story loading support

README.md

@@ -1,6 +1,6 @@
 ---
 title: Kommuneity Story Creator
-emoji: 📖
+emoji: 🪄
 colorFrom: gray
 colorTo: green
 sdk: streamlit

app.py

@@ -5,86 +5,115 @@ import pytz
 import time
 import json
 import re
-from typing import List
+from typing import List, Literal, TypedDict, Tuple
 from transformers import AutoTokenizer
 from gradio_client import Client
+from data import storiesDb
+
+from openai import OpenAI
+import anthropic
+from groq import Groq
 
 from dotenv import load_dotenv
 load_dotenv()
 
-useGpt4 = os.environ.get("USE_GPT_4") == "1"
-
-if useGpt4:
-    from openai import OpenAI
-    client = OpenAI(api_key=os.environ.get("OPENAI_API_KEY"))
-    MODEL = "gpt-4o-mini"
-    MAX_CONTEXT = 128000
-    tokenizer = AutoTokenizer.from_pretrained("Xenova/gpt-4o")
-else:
-    from groq import Groq
-    client = Groq(
-        api_key=os.environ.get("GROQ_API_KEY"),
-    )
-    MODEL = "llama-3.1-70b-versatile"
-    MAX_CONTEXT = 8000
-    tokenizer = AutoTokenizer.from_pretrained("Xenova/Meta-Llama-3.1-Tokenizer")
+ModelType = Literal["GPT4", "CLAUDE", "LLAMA"]
+ModelConfig = TypedDict("ModelConfig", {
+    "client": OpenAI | Groq | anthropic.Anthropic,
+    "model": str,
+    "max_context": int,
+    "tokenizer": AutoTokenizer
+})
 
+modelType: ModelType = os.environ.get("MODEL_TYPE") or "LLAMA"
+modelType: ModelType = "CLAUDE"
 
 JSON_SEPARATOR = ">>>>"
+EXCEPTION_KEYWORD = "<<EXCEPTION>>"
 
+SYSTEM_MSG = f"""
+=> Context:
+You're an storytelling assistant who guides users through four phases of narrative development, helping them craft compelling personal or professional stories.
+The story created should be in simple language, yet evoke great emotions.
 
-def countTokens(text):
-    text = str(text)
-    tokens = tokenizer.encode(text, add_special_tokens=False)
-    return len(tokens)
+-----
+=> Key Points:
+Ask one question at a time, give the options in a numbered and well formatted manner in different lines.
+Summarise options chosen so far in each step.
+Every response should have a question unless it's the end of flow.
 
+-----
+=> Format & Syntax:
+Whenever any of the below rules are satisfied, then append your FINAL response with this exact keyword "{JSON_SEPARATOR}", and only AFTER this, append with a JSON described in the matching rule below.
+Apply at most one rule at a time, the most relevant one.
+Do not write anything after the JSON
 
-SYSTEM_MSG = f"""
-You're an storytelling assistant who guides users through four phases of narrative development, helping them craft compelling personal or professional stories. The story created should be in simple language, yet evoke great emotions.
-Ask one question at a time, give the options in a numbered and well formatted manner in different lines
-If your response has number of options to choose from, only then append your final response with this exact keyword "{JSON_SEPARATOR}", and only after this, append with the JSON of options to choose from. The JSON should be of the format:
+- Rule 1: If your response has multiple numbered options to choose from, append JSON in this format (alway check for this rule):
+```
 {{
-    "options": [
-        {{ "id": "1", "label": "Option 1"}},
-        {{ "id": "2", "label": "Option 2"}}
-    ]
+    "options": [{{ "id": "1", "label": "Option 1"}}, {{ "id": "2", "label": "Option 2"}}]
 }}
+```
 Do not write "Choose one of the options below:"
 Keep options to less than 9.
-Summarise options chosen so far in each step.
 
-# Tier 1: Story Creation
+- Rule 2: If the USER has chosen to adapt a well known story, append this JSON:
+```
+{{
+    "action": "SHOW_STORY_DATABASE"
+}}
+```
+------
+=> Task Definition:
+You take the user through a flow of questions as defined below. You'll navigate the user through three tiers, moving closer to the final story.
+Before giving any response, make sure to evaluate the "Format" rules described above.
+
+## Tier 1: Story Creation
 You initiate the storytelling process through a series of engaging prompts:
-Story Origin:
-Asks users to choose between personal anecdotes or adapting a well-known story (creating a story database here of well-known stories to choose from).
 
