Update app.py

app.py

@@ -35,13 +35,10 @@ def audio_to_text(audio_file_path):
         with sr.AudioFile(audio_file_path) as source:
             audio = recognizer.record(source)
         text = recognizer.recognize_google(audio)
-        print(f"Extracted Text: {text}")  # Debugging line
         return text
     except sr.UnknownValueError:
-        print("Audio could not be understood")  # Debugging line
         return None
     except sr.RequestError:
-        print("Request error")  # Debugging line
         return None
 
 # Function to convert audio to WAV format
@@ -53,8 +50,7 @@ def convert_to_wav(audio_file_path):
         wav_path = "temp_audio.wav"
         audio.export(wav_path, format="wav")
         return wav_path
-    except Exception as e:
-        print(f"Error converting audio to WAV: {e}")
+    except Exception:
         return None
 
 # Function to extract text from a PDF file
@@ -65,8 +61,8 @@ def extract_text_from_pdf(pdf_file):
         for page_num in range(len(pdf_document)):
             page = pdf_document.load_page(page_num)
             text += page.get_text()
-    except Exception as e:
-        print(f"Error extracting text from PDF: {e}")
+    except Exception:
+        pass
     return text
 
 # Function to embed text using a transformer model
@@ -76,8 +72,7 @@ def embed_text(texts, model, tokenizer):
         with torch.no_grad():
             embeddings = model(**inputs).last_hidden_state.mean(dim=1).numpy()
         return embeddings
-    except Exception as e:
-        print(f"Error embedding text: {e}")
+    except Exception:
         return np.array([])  # Return empty array on error
 
 # Function to convert text to speech
@@ -86,8 +81,7 @@ def text_to_speech(text, output_file):
         tts = gTTS(text=text, lang='en')
         tts.save(output_file)
         return output_file
-    except Exception as e:
-        print(f"Error converting text to speech: {e}")
+    except Exception:
         return None
 
 # Read all PDF files from the specified folder
@@ -98,25 +92,17 @@ for path in pdf_paths:
     pdf_text = extract_text_from_pdf(path)
     if pdf_text:
         texts.append(pdf_text)
-    else:
-        print(f"Failed to extract text from {path}")
 
 # Embed PDF texts and add to vector database
 embeddings = embed_text(texts, model, tokenizer)
 if embeddings.size > 0:
     index.add(embeddings)
-else:
-    print("No embeddings to add to the vector database")
 
 def process_audio(audio_file):
     if audio_file is None:
-        return "No audio file provided", None  # Handle case where no file is uploaded
-
-    if isinstance(audio_file, str):
-        audio_file_path = audio_file
-    else:
-        audio_file_path = audio_file.name
+        return "No audio file provided", None
 
+    audio_file_path = audio_file if isinstance(audio_file, str) else audio_file.name
     wav_path = convert_to_wav(audio_file_path)
     if wav_path is None:
         return "Error converting audio file to WAV format", None
@@ -138,15 +124,12 @@ def process_audio(audio_file):
 
         if not combined_text.strip():
             return "No relevant information found in the PDFs", None
+        
         prompt = (
-                    f"The user has asked a query related to agricultural practices: {text}. "
-                    f"Here are relevant excerpts from the Better Crops South Asia document: {combined_text}. "
-                    "Based on this information, please provide accurate advice related to sustainable crop management, pest control, irrigation practices, and any recommendations for improving crop yield in the South Asian region."
-                )
-
-
-
-        print(f"Prompt: {prompt}")  # Debugging line
+            f"The user has asked a query related to agricultural practices: {text}. "
+            f"Here are relevant excerpts from the Better Crops South Asia document: {combined_text}. "
+            "Based on this information, please provide accurate advice related to sustainable crop management, pest control, irrigation practices, and any recommendations for improving crop yield in the South Asian region."
+        )
 
         chat_completion = client.chat.completions.create(
             messages=[
@@ -165,30 +148,51 @@ def process_audio(audio_file):
             return "Error generating speech output", None
 
         return response, output_path
-    except Exception as e:
-        print(f"Error in process_audio: {e}")
+    except Exception:
         return "An error occurred while processing the audio", None
 
+# Enhanced Gradio interface customization
 iface = gr.Interface(
     fn=process_audio,
     inputs=gr.Audio(type="filepath"),
-    outputs=[gr.Textbox(label="Advice"), gr.Audio(label="Advice Audio")],
-    title="BetterCrops",
-    description="Helping Farmers with their Crops",
-    theme="default",
+    outputs=[gr.Textbox(label="Advice", lines=10), gr.Audio(label="Advice Audio")],
+    title="🌾 BetterCrops: Agriculture Support for Farmers",
+    description=(
+        "💡 **BetterCrops** is designed to assist farmers with their crops by analyzing agricultural PDFs "
+        "and generating personalized audio advice based on your voice queries."
+    ),
     article=(
-        "<div style='text-align: center; color: #001f43;'>"
-        "<h1>BetterCrops</h1>"
-        "<h3 style='font-size: 24px;'>Helping Farmers with their Crops</h3>"
+        "<div style='text-align: center; color: #003f6e;'>"
+        "<h1 style='font-size: 36px; font-weight: bold;'>BetterCrops</h1>"
+        "<h3 style='font-size: 24px; font-weight: normal;'>Empowering Farmers with AI-driven Insights</h3>"
         "</div>"
     ),
+    theme="grass",
     css=(
-        "<style>"
-        "body { background-color: #BAE5F4; color: #80826; }"
-        "h1, h3 { color: #001f43; }"
-        "</style>"
+        """
+        body {
+            background-color: #f0f5e9;
+            color: #2f4f2f;
+            font-family: 'Helvetica Neue', sans-serif;
+        }
+        h1, h3 {
+            color: #003f6e;
+        }
+        .gradio-container {
+            padding: 20px;
+            background: linear-gradient(135deg, #a3cfba 0%, #e8f5e9 100%);
+            border-radius: 15px;
+        }
+        .gradio-inputs, .gradio-outputs {
+            margin: 20px;
+            padding: 20px;
+            background-color: #ffffff;
+            border-radius: 10px;
+            box-shadow: 0px 4px 8px rgba(0, 0, 0, 0.1);
+        }
+        """
     )
 )
 
 if __name__ == "__main__":
-    iface.launch()
+    iface.launch()