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updated app.py
Browse files
app.py
CHANGED
@@ -1,7 +1,8 @@
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from fastapi import FastAPI, HTTPException
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from pydantic import BaseModel
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from huggingface_hub.file_download import http_get
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from llama_cpp import Llama
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from langchain_core.callbacks import CallbackManager, StreamingStdOutCallbackHandler
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# from langchain_core.prompts import ChatPromptTemplate
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@@ -19,7 +20,6 @@ class StoryRequest(BaseModel):
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mood: str
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story_type: str
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theme: str
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length: int
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num_scenes: int
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txt: str
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@@ -40,9 +40,19 @@ def load_model(
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os.chmod(final_model_path, 0o777)
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print("Files downloaded!")
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model = Llama(
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model_path=final_model_path,
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)
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print("Model loaded!")
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@@ -51,6 +61,7 @@ def load_model(
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llm = load_model()
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# Create a prompt template
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# system = """You are a helpful and creative assistant that specializes in generating engaging and imaginative stories for kids.
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# Based on the user's provided mood, preferred story type, theme, age, and desired story length of 500-600 words, create a unique and captivating story.
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@@ -58,71 +69,37 @@ llm = load_model()
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# to create another adventure soon!
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# """
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from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
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from langchain_core.messages import HumanMessage
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prompt = ChatPromptTemplate.from_messages(
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[
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(
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"system",
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"""You are a helpful and creative assistant that specializes in generating engaging and imaginative short storie for kids.
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Based on the user's provided mood, preferred story type, theme, age, and desired story length of 500-600 words, create a unique and captivating story.
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Always start with Story Title then generate a single story.Storie begin on Page 1(also mention the all pages headings in bold) and end on Page 7.
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Total pages in storie are seven each page have one short paragraph and dont ask for any feedback at the end just sign off with a cute closing inviting the reader
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to create another adventure soon!
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""",
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),
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MessagesPlaceholder(variable_name="messages"),
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]
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)
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# FastAPI endpoint to generate the story
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@app.post("/generate_story/")
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async def generate_story(story_request: StoryRequest):
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# chain = prompt | llm
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story = f"""here are the inputs from user:
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- **Mood:** {story_request.mood}
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- **Story Type:** {story_request.story_type}
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- **Theme:** {story_request.theme}
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- **Details Provided:** {story_request.txt}
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"""
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# response = chain.invoke({"messages": [HumanMessage(content=story)]})
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formatted_prompt = prompt.format(messages=[HumanMessage(content=story)])
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# formatted_text = formatted_prompt.to_string()
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chain = prompt | llm
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response = chain.invoke(formatted_prompt)
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# response = llm(formatted_prompt)
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# final_prompt = prompt_template.format(text=story)
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# Create the LLMChain
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# chain = LLMChain(llm=llm, prompt=prompt_template)
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# try:
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# response = chain.invoke(final_prompt)
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# return {"story": response}
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# except Exception as e:
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# raise HTTPException(status_code=500, detail=str(e))
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if not response:
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raise HTTPException(status_code=500, detail="Failed to generate the story")
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@@ -152,14 +129,4 @@ async def generate_story(story_request: StoryRequest):
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return {
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"story": response,
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"images": images
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}
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# image_prompt = (
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# f"Generate an image for Scene {i+1}. "
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# f"This image should represent the details described in paragraph {i+1} of the story. "
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# f"Mood: {mood}, Story Type: {', '.join(story_type)}, Theme: {theme}. "
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# f"Story: {response} "
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# f"Focus on the key elements in paragraph {i+1}."
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# )
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from fastapi import FastAPI, HTTPException
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from pydantic import BaseModel
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from langchain_community.llms import LlamaCpp
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from huggingface_hub.file_download import http_get
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# from llama_cpp import Llama
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from langchain_core.callbacks import CallbackManager, StreamingStdOutCallbackHandler
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# from langchain_core.prompts import ChatPromptTemplate
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mood: str
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story_type: str
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theme: str
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num_scenes: int
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txt: str
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os.chmod(final_model_path, 0o777)
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print("Files downloaded!")
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# model = Llama(
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# model_path=final_model_path,
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# n_ctx=1024
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# )
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model = LlamaCpp(
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model_path=final_model_path,
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temperature=0.3,
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max_tokens=2000,
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top_p=1,
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n_ctx=1024,
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callback_manager=callback_manager,
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verbose=True,
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)
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print("Model loaded!")
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llm = load_model()
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# Create a prompt template
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# system = """You are a helpful and creative assistant that specializes in generating engaging and imaginative stories for kids.
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# Based on the user's provided mood, preferred story type, theme, age, and desired story length of 500-600 words, create a unique and captivating story.
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# to create another adventure soon!
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# """
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system = """You are a helpful and creative assistant that specializes in generating engaging and imaginative short storie for kids.
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Based on the user's provided mood, preferred story type, theme, age, and desired story length of 500-600 words, create a unique and captivating story.
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Always start with Story Title then generate a single story.Storie begin on Page 1(also mention the all pages headings in bold) and end on Page 7.
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Total pages in storie are seven each page have one short paragraph and dont ask for any feedback at the end just sign off with a cute closing inviting the reader
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to create another adventure soon!
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"""
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prompt_template = ChatPromptTemplate.from_messages([("system", system), ("human", "{text}")])
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# FastAPI endpoint to generate the story
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@app.post("/generate_story/")
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async def generate_story(story_request: StoryRequest):
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story = f"""here are the inputs from user:
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- **Mood:** {story_request.mood}
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- **Story Type:** {story_request.story_type}
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- **Theme:** {story_request.theme}
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- **Details Provided:** {story_request.txt}
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"""
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final_prompt = prompt_template.format(text=story)
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# Create the LLMChain
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# chain = LLMChain(llm=llm, prompt=prompt_template)
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chain = llm | prompt_template
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# try:
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# response = chain.invoke(final_prompt)
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# return {"story": response}
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# except Exception as e:
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# raise HTTPException(status_code=500, detail=str(e))
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response = chain.invoke(final_prompt)
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if not response:
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raise HTTPException(status_code=500, detail="Failed to generate the story")
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return {
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"story": response,
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"images": images
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}
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