-Story Use Case:
-Asks users to define the purpose of building a story (e.g., profile story, for social media content).
+#### Story Origin:
+- Asks users to choose between personal anecdotes or adapting a well-known real story
+- If they choose to choose to adapt from a well known story, show them a database of the stories to choose from
 
-Story Time Frame:
+#### Story Use Case:
+Asks users to define the purpose of building a story. It can be one of the following (provide very short description for each):
+- Personal Branding: To create a narrative that highlights an individual's unique experiences, skills, and values for use in professional networking, job applications, or personal websites.
+- Company Origin: To craft a compelling narrative about how a company or organization was founded, its mission, and key milestones for use in marketing materials or investor presentations.
+- Product Launch: To develop an engaging narrative around a new product or service, focusing on the problem it solves and its unique value proposition for use in marketing campaigns or sales pitches.
+- Customer Success / Testimonials: To showcase how a product or service has positively impacted a customer's life or business, creating a relatable narrative for potential customers.
+- Team Building: To create a shared narrative that reinforces company values, promotes team cohesion, or introduces new team members, for use in internal communications or team-building exercises.
+
+#### Story Time Frame:
 Allows story selection from various life stages (childhood, mid-career, recent experiences).
 Or Age-wise (below 8, 8-13, 13-15 and so on).
 
-Story Focus:
-Prompts users to select behaviours or leadership qualities to highlight in the story.
-Provides a list of options based on common leadership traits:
-(Generosity / Integrity / Loyalty / Devotion / Kindness / Sincerity / Self-control / Confidence / Persuasiveness / Ambition / Resourcefulness / Decisiveness / Faithfulness / Patience / Determination / Persistence / Fairness / Cooperation / Optimism / Proactive / Charisma / Ethics / Relentlessness / Authority / Enthusiasm / Boldness)
-
-Story Type:
+#### Story Focus:
+Prompts users to select behaviours or leadership qualities to highlight in the story. Allow users to choose upto 3-5 qualities.
+- Resourcefulness (ability to find creative solutions)
+- Sincerity (genuine and honest in intentions and words)
+- Decisiveness (ability to make firm and timely decisions)
+- Kindness (concern and compassion for others' well-being)
+- Ambition (drive to achieve goals and succeed)
+- Patience (ability to endure difficult situations calmly)
+- Boldness (willingness to take risks and speak up)
+- Fairness (commitment to justice and equal treatment)
+- Proactive (taking initiative and anticipating challenges)
+
+#### Story Type:
 Prompts users to select the kind of story they want to tell:
-Where we came from: A founding Story
-Why we can't stay here: A case-for-change story
-Where we're going: A vision story
-How we're going to get there: A strategy story
-Why I lead the way I do: Leadership philosophy story
-Why you should want to work here: A rallying story
-Personal stories: Who you are, what you do, how you do it, and who you do it for
-What we believe: A story about values
-Who we serve: A customer story
-What we do for our customers: A sales story
-How we're different: A marketing story
-
-Guided Storytelling Framework:
+- Where we came from: A founding Story
+- Why we can't stay here: A case-for-change story
+- Where we're going: A vision story
+- How we're going to get there: A strategy story
+- Why I lead the way I do: Leadership philosophy story
+- Why you should want to work here: A rallying story
+- Personal stories: Who you are, what you do, how you do it, and who you do it for
+- What we believe: A story about values
+- Who we serve: A customer story
+- What we do for our customers: A sales story
+- How we're different: A marketing story
+
+#### Guided Storytelling Framework:
 You then lead users through a structured narrative development via the following prompts:
 - Describe the day it happened
 - What was the Call to Action / Invitation
@@ -94,14 +123,16 @@ You then lead users through a structured narrative development via the following
 - Detailing the resolution / Reaching the final goal
 - Reflecting on personal growth or lessons learned (What did you do that changed your life forever?)
 
