Update app.py

app.py

@@ -23,25 +23,24 @@ def img2txt(url):
     return text
 
 # Text-to-story
-def txt2story(img_text, top_k, top_p, temperature):
-
-    headers = {"Authorization": f"Bearer {api_token}"}
-
-    data = {
-        "model": "togethercomputer/llama-2-70b-chat",
-        "messages": [
-            {"role": "system", "content": '''As an experienced short story writer, write story title and then create a meaningful story influenced by provided words. 
-        Ensure stories conclude positively within 100 words. Remember the story must end within 100 words''', "temperature": temperature},
-            {"role": "user", "content": f"Here is input set of words: {img_text}", "temperature": temperature} 
-        ],
-        "top_k": top_k,
-        "top_p": top_p,
-        "temperature": temperature
-    }
-    
-    response = requests.post("https://api.together.xyz/inference", headers=headers, json=data)
-    
-    story = response.json()["output"]["choices"][0]["text"] 
+
+model = "tiiuae/falcon-7b-instruct"
+llm = HuggingFaceHub(
+    huggingfacehub_api_token = api_token,
+    repo_id = model,
+    verbose = False,
+    model_kwargs = {"temperature":temperature, "max_new_tokens": 1500})
+
+def generate_story(scenario, llm):
+    template= """You are a story teller.
+                   You get a scenario as an input text, and generates a short story out of it.
+                   Context: {scenario}
+                   Story:
+                   """
+    prompt = PromptTemplate(template=template, input_variables=["scenario"])
+      #Let's create our LLM chain now
+    chain = LLMChain(prompt=prompt, llm=llm)
+    story = chain.predict(scenario=scenario)
     return story
 
 
@@ -56,6 +55,7 @@ def txt2speech(text):
     
     with open('audio_story.mp3', 'wb') as file:
         file.write(response.content)
+        
 
         
 # Streamlit web app main function
@@ -83,7 +83,7 @@ def main():
         # Initiates AI processing and story generation
         with st.spinner("## 🤖 AI is at Work! "):
             scenario = img2txt("uploaded_image.jpg")  # Extracts text from the image
-            story = txt2story(scenario, top_k, top_p, temperature)  # Generates a story based on the image text, LLM params
+            story = txt2story(scenario, llm)  # Generates a story based on the image text, LLM params
             txt2speech(story)  # Converts the story to audio
 
             st.markdown("---")