:dict, user_query: str):
+    ref_contents=[]
+    if response["code"]==VALID_ANSWER: #       
+        for ref in response["data"]["source_docs"]:
+            content=ref["page_content"]
+            # ref_question=content['問題']
+            # ref_question_answer=content['回答']
+            ref_contents.append(content)      
+    ref_contents_filtered = filter_repeated(user_query, ref_contents) #EC04Jun2024
+    return ref_contents_filtered #ref_contents
+
+def filter_repeated(user_query, ref_contents: list):
+# This function help to filter out the reference question that is 100% same as the user's ASKED question.
+    #EC04Jun2024
+    ref_contents_filtered = []
+    for ref in ref_contents:
+        # ref_dict = json.loads(ref)
+        try:
+            question = next(iter(ref.get('問題').values()))
+        except StopIteration as e:
+            print(e)
+            pass
+        except Exception as e:
+            print(e)
+            ref_contents_filtered.append(ref)
+            continue
+        print(question)
+        print("question == user_query: "+str(question == user_query))
+        if not question == user_query:
+            ref_contents_filtered.append(ref)
+    return ref_contents_filtered
+
+def get_images_from_source(source_docs):
+    image_exts = [".jpg", ".jpeg", ".png"]
+    source_list = [doc['source'] for doc in source_docs]
+    source_img_list = [source for source in source_list if os.path.splitext(source)[1] in image_exts]
+    
+    buffer_img_str = ""
+    for source in source_img_list:
+        response = requests.get(URL+f"images?image_id={source}")
+        if response.status_code == 200:
+            image_data = response.content
+            base64_image = base64.b64encode(image_data).decode("utf-8") #image_data.encode("base64").decode("utf-8")
+        
+            # Create an HTML <img> tag
+            # img_name = os.path.basename(source)
+            img_html = f'<img src="data:image/png;base64,{base64_image}" alt="img_name">'
+            buffer_img_str += "\n"+img_html
+            
+            # Print or use img_html as needed
+            # print(img_html)
+        else:
+            print("Error fetching image")
+    return buffer_img_str
+
+def all_info(response):
+    info="\n".join([f"{GREEN}{key}{RESET}: {value}" for key, value in response.items()])
+    return info
+
+def request_stream_chat(question:str, history):
+    global temp_source_docs
+    
+    if not question:
+        yield "Hello! What would you like to know?"
+        return
+    
+    payload = {
+        "prompt": question,
+        "retrieval_temperature": 0.2, #MODES[CUSTOMER]['retrieval_temperature'], #EC19Jun2024: from 0.2 -> MODES[CUSTOMER]['retrieval_temperature']
+        
+        # "query_mode_indx": 5,
+        # "path": r"E:\workspace\RAG_data\20240412_superQuery\db\EC_test_all\20240603_haigang_qa",
+        
+        "query_mode_indx": MODES[CUSTOMER]['query_mode_indx'],
+        "path": MODES[CUSTOMER]['path'],
+        
+        "stream": True,
+        "LLM_type": "gpt"
+    }
+    reply_buffer = ""
+    with requests.post(url=URL+"query", json=payload, stream=True) as r_stream:
+        for line in r_stream.iter_lines():
+            if line:               
+                line = json.loads(line)
+                if line['finished']: #all the steamed content
+                    response = line
+                    # print(f"{RESET}-end")
+                    # response=filter_repeated(response)
+                    
+                    msg = response['msg']
+                    
+                    if payload['query_mode_indx'] == 5:
+                        source_docs_content = the_references(response, question)
+                        source_docs_content_str = "\n".join([str(content) for content in source_docs_content])
+                        response_str = msg+"\n\nSource documents:\n"+source_docs_content_str
+                    else:
+                        response_str = msg+"\n\n"+response.get('reference') #EC19Jun2024: from [] -> .get()
+                    
+                    source_docs = response['data']['source_docs']
+                    image_str = get_images_from_source(source_docs)
+                    response_str += "\n"+image_str
+                    yield response_str
+                    
+                    break
+                else:
+                    # yield line
+                    # print(f"{BLUE}"+line['reply']+f"{RESET}", end="") #steaming chuncks.
+                    reply_buffer += line['reply']
+                    yield reply_buffer #line['reply']
+                    
+    # response=the_answer(response)+'\n' + str(the_references(response))
+    # return response
+
+def my_generator(x):
+    for i in range(x):
+        yield i
+
+if __name__ == "__main__":
+    # responses=[]; answers=[]; references=[]; all_infos=[]
+    # for q in questions:
+    #     response=request_stream_chat(q, "dummy history")
+        
+    #     responses.append(response)  
+    #     all_infos.append(all_info(response))
+    #     answers.append(the_answer(response))
+    #     references.append(the_references(response))
+    
+    gr.ChatInterface(
+        request_stream_chat, #a4o_response
+        examples=questions,
+
+        chatbot=gr.Chatbot(height=450), #300),
+        textbox=gr.Textbox(placeholder="喺呢度問我問題.", container=False, scale=7),
+        title=MODES[CUSTOMER]['name'],
+        description="智能査詢",
+        theme="soft",
+        cache_examples=False, #True,
+        retry_btn=None,
+        undo_btn="Delete Previous",
+        clear_btn="Clear",
+        fill_height=True,
+    ).launch(share=True) #False) #True)