-Now, show the story created so far, and ask for confirmation before proceeding to the next tier.
+Now, show the story created so far using Story-Spine structure as the default style, and then ask for confirmation before proceeding to the next tier.
+If the user has any suggestions, incorporate them and then show the story again.
+
 
-# Tier 2: Story Enhancement
-After initial story creation, you offer congratulations on completing the first draft and gives 2 options:
+## Tier 2: Story Enhancement
+#### After initial story creation, you offer congratulations on completing the first draft and gives 2 options:
 Option 1 - Provides option for one-on-one sessions with expert storytelling coaches - the booking can be done that at https://calendly.com/
 Options 2 - Provides further options for introducing users to more sophisticated narratives.
 
-If Option 2 chosen, show these options with simple explanation and chose one.
+#### If Option 2 chosen, show these options with simple explanation and chose one.
 You take the story and integrates it into different options of storytelling narrative structure:
 The Story Hanger
 The Story Spine
@@ -114,26 +145,67 @@ The Cliffhanger
 After taking user's preference, you show the final story and ask for confirmation before moving to the next tier.
 Allow them to iterate over different narratives to see what fits best for them.
 
-# Tier 3: Story Polishing
+
+## Tier 3: Story Polishing
 The final phase focuses on refining the narrative further:
-You add suggestions to the story:
-Impactful quotes/poems / similes/comparisons
-Creative enhancements:
-Some lines or descriptions for inspiration
-Tips for maximising emotional resonance and memorability
+- You add suggestions to the story:
+- Impactful quotes/poems / similes/comparisons
+
+#### Creative enhancements:
+- Some lines or descriptions for inspiration
+- Tips for maximising emotional resonance and memorability
+
 By guiding users through these three tiers, you aim to cater to novice storytellers, offering a comprehensive platform for narrative skill development through its adaptive approach.
-You end it with the final story and seeking any suggestions from the user to refine the story further.
+You end it with the final story and seek any suggestions from the user to refine the story further.
+
 Once the user confirms, you congratulate them with emojis on completing the story and provide the final story in a beatifully formatted manner.
 Note that the final story should include twist, turns and events that make it really engaging and enjoyable to read.
 
 """
 
-USER_ICON = "man.png"
-AI_ICON = "Kommuneity.png"
-IMAGE_LOADER = "ripple.svg"
-TEXT_LOADER = "balls.svg"
+USER_ICON = "icons/man.png"
+AI_ICON = "icons/Kommuneity.png"
+IMAGE_LOADER = "icons/Wedges.svg"
+TEXT_LOADER = "icons/balls.svg"
+DB_LOADER = "icons/db_loader.svg"
 START_MSG = "I want to create a story 😊"
 
+
+MODEL_CONFIG: dict[ModelType, ModelConfig] = {
+    "GPT4": {
+        "client": OpenAI(api_key=os.environ.get("OPENAI_API_KEY")),
+        "model": "gpt-4o-mini",
+        "max_context": 128000,
+        "tokenizer": AutoTokenizer.from_pretrained("Xenova/gpt-4o")
+    },
+    "CLAUDE": {
+        "client": anthropic.Anthropic(api_key=os.environ.get("ANTHROPIC_API_KEY")),
+        "model": "claude-3-sonnet-20240229",
+        "max_context": 128000,
+        "tokenizer": AutoTokenizer.from_pretrained("Xenova/claude-tokenizer")
+    },
+    "LLAMA": {
+        "client": Groq(api_key=os.environ.get("GROQ_API_KEY")),
+        "model": "llama-3.1-70b-versatile",
+        "max_context": 128000,
+        "tokenizer": AutoTokenizer.from_pretrained("Xenova/Meta-Llama-3.1-Tokenizer")
+    }
+}
+
+client = MODEL_CONFIG[modelType]["client"]
+MODEL = MODEL_CONFIG[modelType]["model"]
+MAX_CONTEXT = MODEL_CONFIG[modelType]["max_context"]
+tokenizer = MODEL_CONFIG[modelType]["tokenizer"]
+
+isClaudeModel = modelType == "CLAUDE"
+
+
+def __countTokens(text):
+    text = str(text)
+    tokens = tokenizer.encode(text, add_special_tokens=False)
+    return len(tokens)
+
+
 st.set_page_config(
     page_title="Kommuneity Story Creator",
     page_icon=AI_ICON,
@@ -152,6 +224,7 @@ def pprint(log: str):
     print(f"[{now}] [{ipAddress}] {log}")
 
 
+pprint("\n")
 pprint("\n")
 
 st.markdown(
@@ -180,6 +253,11 @@ st.markdown(
         font-family: 'Courier New', Courier, monospace; /* Monospace font */
     }
 
+    div[aria-label="dialog"] {
+        width: 80vw;
+        height: 600px;
+    }
+
     </style>
     """,
     unsafe_allow_html=True
@@ -199,9 +277,19 @@ def __isInvalidResponse(response: str):
     if len(re.findall(r'\n\n', response)) > 15:
         return True
 
+    # LLM API threw exception
+    if EXCEPTION_KEYWORD in response:
+        return True
+
     # json response without json separator
     if ('{\n    "options"' in response) and (JSON_SEPARATOR not in response):
         return True
+    if ('{\n    "action"' in response) and (JSON_SEPARATOR not in response):
+        return True
+
+    # only options with no text
+    if response.startswith(JSON_SEPARATOR):
+        return True
 
 
 def __matchingKeywordsCount(keywords: List[str], text: str):
@@ -219,7 +307,7 @@ def __isStringNumber(s: str) -> bool:
         return False
 
 
-def __getImagePromptDetails(prompt: str, response: str):
+def __getRawImagePromptDetails(prompt: str, response: str) -> Tuple[str, str, str]:
     regex = r'[^a-z0-9 \n\.\-]|((the) +)'
 
     cleanedResponse = re.sub(regex, '', response.lower())
@@ -230,21 +318,21 @@ def __getImagePromptDetails(prompt: str, response: str):
 
     if (
         __matchingKeywordsCount(
-            ["adapt", "profile", "social media", "purpose", "use case"],
+            ["adapt", "personal branding", "purpose", "use case"],
             cleanedResponse
         ) > 2
-        and not __isStringNumber(prompt)
-        and cleanedPrompt in cleanedResponse
         and "story so far" not in cleanedResponse
     ):
         return (
-            f'''
-                Subject: {prompt}.
-                Style: Fantastical, in a storybook, surreal, bokeh
-            ''',
-            "Painting your character ..."
+            f"Extract the name of selected story from this text and add few more details about this story:\n{response}",
+            "Effect: bokeh",
+            "Painting your character ...",
         )
 
+        '''
+        Style: Fantastical, in a storybook, surreal, bokeh
+        '''
+
         '''
         Mood: ethereal lighting that emphasizes the fantastical nature of the scene.
 
@@ -268,37 +356,67 @@ def __getImagePromptDetails(prompt: str, response: str):
         relevantResponse = response[:storyEndIdx]
         pprint(f"{relevantResponse=}")
         return (
-            f"photo of a scene from this text: {relevantResponse}",
-            "Imagining your scene (beta) ..."
+            "Extract the story plot from this text:\n{response}",
+            """
+            Style: In a storybook, surreal
+            """,
+            "Imagining your scene (beta) ...",
         )
+        """
+        photo of a scene from this text: {relevantResponse}.
+        """
 
-    return (None, None)
-
-
-def __resetButtonState():
-    st.session_state["buttonValue"] = ""
+    return (None, None, None)
 
 
-def __setStartMsg(msg):
-    st.session_state.startMsg = msg
-
-
-if "chatHistory" not in st.session_state:
-    st.session_state.chatHistory = []
+def __getImagePromptDetails(prompt: str, response: str):
+    (enhancePrompt, imagePrompt, loaderText) = __getRawImagePromptDetails(prompt, response)
+
+    if imagePrompt or enhancePrompt:
+        pprint(f"[Raw] {enhancePrompt=} | {imagePrompt=}")
+
+        promptEnhanceModelType: ModelType = "LLAMA"
+        pprint(f"{promptEnhanceModelType=}")
+
+        modelConfig = MODEL_CONFIG[promptEnhanceModelType]
+        client = modelConfig["client"]
+        model = modelConfig["model"]
+        isClaudeModel = promptEnhanceModelType == "CLAUDE"
+
+        systemPrompt = "You help in creating prompts for image generation"
+        promptPrefix = f"{enhancePrompt}\nAnd then use the above to" if enhancePrompt else "Use the text below to"
+
+        llmArgs = {
+            "model": model,
+            "messages": [{
+                "role": "user",
+                "content": f"{promptPrefix} create a prompt for image generation (limit to less than 500 words)\n\n{imagePrompt}"
+            }],
+            "temperature": 0.8,
+            "max_tokens": 2000
+        }
 
-if "messages" not in st.session_state:
-    st.session_state.messages = []
+        if isClaudeModel:
+            llmArgs["system"] = systemPrompt
+            response = client.messages.create(**llmArgs)
+            imagePrompt = response.content[0].text
+        else:
+            llmArgs["messages"] = [
+                {"role": "system", "content": systemPrompt},
+                *llmArgs["messages"]
+            ]
+            response = client.chat.completions.create(**llmArgs)
+            responseMessage = response.choices[0].message
+            imagePrompt = responseMessage.content
 
-if "buttonValue" not in st.session_state:
-    __resetButtonState()
+        pprint(f"[Enhanced] {imagePrompt=}")
 
-if "startMsg" not in st.session_state:
-    st.session_state.startMsg = ""
+    return (imagePrompt, loaderText)
 
 
 def __getMessages():
     def getContextSize():
-        currContextSize = countTokens(SYSTEM_MSG) + countTokens(st.session_state.messages) + 100
+        currContextSize = __countTokens(SYSTEM_MSG) + __countTokens(st.session_state.messages) + 100
         pprint(f"{currContextSize=}")
         return currContextSize
 
@@ -309,30 +427,57 @@ def __getMessages():
     return st.session_state.messages
 
 
-def predict():
-    messagesFormatted = [{"role": "system", "content": SYSTEM_MSG}]
-    messagesFormatted.extend(__getMessages())
-    contextSize = countTokens(messagesFormatted)
+def __logLlmRequest(messagesFormatted: list):
+    contextSize = __countTokens(messagesFormatted)
     pprint(f"{contextSize=} | {MODEL}")
+    # pprint(f"{messagesFormatted=}")
 
-    response = client.chat.completions.create(
-        model=MODEL,
-        messages=messagesFormatted,
-        temperature=0.8,
-        max_tokens=4000,
-        stream=True
-    )
-
-    chunkCount = 0
-    for chunk in response:
-        chunkContent = chunk.choices[0].delta.content
-        if chunkContent:
-            chunkCount += 1
-            yield chunkContent
 
+def predict():
+    messagesFormatted = []
 
-def generateImage(prompt: str):
-    pprint(f"imagePrompt={prompt}")
+    try:
+        if isClaudeModel:
+            messagesFormatted.extend(__getMessages())
+            __logLlmRequest(messagesFormatted)
+
+            with client.messages.stream(
+                model=MODEL,
+                messages=messagesFormatted,
+                system=SYSTEM_MSG,
+                max_tokens=4000,
+            ) as stream:
+                for text in stream.text_stream:
+                    yield text
+        else:
+            messagesFormatted.append(
+                {"role": "system", "content": SYSTEM_MSG}
+            )
+            messagesFormatted.extend(__getMessages())
+            __logLlmRequest(messagesFormatted)
+
+            response = client.chat.completions.create(
+                model=MODEL,
+                messages=messagesFormatted,
+                temperature=0.8,
+                max_tokens=4000,
+                stream=True
+            )
+
+            for chunk in response:
+                choices = chunk.choices
+                if not choices:
+                    pprint("Empty chunk")
+                    continue
+                chunkContent = chunk.choices[0].delta.content
+                if chunkContent:
+                    yield chunkContent
+    except Exception as e:
+        pprint(f"LLM API Error: {e}")
+        yield EXCEPTION_KEYWORD
+
+
+def __generateImage(prompt: str):
     fluxClient = Client("black-forest-labs/FLUX.1-schnell")
     result = fluxClient.predict(
             prompt=prompt,
@@ -347,10 +492,86 @@ def generateImage(prompt: str):
     return result
 
 
-st.title("Kommuneity Story Creator 📖")
-if not (st.session_state["buttonValue"] or st.session_state["startMsg"]):
+st.title("Kommuneity Story Creator 🪄")
+
+
+def __resetButtonState():
+    st.session_state.buttonValue = ""
+
+
+def __resetSelectedStory():
+    st.session_state.selectedStory = ""
+
+
+def __setStartMsg(msg):
+    st.session_state.startMsg = msg
+
+
+if "chatHistory" not in st.session_state:
+    st.session_state.chatHistory = []
+
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+
+if "buttonValue" not in st.session_state:
+    __resetButtonState()
+
+if "selectedStory" not in st.session_state:
+    st.session_state.selectedStory = ""
+
+if "startMsg" not in st.session_state:
+    st.session_state.startMsg = ""
     st.button(START_MSG, on_click=lambda: __setStartMsg(START_MSG))
 
+if "showStoryDbDialog" not in st.session_state:
+    st.session_state.showStoryDbDialog = False
+
+
+def __disableStoryDbDialog():
+    if st.session_state.showStoryDbDialog:
+        st.session_state.showStoryDbDialog = False
+        st.rerun()
+
+
+def __enableStoryDbDialog():
+    if not st.session_state.showStoryDbDialog:
+        st.session_state.showStoryDbDialog = True
+        st.rerun()
+
+
+if st.session_state.showStoryDbDialog:
+    @st.dialog("Choose a popular story", width="large")
+    def __openStoryDbDialog():
+        storyPlaceholder = st.empty()
+        col1, col2, col3 = storyPlaceholder.columns([1, 1, 1])
+        col2.image(DB_LOADER)
+        col2.write(
+            """
+            <div class='blinking code'>
+            Loading from database ...
+            </div>
+            """,
+            unsafe_allow_html=True
+        )
+
+        stories = storiesDb.getAllStories()
+        with storyPlaceholder.container(border=False, height=500):
+            for idx, story in enumerate(stories):
+                storyTitle = story['Story Title']
+                storyDetails = story['Story Text']
+                with st.expander(storyTitle):
+                    st.markdown(storyDetails)
+                    if st.button(
+                        "Select",
+                        key=f"select_{idx}",
+                        type="primary",
+                        use_container_width=True
+                    ):
+                        st.session_state.selectedStory = storyTitle
+                        __disableStoryDbDialog()
+
+    __openStoryDbDialog()
+
 for chat in st.session_state.chatHistory:
     role = chat["role"]
     content = chat["content"]
@@ -361,29 +582,36 @@ for chat in st.session_state.chatHistory:
         if imagePath:
             st.image(imagePath)
 
-if prompt := (st.chat_input() or st.session_state["buttonValue"] or st.session_state["startMsg"]):
+
+if prompt := (
+    st.chat_input()
+    or st.session_state["buttonValue"]
+    or st.session_state["selectedStory"]
+    or st.session_state["startMsg"]
+):
     __resetButtonState()
+    __resetSelectedStory()
     __setStartMsg("")
+    __disableStoryDbDialog()
 
     with st.chat_message("user", avatar=USER_ICON):
         st.markdown(prompt)
     pprint(f"{prompt=}")
-    st.session_state.messages.append({"role": "user", "content": prompt})
     st.session_state.chatHistory.append({"role": "user", "content": prompt })
+    st.session_state.messages.append({"role": "user", "content": prompt})
 
     with st.chat_message("assistant", avatar=AI_ICON):
         responseContainer = st.empty()
 
         def __printAndGetResponse():
             response = ""
-            # responseContainer.markdown(".....")
             responseContainer.image(TEXT_LOADER)
             responseGenerator = predict()
 
             for chunk in responseGenerator:
                 response += chunk
                 if __isInvalidResponse(response):
-                    pprint(f"{response=}")
+                    pprint(f"InvalidResponse={response}")
                     return
 
                 if JSON_SEPARATOR not in response:
@@ -425,34 +653,40 @@ if prompt := (st.chat_input() or st.session_state["buttonValue"] or st.session_s
                 )
                 # imgContainer.markdown(f"`{loaderText}`")
                 imgContainer.image(IMAGE_LOADER)
-                (imagePath, seed) = generateImage(imagePrompt)
+                (imagePath, seed) = __generateImage(imagePrompt)
                 imageContainer.image(imagePath)
         except Exception as e:
             pprint(e)
             imageContainer.empty()
 
+        st.session_state.chatHistory.append({
+            "role": "assistant",
+            "content": response,
+            "image": imagePath,
+        })
+        st.session_state.messages.append({
+            "role": "assistant",
+            "content": response,
+        })
+
         if jsonStr:
             try:
                 json.loads(jsonStr)
                 jsonObj = json.loads(jsonStr)
-                options = jsonObj["options"]
-
-                for option in options:
-                    st.button(
-                        option["label"],
-                        key=option["id"],
-                        on_click=lambda label=option["label"]: selectButton(label)
-                    )
-                # st.code(jsonStr, language="json")
+                options = jsonObj.get("options")
+                action = jsonObj.get("action")
+
+                if options:
+                    for option in options:
+                        st.button(
+                            option["label"],
+                            key=option["id"],
+                            on_click=lambda label=option["label"]: selectButton(label)
+                        )
+                elif action:
+                    if action == "SHOW_STORY_DATABASE":
+                        __enableStoryDbDialog()
+                    # st.code(jsonStr, language="json")
             except Exception as e:
                 pprint(e)
 
-        st.session_state.messages.append({
-            "role": "assistant",
-            "content": response,
-        })
-        st.session_state.chatHistory.append({
-            "role": "assistant",
-            "content": response,
-            "image": imagePath,
-        })

data/storiesDb.py

@@ -0,0 +1,14 @@
+import os
+from supabase import create_client, Client
+
+from dotenv import load_dotenv
+load_dotenv()
+
+url: str = os.environ.get("SUPABASE_URL")
+key: str = os.environ.get("SUPABASE_KEY")
+supabase: Client = create_client(url, key)
+
+
+def getAllStories():
+    response = supabase.table("existing_stories").select("*").execute()
+    return response.data

requirements.txt

@@ -1,4 +1,6 @@
 python-dotenv
 groq
 transformers
-gradio_client
+gradio_client
+anthropic
+supabase

tools/webScraper.py

@@ -0,0 +1,45 @@
+from urllib.parse import parse_qs, urlparse
+from bs4 import BeautifulSoup
+import requests
+
+
+def scrapeGoogleSearch(query):
+    result = []
+
+    searchUrl = f"https://www.google.com/search?q={query}"
+    response = requests.get(searchUrl)
+    if response.status_code == 200:
+        soup = BeautifulSoup(response.text, 'html.parser')
+        with open('soupDump.html', 'w', encoding='utf-8') as file:
+            file.write(soup.prettify())
+
+        results = soup.find('body')
+        mainDiv = soup.find('div', attrs={'id': 'main'})
+        answerDiv = (
+            mainDiv.select_one('div.PqksIc')
+            or mainDiv.select_one('div.BNeawe.iBp4i')
+        )
+        if answerDiv:
+            citationDateDiv = answerDiv.select_one('sub.gMUaMb.r0bn4c.rQMQod')
+            citationDate = citationDateDiv.text if citationDateDiv else ""
+            answerText = answerDiv.text.replace(citationDate, '').strip()
+            citationText = f"Citation Date: {citationDate}" if citationDate else ""
+            result.append(f"====\n{answerText}\n{citationText}\n====\n\n")
+
+        results = mainDiv.select('div.egMi0.kCrYT')
+        resultsDesc = mainDiv.select('div.BNeawe.s3v9rd.AP7Wnd .BNeawe.s3v9rd.AP7Wnd:last-child')
+
+        for (i, result) in enumerate(results[:10]):
+            title = result.find('h3').text
+            link = result.find('a')['href']
+            parsedUrl = urlparse(link)
+            urlParams = parse_qs(parsedUrl.query)
+            link = urlParams.get('q', [None])[0]
+            desc = resultsDesc[i].text
+            result.append(f"Title: {title}")
+            result.append(f"Description: {desc}")
+            result.append(f"Link: {link}\n")
+    else:
+        print("Failed to retrieve search results.")
+
+    return "".join(result)